Descriptor: "Disruption Prediction" - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Disruption Prediction"' showing total 88 results

Start Over Descriptor "Disruption Prediction"

88 results on '"Disruption Prediction"'

1. Design of Real-Time Data Acquisition System for Tokamak Disruption Prediction

Author: Peilong ZHANG, Weijie YE, Wei ZHENG, Yonghua DING, Liye WANG, and Yulin YANG
Subjects: tokamak, disruption prediction, data acquisition, udp, real-time transmission, Energy industries. Energy policy. Fuel trade, HD9502-9502.5
Abstract: [Introduction] Plasma disruption poses a significant threat to the tokamak nuclear device during its running and can cause damage to the device. Such damage can be reduced by adopting the disruption mitigation system, which has an action time highly dependent on the real-time running plasma disruption prediction system for predicting the plasma disruption moment. The deep-learning-based neural network has been used to train plasma disruption prediction models, and the real-time running of the deep-learning-based disruption prediction models requires a huge amount of real-time data from multiple diagnostics. [Method] The article proposed a design scheme for a real-time data acquisition system. The real-time data acquisition and transmission system was designed based on the modular structure and divided into the multiple channels acquisition module, ADC converting control and data reading module, data grouping and packing module and data transmission network module. The data transmission network module was developed on the hardware UDP network stack running on the FPGA at a speed of 10 G. This hardware UDP network stack featured a deterministic data transmission process, enabling a very low transmission latency of the system. [Result] The real-time data acquisition system has a sampling rate reaching 2 MSa/s, a data throughput rate exceeding 9.3 Gb/s, and a data transmission latency of less than 10 μs. [Conclusion] This data acquisition system facilitates the fast transmission of diagnostic data streams to disruption prediction models. The high sampling rates enable the system to perform real-time transmission of one-dimensional diagnostics such as radiation and electron temperature, improving the temporal resolution of data. The high data throughput rate can increase the transmission volume of diagnostic data, and the low data transmission latency can reduce the time required for disruption prediction models to obtain diagnostics data.
Published: 2024
Full Text: View/download PDF

2. Tokamak plasma disruption precursor onset time study based on semi-supervised anomaly detection

Author: X.K. Ai, W. Zheng, M. Zhang, D.L. Chen, C.S. Shen, B.H. Guo, B.J. Xiao, Y. Zhong, N.C. Wang, Z.J. Yang, Z.P. Chen, Z.Y. Chen, Y.H. Ding, and Y. Pan
Subjects: Tokamak, Disruption prediction, Machine learning, Anomaly detection, Precursor onset time, Nuclear engineering. Atomic power, TK9001-9401
Abstract: Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.
Published: 2024
Full Text: View/download PDF

3. Adaptive anomaly detection disruption prediction starting from first discharge on tokamak

Author: X.K. Ai, W. Zheng, M. Zhang, Y.H. Ding, D.L. Chen, Z.Y. Chen, B.H. Guo, C.S. Shen, N.C. Wang, Z.J. Yang, Z.P. Chen, Y. Pan, B. Shen, B.J. Xiao, and J-TEXT team
Subjects: tokamak, disruption prediction, deep learning, adaptive learning, anomaly detection, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Plasma disruption presents a significant challenge in tokamak fusion, especially in large-size devices like ITER, where it causes severe damage. While current data-driven machine learning methods perform well in disruption prediction, they require extensive discharge data for model training. However, future tokamaks will begin operations without any prior data, making it difficult to train data-driven disruption predictors and select appropriate hyperparameters during the early operation period. In this period disruption prediction also aims to support safe exploration of operation range and accumulate necessary data to develop advanced prediction models. Thus, predictors must adapt to evolving plasma states during this exploration phase. To address these challenges, this study further develops the enhanced convolutional autoencoder anomaly detection (E-CAAD) predictor and proposes a cross-tokamak adaptive transfer method based on E-CAAD. By training the E-CAAD model on data from existing devices, the predictor can effectively distinguish between disruption precursor and non-disruption samples in new device, enabling disruption prediction from the first shot on the new device. Additionally, adaptive learning from scratch and alarm threshold adaptive adjustment strategies are proposed to enable model automatically adapt to changes in the discharge scenario. The adaptive learning strategy enables the predictor to fully use scarce data during the early operation of the new device while rapidly adapting to changes in the discharge scenario. The threshold adaptive adjustment strategy addresses the challenge of selecting alarm thresholds on new devices where the validation set is lacking, ensuring that the alarm thresholds adapt to changes in the discharge scenario. Finally, the experiment transferring the model from J-TEXT to EAST exhibit that this method enables disruption prediction from the first shot on EAST, allowing the predictor to adapt to changes in the discharge scenario and maintain high prediction performance.
Published: 2025
Full Text: View/download PDF

4. Implementing deep learning-based disruption prediction in a drifting data environment of new tokamak: HL-3

Author: Zongyu Yang, Wulyu Zhong, Fan Xia, Zhe Gao, Xiaobo Zhu, Jiyuan Li, Liwen Hu, Zhaohe Xu, Da Li, Guohui Zheng, Yihang Chen, Junzhao Zhang, Bo Li, Xiaolong Zhang, Yiren Zhu, Ruihai Tong, Yunbo Dong, Yipo Zhang, Boda Yuan, Xin Yu, Zongyuhui He, Wenjing Tian, Xinwen Gong, and Min Xu
Subjects: disruption prediction, deep learning, HL-3, tokamak, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: A deep learning-based disruption prediction algorithm has been implemented on a new tokamak, HL-3. An Area Under receiver-operator characteristic Curve of 0.940 has been realized offline over a test campaign involving 72 disruptive and 240 non-disruptive shots, despite the limited training data available from the initial two campaigns. In addition to the well-documented challenge of insufficient training data, a previously unanticipated issue is addressed that the data distribution of a new device is continuously drifting. The plasma scans across a broad parameter space, bringing a drifting distribution of disruption causes and diagnostic data. This problem is often overlooked in previous implementations on steadily operating tokamaks, necessitating greater attention in future tokamaks like ITER. To address these challenges, innovative modules including predict-first neural network, data augmentation, and pseudo data placeholders are developed and implemented, which promotes the accuracy by up to 20%. A series of advantages are also brought by the modules, including the robustness in handling missing input channels, and the interpretability to identify which parameter of plasma is under abnormal condition. The results demonstrate that, with dedicated data collection and algorithm implementation, the issues of limited data and drifting distribution can be overcome, and further, the deep learning-based algorithm can reliably provide disruption alarms on a new tokamak.
Published: 2025
Full Text: View/download PDF

5. Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis.

Author: Keith, Zander, Nagpal, Chirag, Rea, Cristina, and Tinguely, R. Alex
Abstract: Survival regression models can achieve longer warning times at similar receiver operating characteristic performance than previously investigated models. Survival regression models are also shown to predict the time until a disruption will occur with lower error than other predictors. Time-to-event predictions from time-series data can be obtained with a survival analysis statistical framework, and there have been many tools developed for this task which we aim to apply to disruption prediction. Using the open-source Auton-Survival package we have implemented disruption predictors with the survival regression models Cox Proportional Hazards, Deep Cox Proportional Hazards, and Deep Survival Machines. To compare with previous work, we also include predictors using a Random Forest binary classifier, and a conditional Kaplan-Meier formalism. We benchmarked the performance of these five predictors using experimental data from the Alcator C-Mod and DIII-D tokamaks by simulating alarms on each individual shot. We find that developing machine-relevant metrics to evaluate models is an important area for future work. While this study finds cases where disruptive conditions are not predicted, there are instances where the desired outcome is produced. Giving the plasma control system the expected time-to-disruption will allow it to determine the optimal actuator response in real time to minimize risk of damage to the device. [ABSTRACT FROM AUTHOR]
Published: 2024
Full Text: View/download PDF

6. 面向托卡马克破裂预测的实时数据采集系统设计.

Author: 张沛龙, 叶伟杰, 郑玮, 丁永华, 王俪晔, and 杨玉林
Abstract: Copyright of Southern Energy Construction is the property of Southern Energy Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Published: 2024
Full Text: View/download PDF

7. Disruption prediction on EAST with different wall conditions based on a multi-scale deep hybrid neural network.

Author: Guo, B.H., Chen, D.L., Rea, C., Wu, M.Q., Shen, B., Granetz, R.S., Zhang, Z.C., Huang, Y., Duan, Y.M., Zeng, L., and Xiao, B.J.
Subjects: *DEEP learning, *FUSION reactors, *CONVOLUTIONAL neural networks, *MACHINE learning, *PLASMA instabilities, *DATABASES
Abstract: Plasma disruption is a very dangerous event for future tokamaks and fusion reactors. Therefore, predicting disruption is crucial for ensuring the safety and performance of reactors. In this study, the features of two deep learning algorithms are integrated to establish a multi-scale hybrid network disruption predictor. Firstly, 43 diagnostic signals are extracted by a convolutional neural network (CNN), and the time information is learned by a long short-term memory network. The predictor is trained and tested on a database containing ∼ 10 4 non-all-metal wall discharges. Its area under the receiver operator characteristic curve (AUC), which is a common performance metric for deep learning algorithms, reaches 0.97, and the true positive rate is ∼ 95.3 % , while the false positive rate is ∼ 8 %. Since EAST was upgraded in 2020, the wall condition has been upgraded from non-all-metal to all-metal. To examine the robustness of the predictor, the EAST disruption predictor is migrated to the all-metal wall experiment for the first time. It is again tested under the all-metal wall experimental data, and its warning performance decreases significantly, with an AUC of only 0.79. To improve the robustness and sensitivity of the predictor against disruptions, the convolutional attention mechanism is introduced into the CNN. After training and testing with the same data set, the warning performance for the all-metal wall data is improved, with the AUC value increasing to 0.84. To further improve the robustness of the predictor, the t-distributed stochastic neighbor embedding algorithm is employed to explore the difference of the sample distribution before and after EAST upgradation, and the transfer learning algorithm is used to reduce this difference. By applying transfer learning to a small amount of discharges from the all-metal wall data, the warning performance in the all-metal wall experiment is further improved, and the AUC value increases to 0.93. Overall, the proposed data-driven predictor is promising for applications in future devices such as ITER. [ABSTRACT FROM AUTHOR]
Published: 2023
Full Text: View/download PDF

8. Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time

Author: J. Vega, S. Dormido-Canto, R. Castro, J.D. Fernández, A. Murari, and JET Contributors
Subjects: disruption prediction, privileged information, JT-60SA, SVMplus, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: This article describes the use of privileged information to train supervised classifiers, applied for the first time to the prediction of disruptions in tokamaks. The objective consists of making predictions with real-time signals during the discharges (as usual) but after training the predictor also with any kind of data at training time that is not available during discharge execution. The latter kind of data is known as privileged information. Taking into account the limited number of foreseen real time signals for disruption prediction at the beginning of operation in JT-60SA, a predictor with a line integrated density signal and the mode lock signal as privileged information has been developed and tested with 1437 JET discharges. The success rate with positive warning time has been improved from 45.24% to 90.48% and the tardy detection rate has diminished from 50% to 8.33%. The use of privileged information in an adaptive way also provides a remarkable reduction of false alarms from 11.53% to 1.15%. The potential of the methodology, exemplified with data relevant to the beginning of JT-60SA operation, is absolutely general and can be applied to any combination of diagnostic signals.
Published: 2024
Full Text: View/download PDF

9. Cross-tokamak disruption prediction based on domain adaptation

Author: Chengshuo Shen, Wei Zheng, Bihao Guo, Yonghua Ding, Dalong Chen, Xinkun Ai, Fengming Xue, Yu Zhong, Nengchao Wang, Biao Shen, Binjia Xiao, Zhongyong Chen, Yuan Pan, and J-TEXT Team
Subjects: disruption prediction, cross tokamak, domain adaptation, machine learning, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated a novel approach to predict disruption in a future tokamak using only a few discharges based on domain adaptation (DA). The approach aims to predict disruption by finding a feature space that is universal to all tokamaks. The first step is to use the existing understanding of physics to extract physics-guided features from the diagnostic signals of each tokamak, called physics-guided feature extraction (PGFE). The second step is to align a few data from the future tokamak (target domain) and a large amount of data from existing tokamaks (source domain) based on a DA algorithm called CORrelation ALignment (CORAL). It is the first attempt at applying DA in the cross-tokamak disruption prediction task. PGFE has been successfully applied in J-TEXT to predict disruption with excellent performance. PGFE can also reduce the data volume requirements due to extracting the less device-specific features, thereby establishing a solid foundation for cross-tokamak disruption prediction. We have further improved CORAL called supervised CORAL (S-CORAL) to enhance its appropriateness in feature alignment for the disruption prediction task. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak, which has a large gap in the ranges of plasma parameters. The utilization of the S-CORAL improves the disruption prediction performance on future tokamak. Through interpretable analysis, we discovered that the learned knowledge of the disruption prediction model through this approach exhibits more similarities to the model trained on large data volumes of future tokamak. This approach provides a light, interpretable and few data-required ways by aligning features to predict disruption using small data volume from the future tokamak.
Published: 2024
Full Text: View/download PDF

10. High-beta disruption prediction study on HL-2A with instance-based transfer learning

Author: Y. Zhong, W. Zheng, Z.Y. Chen, W. Yan, F. Xia, L.M. Yu, F.M. Xue, C.S. Shen, X.K. Ai, Z.Y. Yang, Y.L. Yu, Z.S. Nie, Y.H. Ding, Y.F. Liang, Z.P. Chen, and J-TEXT Team
Subjects: disruption prediction, machine learning, transfer learning, LightGBM, SHAP, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Unmitigated disruptions pose a much more serious threat when large-scale tokamaks are operating in the high performance regime. Machine learning based disruption predictors can exhibit impressive performance. However, their effectiveness is based on a substantial amount of training data. In future reactors, obtaining a substantial amount of disruption data in high performance regimes without risking damage to the machine is highly improbable. Using machine learning to develop disruption predictors on data from the low performance regime and transfer them to the high performance regime is an effective solution for a large reactor-sized tokamak like ITER and beyond. In this study, a number of models are trained using different subsets of data from the HL-2A tokamak experiment. A SHapley Additive exPlanations (SHAP) analysis is executed on the models, revealing that there are different, even contradicting, patterns between different performance regimes. Thus, simply mixing data among different performance regimes will not yield optimal results. Based on this analysis, we propose an instance-based transfer learning technique which trains the model using a dataset generated with an optimized strategy. The strategy involves instance and feature selection based on the physics behind differences in high- and low-performance discharges, as revealed by SHAP model analysis. The TrAdaBoost technique significantly improved the model performance from 0.78 BA (balanced accuracy) to 0.86 BA with a few high-performance operation data.
Published: 2024
Full Text: View/download PDF

11. Performance Comparison of Machine Learning Disruption Predictors at JET.

Author: Aymerich, Enrico, Cannas, Barbara, Pisano, Fabio, Sias, Giuliana, Sozzi, Carlo, Stuart, Chris, Carvalho, Pedro, and Fanni, Alessandra
Subjects: MACHINE learning, FUSION reactors, MACHINE performance, CONVOLUTIONAL neural networks, TOPOGRAPHIC maps, TOKAMAKS
Abstract: Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant (DEMO) and China Fusion Engineering Test Reactor (CFETR). In the last two decades, a great number of DP systems have been developed using data-driven methods. The performance of the DP models has been improved over the years both for a more appropriate choice of diagnostics and input features and for the availability of increasingly powerful data-driven modelling techniques. However, a direct comparison among the proposals has not yet been conducted. Such a comparison is mandatory, at least for the same device, to learn lessons from all these efforts and finally choose the best set of diagnostic signals and the best modelling approach. A first effort towards this goal is made in this paper, where different DP models will be compared using the same performance indices and the same device. In particular, the performance of a conventional Multilayer Perceptron Neural Network (MLP-NN) model is compared with those of two more sophisticated models, based on Generative Topographic Mapping (GTM) and Convolutional Neural Networks (CNN), on the same real time diagnostic signals from several experiments at the JET tokamak. The most common performance indices have been used to compare the different DP models and the results are deeply discussed. The comparison confirms the soundness of all the investigated machine learning approaches and the chosen diagnostics, enables us to highlight the pros and cons of each model, and helps to consciously choose the approach that best matches with the plasma protection needs. [ABSTRACT FROM AUTHOR]
Published: 2023
Full Text: View/download PDF

12. Data-Driven-Based Disruption Prediction in GOLEM Tokamak with Missing Values

Author: Chandrasekaran, Jayakumar, Madhawa, Surendar, Sangeetha, J., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Paprzycki, Marcin, editor, Thampi, Sabu M., editor, Mitra, Sushmita, editor, Trajkovic, Ljiljana, editor, and El-Alfy, El-Sayed M., editor
Published: 2021
Full Text: View/download PDF

13. Development of robust indicators for the identification of electron temperature profile anomalies and application to JET.

Author: Rossi, Riccardo, Gelfusa, Michela, Flanagan, Joanne, and Murari, Andrea
Subjects: *ELECTRON temperature, *ELECTRON detection, *FALSE alarms, *DETECTION alarms, *COOLING
Abstract: Recent experience with metallic devices operating in ITER relevant regions of the operational space, has shown that the disruptivity of these plasmas is unacceptably high. The main causes of the disruptions are linked to impurity accumulation in the core and edge cooling, resulting in unstable current profiles. Avoidance and prevention of the consequent instabilities require the early detection of anomalous electron temperature profiles. A series of indicators have been developed and their performances compared, to find the most suitable inputs for disruption predictors. Their properties are assessed on the basis of information content, reliability and real-time availability. The best performing ones provide much better results than the ones reported in the literature, as shown by both numerical tests with synthetic data and the analysis of experimental signals from JET with the ITER-like wall. They provide better accuracy, lower false alarms and earlier detection. The improved discriminatory capability of the developed indicators is expected to significantly improve the performance of the most advanced predictors recently reported in the literature. [ABSTRACT FROM AUTHOR]
Published: 2022
Full Text: View/download PDF

14. Performance Comparison of Machine Learning Disruption Predictors at JET

Author: Enrico Aymerich, Barbara Cannas, Fabio Pisano, Giuliana Sias, Carlo Sozzi, Chris Stuart, Pedro Carvalho, Alessandra Fanni, and the JET Contributors
Subjects: tokamak, disruption prediction, machine learning, deep learning, data analysis, plasma diagnostics, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract: Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant (DEMO) and China Fusion Engineering Test Reactor (CFETR). In the last two decades, a great number of DP systems have been developed using data-driven methods. The performance of the DP models has been improved over the years both for a more appropriate choice of diagnostics and input features and for the availability of increasingly powerful data-driven modelling techniques. However, a direct comparison among the proposals has not yet been conducted. Such a comparison is mandatory, at least for the same device, to learn lessons from all these efforts and finally choose the best set of diagnostic signals and the best modelling approach. A first effort towards this goal is made in this paper, where different DP models will be compared using the same performance indices and the same device. In particular, the performance of a conventional Multilayer Perceptron Neural Network (MLP-NN) model is compared with those of two more sophisticated models, based on Generative Topographic Mapping (GTM) and Convolutional Neural Networks (CNN), on the same real time diagnostic signals from several experiments at the JET tokamak. The most common performance indices have been used to compare the different DP models and the results are deeply discussed. The comparison confirms the soundness of all the investigated machine learning approaches and the chosen diagnostics, enables us to highlight the pros and cons of each model, and helps to consciously choose the approach that best matches with the plasma protection needs.
Published: 2023
Full Text: View/download PDF

15. Integrated deep learning framework for unstable event identification and disruption prediction of tokamak plasmas

Author: J.X. Zhu, C. Rea, R.S. Granetz, E.S. Marmar, R. Sweeney, K. Montes, and R.A. Tinguely
Subjects: tokamak, disruption, disruption prediction, disruption avoidance, machine learning, plasma instabilities, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: The ability to identify underlying disruption precursors is key to disruption avoidance. In this paper, we present an integrated deep learning (DL) based model that combines disruption prediction with the identification of several disruption precursors like rotating modes, locked modes, H-to-L back transitions and radiative collapses. The first part of our study demonstrates that the DL-based unstable event identifier trained on 160 manually labeled DIII-D shots can achieve, on average, 84% event identification rate of various frequent unstable events (like H-L back transition, locked mode, radiative collapse, rotating MHD mode, large sawtooth crash), and the trained identifier can be adapted to label unseen discharges, thus expanding the original manually labeled database. Based on these results, the integrated DL-based framework is developed using a combined database of manually labeled and automatically labeled DIII-D data, and it shows state-of-the-art (AUC = 0.940) disruption prediction and event identification abilities on DIII-D. Through cross-machine numerical disruption prediction studies using this new integrated model and leveraging the C-Mod, DIII-D, and EAST disruption warning databases, we demonstrate the improved cross-machine disruption prediction ability and extended warning time of the new model compared with a baseline predictor. In addition, the trained integrated model shows qualitatively good cross-machine event identification ability. Given a labeled dataset, the strategy presented in this paper, i.e. one that combines a disruption predictor with an event identifier module, can be applied to upgrade any neural network based disruption predictor. The results presented here inform possible development strategies of machine learning based disruption avoidance algorithms for future tokamaks and highlight the importance of building comprehensive databases with unstable event information on current machines.
Published: 2023
Full Text: View/download PDF

16. IDP-PGFE: an interpretable disruption predictor based on physics-guided feature extraction

Author: C. Shen, W. Zheng, Y. Ding, X. Ai, F. Xue, Y. Zhong, N. Wang, L. Gao, Z. Chen, Z. Yang, Y. Pan, and J-TEXT team
Subjects: interpretability, disruption prediction, machine learning, feature extraction, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. If a disruption prediction model can be interpreted, it can tell why certain samples are classified as disruption precursors. This allows us to tell the types of incoming disruption for disruption avoidance and gives us insight into the mechanism of disruption. This paper presents a disruption predictor called interpretable disruption predictor based on physics-guided feature extraction (IDP-PGFE) and its results on J-TEXT experiment data. The prediction performance of IDP-PGFE with physics-guided features is effectively improved (true positive rate = 97.27%, false positive rate = 5.45%, area under the ROC curve = 0.98) compared to the models with raw signal input. The validity of the interpretation results is ensured by the high performance of the model. The interpretability study using an attribution technique provides an understanding of J-TEXT disruption and conforms to our prior comprehension of disruption. Furthermore, IDP-PGFE gives a possible mean on inferring the underlying cause of the disruption and how interventions affect the disruption process in J-TEXT. The interpretation results and the experimental phenomenon have a high degree of conformity. The interpretation results also gives a possible experimental analysis direction that the resonant magnetic perturbations delays the density limit disruption by affecting both the MHD instabilities and the radiation profile. PGFE could also reduce the data requirement of IDP-PGFE to 10% of the training data required to train a model on raw signals. This made it possible to be transferred to the next-generation tokamaks, which cannot provide large amounts of data. Therefore, IDP-PGFE is an effective approach to exploring disruption mechanisms and transferring disruption prediction models to future tokamaks.
Published: 2023
Full Text: View/download PDF

17. Recent progress on the control and mitigation of runaway electrons and disruption prediction in the HL-2A and J-TEXT tokamaks

Author: Zhang, Y. P., Tong, R. H., Yang, Z. Y., Chen, Z. Y., Hu, D., Dong, Y. B., Zhang, J., Zhu, Y. X., Xu, H. B., and Zhong, W. L.
Published: 2023
Full Text: View/download PDF

18. Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks.

Author: Zhu, J., Rea, C., Granetz, R.S., Marmar, E.S., Montes, K.J., Sweeney, R., Tinguely, R.A., Chen, D.L., Shen, B., Xiao, B.J., Humphreys, D., Barr, J., and Meneghini, O.
Subjects: *TOKAMAKS, *NUMERICAL analysis, *PLASMA instabilities, *FORECASTING, *DATA analysis
Abstract: Next generation High Performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. Achieving reliable disruption prediction for a deviceâ€™s HP operation based on its Low Performance (LP) data is a key to its success. In this letter, through explorative data analysis and dedicated numerical experiments on multiple existing tokamaks, we demonstrate how the operational regimes of tokamaks can affect the power of a trained disruption predictor. First, our results suggest data-driven disruption predictors trained on abundant LP discharges work poorly on the HP regime of the same tokamak, which is a consequence of the distinct distributions of the tightly correlated signals related to disruptions in these two regimes. Second, we find that matching operational parameters among tokamaks strongly improves cross-machine accuracy which implies our model learns from the underlying scalings of dimensionless physics parameters like q 95, Î˛ p and confirms the importance of these parameters in disruption physics and cross machine domain matching from the data-driven perspective. Finally, our results show in the absence of HP data from the target devices, the best predictivity of the HP regime for the target machine can be achieved by combining LP data from the target with HP data from other machines. These results provide a possible disruption predictor development strategy for next generation tokamaks, such as ITER and SPARC, and highlight the importance of developing baseline scenario discharges of future tokamaks on existing machines to collect more relevant disruptive data. [ABSTRACT FROM AUTHOR]
Published: 2021
Full Text: View/download PDF

19. MHD spectrogram contribution to disruption prediction using Convolutional Neural Networks.

Author: Aymerich, E., Sias, G., Atzeni, S., Pisano, F., Cannas, B., and Fanni, A.
Subjects: *CONVOLUTIONAL neural networks, *ARTIFICIAL neural networks, *SPECTROGRAMS, *PLASMA instabilities, *MAGNETOHYDRODYNAMIC instabilities, *DEEP learning, *PLASMA diagnostics
Abstract: • A disruption prediction system for JET, based on a Convolutional Neural Network, is designed. • The contribution of the Mirnov coil measurement spectrogram to disruption prediction is investigated. • The spatiotemporal information from temperature, radiation, and density profiles and the MHD spectrogram are given as input to the predictor as images. • The model is tested on a dataset different from that used during the training phase. • The predictor performance of the proposed predictor is compared with the same predictor model and the same database trained without Mirnov measurement contribute. The present research focuses on investigating deep neural networks techniques for predicting plasma disruptions in tokamaks. For this purpose, various deep-learning predictive models and several plasma diagnostics will be analyzed, using data gathered during the experimental campaigns conducted at the JET nuclear fusion tokamak between 2011 and 2020. The primary objective of this study is to show that the contribution of the MHD spectrograms increases the disruption predictor performance. The final deep-learning prediction model leverages the capability of Convolutional Neural Networks to directly learn important spatiotemporal information from 1D plasma profiles of temperature, density and power radiation, obtained from High Resolution Thomson Scattering and Bolometer diagnostics, as well as from spectrograms generated by a set of fast magnetic pick-up coils known as Mirnov coils. The Convolutional Neural Network eliminates the need for manual feature extraction methods that characterize the majority of machine learning methods. Using plasma profiles information allows to distinguish between core radiation caused by impurity accumulations and outboard radiation phenomena. Likewise, the decision to incorporate spectrograms from Mirnov coils is based on the diagnostic's ability to measure magnetic fluctuations originating from MHD instabilities, which can lead to disruptions. In order to address phenomena characterized by fast temporal dynamics, the inclusion of the locked mode signal was chosen. This signal is commonly employed at JET to trigger mitigation actions. It is integrated into an alarm scheme that employs both AND/OR logic and optimized thresholds, ensuring its effectiveness. The proposed predictor exhibits significant performance, with only one missed alarm out of 92 disrupted discharges and three false alarms out of 131 regularly terminated discharges in the test set. [ABSTRACT FROM AUTHOR]
Published: 2024
Full Text: View/download PDF

20. Data-driven disruption prediction in GOLEM Tokamak using ensemble classifiers.

Author: Chandrasekar, Jayakumar, Madhawa, Surendar, Sangeetha, J., Varadarajan, Vijayakumar, Kommers, Piet, Piuri, Vincenzo, and Subramaniyaswamy, V.
Subjects: *FORECASTING, *SUPERVISED learning, *ALGORITHMS, *MACHINE learning, *TREE pruning
Abstract: A robust disruption prediction system is mandatory in a Tokamak control system as the disruption can cause malfunctioning of the plasma-facing components and impair irrecoverable structural damage to the vessel. To mitigate the disruption, in this article, a data-driven based disruption predictor is developed using an ensemble technique. The ensemble algorithm classifies disruptive and non-disruptive discharges in the GOLEM Tokamak system. Ensemble classifiers combine the predictive capacity of several weak learners to produce a single predictive model and are utilized both in supervised and unsupervised learning. The resulting final model reduces the bias, minimizes variance and is unlikely to over-fit when compared to the individual model from a single algorithm. In this paper, popular ensemble techniques such as Bagging, Boosting, Voting, and Stacking are employed on the time-series Tokamak dataset, which consists of 117 normal and 70 disruptive shots. Stacking ensemble with REPTree (Reduced Error Pruning Tree) as a base learner and Multi-response Linear Regression as meta learner produced better results in comparison to other ensembles. A comparison with the widely employed stand-alone machine learning algorithms and ensemble algorithms are illustrated. The results show the excellent performance of the Stacking model with an F1 score of 0.973. The developed predictive model would be capable of warning the human operator with feedback about the feature(s) causing the disruption. [ABSTRACT FROM AUTHOR]
Published: 2020
Full Text: View/download PDF

21. A multidimensional linear model for disruption prediction in JET.

Author: Rattá, G.A., Vega, J., and Murari, A.
Subjects: *LINEAR equations, *PREDICTION models, *MACHINE learning, *FALSE alarms, *GENETIC algorithms
Abstract: • Disruptions are a priority research subject in sights of ITER. • It is urgent to find solutions to avoid or at least mitigate their effects. • A fundamental pre-requisite to intervene is to predict their occurrence. • Machine learning techniques can aid to model accurate disruption predictors. • A linear equation derived from a machine learning disruption predictor has been derived. The implementation of Machine Learning techniques can help to develop accurate disruption predictors. However, they may provide outcomes difficult to understand from a physics point of view due to their mathematical formulation. In this work an interpretable linear equation has been derived from a data-driven model. This developed predictor can be used for real-time forecasting and for the off-line analysis of the variables that contribute to the alarm triggering. To create the linear model, in addition to physic quantities, Time Increments (TIs) that represent the differences in the amplitude of a signal divided by a predefined time interval have been considered. To select the best subset of quantities to include (from the wide range of possible combinations of signals and TIs), Genetic Algorithms have been applied. The results, obtained over an independent testing database of 131 unintentional disruptive and 1310 non-disruptive shots, are 99,24% of success rate (94,66% of them with at least 10 ms of warning time) and 3,51% of false alarms. [ABSTRACT FROM AUTHOR]
Published: 2019
Full Text: View/download PDF

22. Assessment of linear disruption predictors using JT-60U data.

Author: Vega, J., Hernández, F., Dormido-Canto, S., Isayama, A., Joffrin, E., Matsunaga, G., and Suzuki, T.
Subjects: *NUCLEAR fusion, *TOKAMAKS
Abstract: Disruptions are dangerous events in tokamaks that require mitigation methods to alleviate its detrimental effects. A prerequisite to trigger any mitigation action is the existence of a reliable disruption predictor. This article assesses a predictor that relates in a linear way consecutive samples of a single quantity (in particular, the magnetic perturbation time derivative signal has been used). With this kind of predictor, the recognition of disruptions does not depend on how large the signal amplitude is but on how large the signal increments are: small increments mean smooth plasma evolution whereas abrupt increments reflect a non-smooth evolution and potential risk of disruption. Results are presented with data from the JT-60U tokamak and high-beta discharges. Two training methods have been tested: a classical approach in which the more data for training the better and an adaptive method that starts from scratch. In both cases the success rate is about 95%. It should be noted that predictors based on signal increments and their adaptive versions can be of big interest for next devices such as JT-60SA or ITER. [ABSTRACT FROM AUTHOR]
Published: 2019
Full Text: View/download PDF

23. OpenCL Implementation of an Adaptive Disruption Predictor Based on a Probabilistic Venn Classifier.

Author: Carpeno, Antonio, Ruiz, Mariano, Gonzalez, Cesar, Vega, Jesus, Dormido-Canto, Sebastian, Esquembri, Sergio, and Bernal, Enrique
Subjects: *HETEROGENEOUS computing, *DATA acquisition systems, *ANALOG-to-digital converters, *GATE array circuits, *FIELD programmable gate arrays, *GRAPHICS processing units
Abstract: The ability and flexibility of the open computing language (OpenCL) for task parallelization in heterogeneous computing platforms [field-programmable gate array (FPGA), CPU, and GPU] are remarkable advantages when designing advanced data acquisition and processing systems. The use of FPGA devices in data acquisition devices increases the capabilities of traditional data acquisition systems, thus allowing the implementation of complex mathematical algorithms with the acquired data. This paper shows an OpenCL implementation of an adaptive probabilistic disruption predictor for fusion devices implemented in a Cyclone V SoC device with an ARM processor, the FPGA logic, and an analog to digital converter. This paper describes the methodology used, the hardware/software system architecture, and the implementation results. The work highlights the critical aspects involved in designing these OpenCL-based systems and, in particular, the implementation of the board support package. This paper also presents the aspects such as the significant differences in the design flow concept between FPGA and GPU OpenCL implementations and how to optimize the FPGA implementation with OpenCL. The results show that it is possible to make predictions with computation times shorter than $500~\mu \text{s}$ when using this low-cost SoC. [ABSTRACT FROM AUTHOR]
Published: 2019
Full Text: View/download PDF

24. Data-driven disruption prediction using random forest in KSTAR.

Author: Lee, Jeongwon, Kim, Jayhyun, Hahn, Sang-hee, Han, Hyunsun, Shin, Giwook, Kim, Woong-Chae, and Yoon, Si-Woo
Subjects: *RANDOM forest algorithms, *PLASMA instabilities, *PLASMA currents, *PLASMA confinement, *PLASMA physics, *REGRESSION trees, *MACHINE learning
Abstract: • Data driven disruption prediction using random forest algorithm is studied in KSTAR. • The model shows an overall accuracy of 94.7 % in both phases, and the F-1 score is 0.93. • The prediction model will be implemented in the KSTAR plasma control system to trigger a disruption mitigation system to reduce the harmful effects of plasma disruption. Here, we develop a data driven disruption prediction model using the random forest algorithm using a database of KSTAR disruption shots. A total of 719 disruption shots from the past 3 campaigns have been selected to generate a database, which are terminated during the plasma current flattop phase by plasma physics issues and have all the essential diagnostic data. To train and test our machine learning model, the input features known to be associated with plasma disruption were collected from various diagnostic and analysis data. The slice datasets were labeled as 'disruption' less than 40 ms from thermal quench, and the remainder as 'non-disruption'. The random forest-based disruption prediction model was trained and tested against the database. An out-of-bag (OBB) error indicates the optimal value of the 'number of trees' in the random forest model. A confusion matrix with test database shows an overall accuracy of 91 % in both phases, and an F-1 score of 0.915. This model shows the characteristics of disruptions that occur in KSTAR, and it can be helpful for disruption prediction studies of future superconducting tokamaks. [ABSTRACT FROM AUTHOR]
Published: 2024
Full Text: View/download PDF

25. Prediction of high-beta disruptions in JT-60U based on sparse modeling using exhaustive search.

Author: Yokoyama, T., Miyoshi, Y., Hiwatari, R., Isayama, A., Matsunaga, G., Oyama, N., Igarashi, Y., Okada, M., and Ogawa, Y.
Subjects: *PLASMA gases, *NUCLEAR fusion, *FLUCTUATIONS (Physics), *TOKAMAKS, *PARAMETER estimation
Abstract: Highlights • Disruption predictor based on high-beta plasma experimental data in JT-60U. • Input plasma parameters are selected by sparse modeling using exhaustive search. • Performance of prediction is improved by extracting input parameters. • Key parameters including magnetic fluctuation and its time delivered are extracted. • False alarm rate is improved considering temporal change of parameters. Abstract Disruption is a critical phenomenon in a tokamak reactor. Although disruption causes serious damage to the reactor, its physical mechanism remains unclear. To realize a tokamak reactor, it is necessary to understand and control the disruption phenomenon. The present research constructs a disruption predictor using experimental high-beta plasma data in the JT-60U tokamak. The predictor was constructed using a support vector machine as a linear discriminant, and we focus on a variable selection problem for the binary classification by sparse modeling, specifically, exhaustively searching the best combinations of variables which maximize the predictor performance. By the sparse modeling, we found that the six input parameters as the best combinations. The selected parameters were the n = 1 mode amplitude | B r n = 1 | and its time derivative d | B r n = 1 | / d t , the plasma density (relative to the Greenwald density limit) and its time derivative, and the time derivatives of the plasma internal inductance and plasma elongation. In particular, it was identified that the parameter d | B r n = 1 | / d t , plays a key role on plasma disruption. We should notice that the combination with other plasma parameters is indispensable and remarkably make it possible to improve the performance of disruption prediction. [ABSTRACT FROM AUTHOR]
Published: 2019
Full Text: View/download PDF

26. A locked mode indicator for disruption prediction on JET and ASDEX upgrade.

Author: Sias, G., Cannas, B., Fanni, A., Murari, A, and Pau, A.
Subjects: *SIGNAL processing, *ALGORITHMS, *TOKAMAKS, *SPONTANEOUS magnetization, *MATHEMATICAL optimization
Abstract: Abstract The aim of this paper is to present a signal processing algorithm that, applied to the raw Locked Mode signal, allows us to obtain a disruption indicator in principle exploitable on different tokamaks. A common definition of such an indicator for different machines would facilitate the development of portable systems for disruption prediction, which is becoming of increasingly importance for the next tokamak generations. Moreover, the indicator allows us to overcome some intrinsic problems in the diagnostic system such as drift and offset. The behavior of the proposed indicator as disruption predictor, based on crossing optimized thresholds of the signal amplitude, has been analyzed using data of both JET and ASDEX Upgrade experiments. A thorough analysis of the disruption prediction performance shows how the indicator is able to recover some missed and tardy detections of the raw signal. Moreover, it intervenes and corrects premature or even wrong alarms due to, e.g., drifts and/or offsets. [ABSTRACT FROM AUTHOR]
Published: 2019
Full Text: View/download PDF

27. A First Analysis of JET Plasma Profile-Based Indicators for Disruption Prediction and Avoidance.

Author: Pau, A., Fanni, A., Cannas, B., Carcangiu, S., Pisano, G., Sias, G., Sparapani, P., Baruzzo, M., Murari, A., Rimini, F., Tsalas, M., and De Vries, P. C.
Subjects: *PLASMA currents, *MAGNETOHYDRODYNAMICS, *PHENOMENOLOGY, *LIDAR
Abstract: Reliable algorithms for disruption avoidance and prediction are foreseen to play a fundamental role in the JET control system for the successful operation of the machine in the upcoming deuterium-tritium campaigns. The integration of such algorithms is expected to be a key part also in the implementation of the ITER plasma control system. So far, most of the effort has been devoted to the prediction of disruptions, which is required to mitigate the effects of these transient events, protecting the integrity of in-vessel components. Nevertheless, in order to put in place recover strategies or to have the possibility of a soft landing for the plasma current, the paradigm must be shifted to avoiding disruptions. In this paper, plasma profile-based indicators will be statistically analyzed showing their potential in such a perspective, where warning times and reliability of detection are crucial. [ABSTRACT FROM AUTHOR]
Published: 2018
Full Text: View/download PDF

28. Real-time implementation with FPGA-based DAQ system of a probabilistic disruption predictor from scratch.

Author: Vega, J., Ruiz, M., Barrera, E., Castro, R., Rattá, G.A., Dormido-Canto, S., Murari, A., and Bernal, E.
Subjects: *DATA acquisition systems, *PROBABILISTIC inference, *VENN diagrams, *STANDARD deviations, *PLASMA currents
Abstract: Real-time (RT) disruption prediction (DP) is essential to trigger mitigation actions that avoid irreversible damage to the devices. This paper deals with disruption mitigation alarms and performs the RT implementation of a probabilistic predictor. The RT implementation has been carried out with a fast controller with DAQ FPGA-based data acquisition devices corresponding to ITER catalogue (in particular, a reconfigurable Input/Output platform has been used). Up to three input signals have been used and relevant information for the prediction is extracted from the temporal and the frequency domains. The signals are read from the JET database. Then D/A conversions are carried out and used as inputs to the real time system. In this way, the whole process of digitization, data analysis and prediction is performed. The computation time for each prediction takes less than 200 μs. [ABSTRACT FROM AUTHOR]
Published: 2018
Full Text: View/download PDF

29. A cost-based criterion for implementing data-driven disruption predictors.

Author: Parsons, Matthew S.
Subjects: *PLASMA instabilities, *OPERATING costs, *TOKAMAKS
Abstract: Plasma disruptions are a serious impediment to tokamak operations, and accordingly there has been a lot of attention given to the topic of predicting disruptions in order to avoid or more effectively mitigate them. While existing prediction goals are based on a combination of engineering constraints and desired operating outcomes, it does not follow that a predictor must meet these strict requirements to offer a benefit. Rather, a cost-based criterion should provide the necessary justification to implement a disruption predictor. A general model for such a cost-based criterion is derived here. This model is used to demonstrate that, with realistic assumptions about the cost of disruptions compared to the cost of unnecessary discharge terminations, existing disruption prediction techniques already meet the minimum criterion to reduce operating costs. [ABSTRACT FROM AUTHOR]
Published: 2023
Full Text: View/download PDF

30. Global optimization driven by genetic algorithms for disruption predictors based on APODIS architecture.

Author: Rattá, G.A., Vega, J., Murari, A., Dormido-Canto, S., and Moreno, R.
Subjects: *TOKAMAKS, *GLOBAL optimization, *GENETIC algorithms, *PARAMETERS (Statistics), *SIGNAL processing
Abstract: Since year 2010, the APODIS architecture has proven its accuracy predicting disruptions in JET tokamak. Nevertheless, it has shown margins for improvements, fact indisputable after the enhanced performances achieved in posterior upgrades. In this article, a complete optimization driven by Genetic Algorithms (GA) is applied to it aiming at considering all possible combination of signals, signal features, quantity of models, their characteristics and internal parameters. This global optimization targets the creation of the best possible system with a reduced amount of required training data. The results harbor no doubts about the reliability of the global optimization method, allowing to outperform the ones of previous versions: 91.77% of predictions (89.24% with an anticipation higher than 10 ms) with a 3.55% of false alarms. Beyond its effectiveness, it also provides the potential opportunity to develop a spectrum of future predictors using different training datasets. [ABSTRACT FROM AUTHOR]
Published: 2016
Full Text: View/download PDF

31. Improvements in disruption prediction at ASDEX Upgrade.

Author: Aledda, R., Cannas, B., Fanni, A., Pau, A., and Sias, G.
Subjects: *PLASMA instabilities, *TOKAMAKS, *PREDICTION models, *MACHINE learning, *LOGISTIC regression analysis
Abstract: In large-scale tokamaks disruptions have the potential to create serious damage to the facility. Hence disruptions must be avoided, but, when a disruption is unavoidable, minimizing its severity is mandatory. A reliable detection of a disruptive event is required to trigger proper mitigation actions. To this purpose machine learning methods have been widely studied to design disruption prediction systems at ASDEX Upgrade. The training phase of the proposed approaches is based on the availability of disrupted and non-disrupted discharges. In literature disruptive configurations were assumed appearing into the last 45 ms of each disruption. Even if the achieved results in terms of correct predictions were good, it has to be highlighted that the choice of such a fixed temporal window might have limited the prediction performance. In fact, it generates confusing information in cases of disruptions with disruptive phase different from 45 ms. The assessment of a specific disruptive phase for each disruptive discharge represents a relevant issue in understanding the disruptive events. In this paper, the Mahalanobis distance is applied to define a specific disruptive phase for each disruption, and a logistic regressor has been trained as disruption predictor. The results show that enhancements on the achieved performance on disruption prediction are possible by defining a specific disruptive phase for each disruption. [ABSTRACT FROM AUTHOR]
Published: 2015
Full Text: View/download PDF

32. Study of disruption in plasma of the Â«Globus-M/M2Â» tokamak using neural networks

Subjects: deep machine learning, disruption prediction, correlation, Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ, ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ð¾Ð½Ð½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð·, Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ðµ ÑÑÑÐ²Ð¾Ð²
Abstract: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ ÑÑÑÐ²Ð° Ð² Ð¿Ð»Ð°Ð·Ð¼Ðµ ÑÐ¾ÐºÐ°Ð¼Ð°ÐºÐ° âÐÐ»Ð¾Ð±ÑÑ-Ð/Ð2â Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð¡ÑÑÐ²Ñ ÑÐ²Ð»ÑÑÑÑÑ ÑÑÑÐ¾Ð³Ð¾ Ð½ÐµÐ¶ÐµÐ»Ð°ÑÐµÐ»ÑÐ½ÑÐ¼Ð¸ ÑÐ¾Ð±ÑÑÐ¸ÑÐ¼Ð¸ Ð´Ð»Ñ ÑÐ¾ÐºÐ°Ð¼Ð°ÐºÐ° ÑÐµÐ°ÐºÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð¼Ð°ÑÑÑÐ°Ð±Ð°, Ð¿Ð¾ÑÑÐ¾Ð¼Ñ Ð½ÐµÐ¾Ð±Ñ Ð¾Ð´Ð¸Ð¼Ð¾ Ð´Ð¾Ð±Ð¸ÑÑÑÑ Ð»Ð¸Ð±Ð¾ ÑÐ½Ð¸Ð¶ÐµÐ½Ð¸Ñ ÑÐ°ÑÑÐ¾ÑÑ Ð²Ð¾Ð·Ð½Ð¸ÐºÐ½Ð¾Ð²ÐµÐ½Ð¸Ñ ÑÑÑÐ²Ð¾Ð², Ð»Ð¸Ð±Ð¾ ÑÐ¼ÑÐ³ÑÐ¸ÑÑ Ð¿Ð¾ÑÐ»ÐµÐ´ÑÑÐ²Ð¸Ñ ÑÑÑÐ²Ð¾Ð², ÐºÐ¾ÑÐ¾ÑÑÑ Ð½ÐµÐ»ÑÐ·Ñ Ð¸Ð·Ð±ÐµÐ¶Ð°ÑÑ Ð¸Ð½ÑÐ¼Ð¸ ÑÐ¿Ð¾ÑÐ¾Ð±Ð°Ð¼Ð¸. ÐÐ»Ñ ÑÐ¼ÑÐ³ÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÑÐ»ÐµÐ´ÑÑÐ²Ð¸Ð¹ ÑÑÑÐ²Ð° Ð½ÐµÐ¾Ð±Ñ Ð¾Ð´Ð¸Ð¼Ð¾ ÑÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°ÑÑ ÑÑÐ¸Ð³Ð³ÐµÑ Ð´Ð°Ð½Ð½Ð¾Ð³Ð¾ ÑÐ¾Ð±ÑÑÐ¸Ñ Ñ Ð¾Ð¿ÐµÑÐµÐ¶ÐµÐ½Ð¸ÐµÐ¼ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð´Ð¾ÑÑÐ°ÑÐ¾ÑÐ½ÑÐ¼ Ð´Ð»Ñ Ð°ÐºÑÐ¸Ð²Ð°ÑÐ¸Ð¸ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐ¿ÑÐ°Ð²Ð»ÑÐµÐ¼Ð¾Ð³Ð¾ Ð³Ð°ÑÐµÐ½Ð¸Ñ ÑÐ°Ð·ÑÑÐ´Ð°. Ð¦ÐµÐ»Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ â Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÑ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð½Ð°Ñ ÑÐ¾Ð±ÑÑÐ¸Ð¹ Ð² Ð¿Ð»Ð°Ð·Ð¼Ðµ ÑÐ¾ÐºÐ°Ð¼Ð°ÐºÐ° ÐÐ»Ð¾Ð±ÑÑ-Ð2 Ð½Ð° Ð¿ÑÐ¸Ð¼ÐµÑÐµ Ð´Ð°Ð½Ð½ÑÑ Ð¾ ÑÑÑÐ²Ð°Ñ . ÐÐ»Ñ Ð´Ð¾ÑÑÐ¸Ð¶ÐµÐ½Ð¸Ñ Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ ÑÐµÐ»Ð¸ Ð±ÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ð¾Ð½Ð½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸Ðº Ð´Ð¾ÑÑÑÐ¿Ð½ÑÑ Ð² ÑÐµÐ°Ð»ÑÐ½Ð¾Ð¼ Ð²ÑÐµÐ¼ÐµÐ½Ð¸, ÑÑÐ¾Ð±Ñ Ð¾ÑÐ¾Ð±ÑÐ°ÑÑ Ð½Ð°Ð±Ð¾Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð², Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼ÑÑ Ð´Ð»Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ ÑÑÑÐ²Ð°. ÐÑÐ±ÑÐ°Ð½Ð° ÑÐ¾Ð¿Ð¾Ð»Ð¾Ð³Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ñ Ð¿Ð°ÑÐ°Ð»Ð»ÐµÐ»ÑÐ½ÑÐ¼Ð¸ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐ¼Ð¸ Ð¸ Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½ÑÐ¼Ð¸ ÑÐ»Ð¾ÑÐ¼Ð¸. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÐ¹ ÐºÐ¾Ð¼Ð¿Ð»ÐµÐºÑ Ð´Ð»Ñ Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð±Ð°Ð·Ð¸ÑÐ½Ð¾Ð³Ð¾ Ð½Ð°Ð±Ð¾ÑÐ° ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð²,Ð¿Ð¾Ð´Ð³Ð¾ÑÐ¾Ð²ÐºÐ¸ ÑÑÐµÐ½Ð¸ÑÐ¾Ð²Ð¾ÑÐ½ÑÑ Ð¿ÑÐ¸Ð¼ÐµÑÐ¾Ð², Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¸ Ð¾ÑÐµÐ½ÐºÐ¸, Ð¿Ð¾Ð»ÑÑÐ¸Ð²ÑÐµÐ¹ÑÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸. ÐÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ ÑÐ¸ÑÑÐµÐ¼Ñ Ð±ÑÐ»Ð° Ð¿ÑÐ¾Ð²ÐµÑÐµÐ½Ð° Ð½Ð° 50 ÑÐµÑÑÐ¾Ð²ÑÑ ÑÐ°Ð·ÑÑÐ´Ð°Ñ . ÐÐ½Ð°ÑÐµÐ½Ð¸Ðµ Ð»Ð¾Ð¶Ð½ÑÑ ÑÑÐ°Ð±Ð°ÑÑÐ²Ð°Ð½Ð¸Ñ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð² Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÐ¾ÑÑÐ°Ð²Ð¸Ð»Ð¾ 14%, Ð·Ð½Ð°ÑÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾Ð¿ÑÑÐµÐ½Ð½ÑÑ ÑÑÑÐ²Ð¾Ð² - 7%., This work is devoted to the study of the disruption prediction in the plasma of the Globus-M/M2 tokamak using deep machine learning. Disruptions are strictly undesirable events for a reactor-scale tokamak; therefore, it is necessary to either reduce the frequency of disruptions or mitigate the consequences of disruptions that cannot be avoided by other means. To mitigate the consequences of the breakdown, it is necessary to form a trigger of this event ahead of time sufficient to activate the controlled discharge extinguishing system. The purpose of this work is to investigate the possibility of using neural networks for predicted events in the plasma of the Globus-M2 tokamak using data on disruptions as an example. To achieve this goal, a correlation analysis of signals from diagnostics available in real time was carried out in order to select a set of signals used to predict breakdown. A neural network topology with parallel convolutional and fully connected layers is chosen. A software package has been developed for identifying a basic set of signals, preparing training examples, training a neural network model and evaluating the resulting efficiency. The efficiency of the system has been tested with 50 test discharges. The value of false alarms for the model trained in this work was 14%, the value of missed disruptions was 7%.
Published: 2021
Full Text: View/download PDF

33. Real-time disruption prediction in the plasma control system of HL-2A based on deep learning.

Author: Yang, Zongyu, Xia, Fan, Song, Xianming, Gao, Zhe, Li, Yixuan, Gong, Xinwen, Dong, Yunbo, Zhang, Yipo, Chen, Chengyuan, Luo, Cuiwen, Li, Bo, Zhu, Xiaobo, Ji, Xiaoquan, Li, Yonggao, Liu, Liang, Gao, Jinming, and Liu, Yuhang
Subjects: *PLASMA confinement, *PLASMA instabilities, *DEEP learning, *ARTIFICIAL intelligence, *MOLECULAR beams, *GAS injection
Abstract: • A deep learning- based real-time disruption predictor is implemented in the plasma control system (PCS) in HL-2A. It works well during the online testing of 382 shots and gets a TPR of 95.8% and a TNR of 77.5%. • A python interpreter + TFlite paradigm is proposed to implement AI modules in PCS. AI-based algorithms can be implemented into the C/C++ based PCS with little modification. Only 0.06 ms of time consumption and almost no penalty on the algorithm's performance will be introduced during this process. • An integrated system for disruption prediction and mitigation is developed in HL-2A. Some closed-loop online testing is implemented, during which the algorithm successfully triggered the MGI and SMBI system to mitigate the coming disruption. A real-time disruption predictor based on deep learning method is implemented into the Plasma Control System (PCS) of HL-2A. It has a total accuracy of 89.0% during the online testing of Shot Nos. 38,650–39,347 in HL-2A. 32 false alarms are triggered during the 142 non-disruptive shots and 10 disruptions are missed in the 240 disruptive shots. In several shots, this disruption prediction algorithm is used to trigger the disruption mitigation system, namely, massive gas injection (MGI) and supersonic molecular beam injection (SMBI) in this experiment. For MGI, there is a 12-millisecond delay between the trigger signal and the mitigated disruption. In the 240 disruptive shots, 81.5% of them are predicted with more than 12 ms in advance and thus can be successfully mitigated. For SMBI, the delay is 4 millisecond and 91.3% of the disruptions can be successfully mitigated. In general, although dedicated work is needed to close the gap between online disruption prediction and the offline algorithms, the effectiveness of deep learning-based disruption predictors in real-time environments is validated in this research. [ABSTRACT FROM AUTHOR]
Published: 2022
Full Text: View/download PDF

34. Multivariate statistical models for disruption prediction at ASDEX Upgrade.

Author: Aledda, R., Cannas, B., Fanni, A., Sias, G., and Pautasso, G.
Subjects: *NUCLEAR physics experiments, *SYMMETRY (Physics), *FUSION reactor divertors, *MULTIVARIATE analysis, *STATISTICAL models, *PREDICTION theory, *DATA analysis
Abstract: Abstract: In this paper, a disruption prediction system for ASDEX Upgrade has been proposed that does not require disruption terminated experiments to be implemented. The system consists of a data-based model, which is built using only few input signals coming from successfully terminated pulses. A fault detection and isolation approach has been used, where the prediction is based on the analysis of the residuals of an auto regressive exogenous input model. The prediction performance of the proposed system is encouraging when it is applied to the same set of campaigns used to implement the model. However, the false alarms significantly increase when we tested the system on discharges coming from experimental campaigns temporally far from those used to train the model. This is due to the well know aging effect inherent in the data-based models. The main advantage of the proposed method, with respect to other data-based approaches in literature, is that it does not need data on experiments terminated with a disruption, as it uses a normal operating conditions model. This is a big advantage in the prospective of a prediction system for ITER, where a limited number of disruptions can be allowed. [Copyright &y& Elsevier]
Published: 2013
Full Text: View/download PDF

35. Results of the JET real-time disruption predictor in the ITER-like wall campaigns.

Author: Vega, Jesús, Dormido-Canto, Sebastián, López, Juan M., Murari, Andrea, Ramírez, Jesús M., Moreno, Raúl, Ruiz, Mariano, Alves, Diogo, and Felton, Robert
Subjects: *REAL-time computing, *ELECTRIC discharges, *DATA analysis, *FALSE alarms
Abstract: Highlights: [•] JET real-time disruption predictor with metallic wall 991 discharges analyzed. [•] Predictor training has been carried out with JET C wall data. [•] Success, false alarm and missed alarm rates are 98.4%, 0.9% and 1.6%, respectively. [•] Alarms are triggered in average 426ms before the disruption. [Copyright &y& Elsevier]
Published: 2013
Full Text: View/download PDF

36. Adaptive mapping of the plasma operational space of ASDEX Upgrade for disruption prediction.

Author: Aledda, Raffaele, Cannas, Barbara, Fanni, Alessandra, Sias, Giuliana, Pautasso, Gabriella, and the ASDEX Upgrade team
Subjects: *SELF-organizing maps, *PLASMA gases, *TOKAMAKS, *TRAJECTORIES (Mechanics), *FUSION reactors, *PHYSICS experiments
Abstract: The Self-Organizing Map is a computational method for the visualization and analysis of high-dimensional data. Self Organizing Maps have been applied to ASDEX Upgrade data to define an ordered mapping of an 8-dimensional plasma parameter space onto a regular, 2-dimensional grid. The map has been used to track the plasma trajectory during the experiments and monitor the disruption risk. In order to face with ever new operational conditions, a periodical updating of the Self Organizing Map is proposed. [ABSTRACT FROM AUTHOR]
Published: 2012
Full Text: View/download PDF

37. Mapping of the ASDEX Upgrade Operational Space for Disruption Prediction.

Author: Aledda, Raffaele, Cannas, Barbara, Fanni, Alessandra, Sias, Giuliana, and Pautasso, Gabriella
Subjects: *SELF-organizing maps, *DATA visualization, *NEURONS, *PLASMA currents, *ELECTRIC breakdown
Abstract: The mapping of the n-dimensional plasma parameter space of ASDEX Upgrade (AUG) has been performed using a 2-D self-organizing map (SOM), which reveals the map potentiality in data visualization. The proposed approach allows us the definition of simple displays capable of presenting meaningful information on the actual state of the plasma, but it also suggests to use the SOM as a disruption predictor. In this paper, various criteria have been studied to associate the risk of disruption of each cluster in the map to a disruption alarm threshold. The data for this study come from AUG experiments executed between July 2002 and November 2009. The prediction performance of the proposed system has been evaluated on a set of discharges different from those used for the map training, obtaining a good prediction success rate. A visual analysis of the predictor input signals has been performed for wrong predictions in order to identify possible common causes, and some criteria to increase prediction performance have been derived. [ABSTRACT FROM PUBLISHER]
Published: 2012
Full Text: View/download PDF

38. Disruption prediction with adaptive neural networks for ASDEX Upgrade

Author: Cannas, B., Fanni, A., Pautasso, G., and Sias, G.
Subjects: *PREDICTION models, *ADAPTIVE control systems, *ARTIFICIAL neural networks, *SELF-organizing maps, *PERCEPTRONS, *NUCLEAR engineering
Abstract: Abstract: In this paper, an adaptive neural system has been built to predict the risk of disruption at ASDEX Upgrade. The system contains a Self Organizing Map, which determines the ‘novelty’ of the input of a Multi Layer Perceptron predictor module. The answer of the MLP predictor will be inhibited whenever a novel sample is detected. Furthermore, it is possible that the predictor produces a wrong answer although it is fed with known samples. In this case, a retraining procedure will be performed to update the MLP predictor in an incremental fashion using data coming from both the novelty detection, and from wrong predictions. In particular, a new update is performed whenever a missed alarm is triggered by the predictor. The performance of the adaptive predictor during the more recent experimental campaigns until November 2009 has been evaluated. [Copyright &y& Elsevier]
Published: 2011
Full Text: View/download PDF

39. Geometrical Kernel Machine for Prediction and Novelty Detection of Disruptive Events in TOKAMAK Machines.

Author: Cannas, Barbara, Delogu, Rita, Fanni, Alessandra, Montisci, Augusto, Sonato, Piergiorgio, and Zedda, Katiuscia
Abstract: This paper presents a recently introduced Kernel Machine, called Geometrical Kernel Machine, used to predict disruptive events in nuclear fusion reactors. The algorithm proposed to construct the Kernel Machine is able to automatically determine both the number of neurons and the synaptic weights of a Multilayer Perceptron neural network with a single hidden layer. It has been demonstrated that the resulting network is able to classify any finite set of patterns defined in a real domain. The prediction problem has been here modeled as a two classes classification problem. The geometrical interpretation of the network equations allows us both to develop the disruption predictor and to manage the so called ageing of the kernel machine. In fact, using the same kernel machine, a novelty detection system has been integrated in the predictor, increasing the overall system performance, and the reliability of the predictor. [ABSTRACT FROM AUTHOR]
Published: 2010
Full Text: View/download PDF

40. New information processing methods for control on JET

Author: Murari, A., Vega, J., Mazon, D., Rattá, G.A., Svensson, J., Vagliasindi, G., Blum, J., Boulbe, C., and Faugeras, B.
Subjects: *BAYESIAN analysis, *COMPUTER networks, *SUPPORT vector machines, *STATISTICAL hypothesis testing, *STATISTICS, *GEOMETRY
Abstract: Abstract: Magnetic confinement fusion devices are very integrated systems, difficult to access for measurement, and therefore they pose particular challenges to identification and to the prediction of undesired events. With regard to system identification, Bayesian statistics is a very promising methodology, which provides for the first time a sound way to include physical information about the diagnostics in the evaluation of the error bars. In a prototypical application, a Bayesian statistical approach determines the magnetic topology without any assumption on the equilibrium and provides clear confidence intervals on all the derived quantities. This technique, which complements another more traditional approach based on the Grad–Shafranov equation, can be implemented to provide results about every few milliseconds and therefore it can be envisaged to exploit for feedback purposes. Some phenomena in the evolution of Tokamak plasmas, like disruptions, are too dangerous and prohibitively difficult to control. For these cases avoidance is the best alternative and therefore specific classifiers have been trained and optimized to predict the occurrence of disruptions. The success rates of these predictors, mainly based on Support Vector Machines, are very often of the order or higher than 90%. The generalisation capability of the method has been confirmed by applying the same predictor to new campaigns without retraining. The success rate remains very high (above 80%) even 12 campaigns after the last one used for training. [ABSTRACT FROM AUTHOR]
Published: 2010
Full Text: View/download PDF

41. Criteria and algorithms for constructing reliable databases for statistical analysis of disruptions at ASDEX Upgrade

Author: Cannas, B., Fanni, A., Pautasso, G., Sias, G., and Sonato, P.
Subjects: *TOKAMAKS, *FAILURE analysis, *NUMERIC databases, *NUCLEAR reactor stability, *ALGORITHMS, *ARTIFICIAL neural networks, *STATISTICAL reliability, *STATISTICAL physics
Abstract: Abstract: The present understanding of disruption physics has not gone so far as to provide a mathematical model describing the onset of this instability. A disruption prediction system, based on a statistical analysis of the diagnostic signals recorded during the experiments, would allow estimating the probability of a disruption to take place. A crucial point for a good design of such a prediction system is the appropriateness of the data set. This paper reports the details of the database built to train a disruption predictor based on neural networks for ASDEX Upgrade. The criteria of pulses selection, the analyses performed on plasma parameters and the implemented pre-processing algorithms, are described. As an example of application, a short description of the disruption predictor is reported. [Copyright &y& Elsevier]
Published: 2009
Full Text: View/download PDF

42. A Disruption Predictor Based on Fuzzy Logic Applied to JET. Database.

Author: Vagliasindi, Guido, Murari, Andrea, Arena, Paolo, Fortuna, Luigi, Johnson, Mike, and Howell, David
Subjects: *FUZZY logic, *FUZZY systems, *FUSION reactors, *PLASMA gases, *TOKAMAKS, *PINCH effect (Physics), *MAGNETIC fields, *ELECTRIC currents, *FALSE alarms
Abstract: Disruptions remain a serious issue for International Thermonuclear Experimental Reactor (ITER) and the commercial reactor. In the past years, significant efforts have been devoted to the development of reliable disruption predictors. In this paper, the potential of fuzzy logic in this field is explored. A new disruption predictor for Joint European Torus (JET), which is based on fuzzy logic, was tested off-line using data collected during several JET campaigns, covering a very wide range of scenarios and plasma parameters. In the standard configuration, it was able to predict 92% of disruptions with about 18% of false alarms (FAs). The flexibility of the approach, which is to provide a different tradeoff between missed and FAs, depending on the desired results in terms of safety, is demonstrated. The potential of the technique to help quantifying the risks that are associated with the operation outside the already explored parameter space is also discussed. [ABSTRACT FROM AUTHOR]
Published: 2008
Full Text: View/download PDF

43. Support vector machines for disruption prediction and novelty detection at JET

Author: Cannas, B., Delogu, R.S., Fanni, A., Sonato, P., and Zedda, M.K.
Subjects: *ARTIFICIAL neural networks, *ARTIFICIAL intelligence, *ELECTRIC lines, *ENERGY industries
Abstract: Abstract: In the last years there has been a growing interest on black box approaches to disruption prediction. The drawback of these approaches is that the system could deteriorate its performance once it does not get updated. This could be the case of a disruption predictor for JET, where new plasma configurations might present features completely different from those observed in the experiments used during the training phase. This ‘novelty’ can be incorrectly classified by the system. A novelty detection method, which determines the novelty of the input of the prediction system, can be used to assess the system reliability. This paper presents a support vector machines disruption predictor for JET, wherein multiple plasma diagnostic signals are combined to provide a composite impending disruption warning indicator. In a support vector machine the analysis of the decision function value gives useful information about the novelty of an input and, on the reliability of the predictor output, during on-line applications. Results show the suitability of support vector machines both for prediction and novelty detection tasks at JET. [Copyright &y& Elsevier]
Published: 2007
Full Text: View/download PDF

44. Performance Enhancement of On-Board Communication Networks Using Outage Prediction.

Author: Baig, Adeel, Libman, Lavy, and Hassan, Mahbub
Subjects: MOBILE communication systems, INTERNET, TCP/IP, MARKOV processes, TELECOMMUNICATION systems
Abstract: A research area that has become increasingly important in recent years is that of on-board mobile communication, where users on a vehicle are connected to a local network that attaches to the Internet via a mobile router and a wireless link. In this architecture, link disruptions (e.g., due to signal degradation) may have an immediate impact on a potentially large number of connections. We argue that the advance knowledge of public transport routes, and their repetitive nature, allows a certain degree of prediction of impending link disruptions, which can be used to offset their catastrophic impact. Focusing on the transmission control protocol (TCP) and its extension known as Freeze-TCP, we present a detailed analysis of the performance improvement of TCP connections in the presence of disruption prediction. In particular, we propose a Markov model of Freeze-TCP that captures both the TCP behavior and the prediction+"freezing" feature and, using simulations, show that it accurately predicts the performance of the protocol. Our results demonstrate the significant throughput improvement that can be gained by disruption prediction, even with random packet losses or imperfect timing of the predicted disruptions. [ABSTRACT FROM AUTHOR]
Published: 2006
Full Text: View/download PDF

45. A locked mode indicator for disruption prediction on JET and ASDEX upgrade

Author: Sias, G, Cannas, B, Fanni, A, Murari, A, Pau, A, Kallenbach, A, Aguiam, D, Aho-Mantila, L, Angioni, C, Arden, N, Arredondo Parra, R, Asunta, O, de Baar, M, Balden, M, Behler, K, Bergmann, A, Bernardo, J, Bernert, M, Beurskens, M, Biancalani, A, Bilato, R, Birkenmeier, G, Bobkov, V, Bock, A, Bogomolov, A, Bolzonella, T, Boeswirth, B, Bottereau, C, Bottino, A, van den Brand, H, Brezinsek, S, Brida, D, Brochard, F, Bruhn, C, Buchanan, J, Buhler, A, Burckhart, A, Cambon-Silva, D, Camenen, Y, Carvalho, P, Carrasco, G, Cazzaniga, C, Carr, M, Carralero, D, Casali, L, Castaldo, C, Cavedon, M, Challis, C, Chankin, A, Chapman, I, Clairet, F, Classen, I, Coda, S, Coelho, R, Coenen, J, Colas, L, Conway, G, Costea, S, Coster, D, Croci, G, Cseh, G, Czarnecka, A, D'Arcangelo, O, Day, C, Delogu, R, de Marne, P, Denk, S, Denner, P, Dibon, M, D'Inca, R, Di Siena, A, Douai, D, Drenik, A, Drube, R, Dunne, M, Duval, B, Dux, R, Eich, T, Elgeti, S, Engelhardt, K, Erdos, B, Erofeev, I, Esposito, B, Fable, E, Faitsch, M, Fantz, U, Faugel, H, Felici, F, Fietz, S, Figueredo, A, Fischer, R, Ford, O, Frassinetti, L, Freethy, S, Froeschle, M, Fuchert, G, Fuchs, J, Fuenfgelder, H, Galazka, K, Galdon-Quiroga, J, Gallo, A, Gao, Y, Garavaglia, S, Garcia-Munoz, M, Geiger, B, Cianfarani, C, Giannone, L, Giovannozzi, E, Gleason-Gonzalez, C, Gloeggler, S, Gobbin, M, Goerler, T, Goodman, T, Gorini, G, Gradic, D, Graeter, A, Granucci, G, Greuner, H, Griener, M, Groth, M, Gude, A, Guenter, S, Guimarais, L, Haas, G, Hakola, A, Ham, C, Happel, T, Harrison, J, Hatch, D, Hauer, V, Hayward, T, Heinemann, B, Heinzel, S, Hellsten, T, Henderson, S, Hennequin, P, Herrmann, A, Heyn, E, Hitzler, F, Hobirk, J, Hoelzl, M, Hoeschen, T, Holm, J, Hopf, C, Hoppe, F, Horvath, L, Houben, A, Huber, A, Igochine, V, Ilkei, T, Ivanova-Stanik, I, Jacob, W, Jacobsen, A, Jacquot, J, Janky, F, Jardin, A, Jaulmes, F, Jenko, F, Jensen, T, Joffrin, E, Kasemann, C, Kalvin, S, Kantor, M, Kappatou, A, Kardaun, O, Karhunen, J, Kasilov, S, Kernbichler, W, Kim, D, Kimmig, S, Kirk, A, Klingshirn, H, Koch, F, Kocsis, G, Koehn, A, Kraus, M, Krieger, K, Krivska, A, Kraemer-Flecken, A, Kurki-Suonio, T, Kurzan, B, Lackner, K, Laggner, F, Lang, P, Lauber, P, Lazanyi, N, Lazaros, A, Lebschy, A, Li, L, Li, M, Liang, Y, Lipschultz, B, Liu, Y, Lohs, A, Luhmann, N, Lunt, T, Lyssoivan, A, Madsen, J, Maier, H, Maj, O, Mailloux, J, Maljaars, E, Manas, P, Mancini, A, Manhard, A, Manso, M, Mantica, P, Mantsinen, M, Manz, P, Maraschek, M, Martens, C, Martin, P, Marrelli, L, Martitsch, A, Mastrostefano, S, Mayer, A, Mayer, M, Mazon, D, McCarthy, P, McDermott, R, Meisl, G, Meister, H, Medvedeva, A, Merkel, P, Merkel, R, Merle, A, Mertens, V, Meshcheriakov, D, Meyer, H, Meyer, O, Miettunen, J, Milanesio, D, Mink, F, Mlynek, A, Monaco, F, Moon, C, Nazikian, R, Nemes-Czopf, A, Neu, G, Neu, R, Nielsen, A, Nielsen, S, Nikolaeva, V, Nocente, M, Noterdaeme, J, Nowak, S, Oberkofler, M, Oberparleiter, M, Ochoukov, R, Odstrcil, T, Olsen, J, Orain, F, Palermo, F, Papp, G, Paradela Perez, I, Pautasso, G, Enzel, F, Petersson, P, Pinzon, J, Piovesan, P, Piron, C, Plaum, B, Ploeckl, B, Plyusnin, V, Pokol, G, Poli, E, Porte, L, Potzel, S, Prisiazhniuk, D, Putterich, T, Ramisch, M, Rapson, C, Rasmussen, J, Raupp, G, Refy, D, Reich, M, Reimold, F, Ribeiro, T, Riedl, R, Rittich, D, Rocchi, G, Rodriguez-Ramos, M, Rohde, V, Ross, A, Rott, M, Rubel, M, Ryan, D, Ryter, F, Saarelma, S, Salewski, M, Salmi, A, Sanchis-Sanchez, L, Santos, G, Santos, J, Sauter, O, Scarabosio, A, Schall, G, Schmid, K, Schmitz, O, Schneider, P, Schneller, M, Schrittwieser, R, Schubert, M, Schwarz-Selinger, T, Schweinzer, J, Scott, B, Sehmer, T, Sertoli, M, Shabbir, A, Shalpegin, A, Shao, L, Sharapov, S, Siccinio, M, Sieglin, B, Sigalov, A, Silva, A, Silva, C, Simon, P, Simpson, J, Snicker, A, Sommariva, C, Sozzi, C, Spolaore, M, Stejner, M, Stober, J, Stobbe, F, Stroth, U, Strumberger, E, Suarez, G, Sugiyama, K, Sun, H, Suttrop, W, Szepesi, T, Tal, B, Tala, T, Tardini, G, Tardocchi, M, Terranova, D, Tierens, W, Told, D, Tudisco, O, Trevisan, G, Treutterer, W, Trier, E, Tripsky, M, Valisa, M, Valovic, M, Vanovac, B, Varela, P, Varoutis, S, Verdoolaege, G, Vezinet, D, Vianello, N, Vicente, J, Vierle, T, Viezzer, E, von Toussaint, U, Wagner, D, Wang, N, Wang, X, Weidl, M, Weiland, M, White, A, Willensdorfer, M, Wiringer, B, Wischmeier, M, Wolf, R, Wolfrum, E, Xiang, L, Yang, Q, Yang, Z, Yu, Q, Zagorski, R, Zammuto, I, Zarzoso, D, Zhang, W, van Zeeland, M, Zehetbauer, T, Zilker, M, Zoletnik2, S, Zohm, H, Adamek, J, Agostini, M, Ahn, J, Akers, R, Albanese, R, Aledda, R, Alessi, E, Allan, S, Alves, D, Ambrosino, R, Amicucci, L, Anand, H, Anastassiou, G, Andrebe, Y, Apruzzese, G, Ariola, M, Arnichand, H, Arter, W, Baciero, A, Barnes, M, Barrera, L, Behn, R, Bencze, A, Bettini, P, Bilkova, P, Bin, W, Bizarro, J, Blanchard, P, Blanken, T, Bluteau, M, Bogar, O, Bohm, P, Boncagni, L, Botrugno, A, Bouquey, F, Bourdelle, C, Bremond, S, Bufferand, H, Buratti, P, Cahyna, P, Calabro, G, Caniello, R, Canton, A, Cardinali, A, Carnevale, D, Castejon, F, Castro, R, Causa, F, Cavazzana, R, Cecconello, M, Ceccuzzi, S, Cesario, R, Chapman, S, Chernyshova, M, Choi, D, Ciraolo, G, Citrin, J, Corre, Y, Crisanti, F, Cruz, N, De Angeli, M, De Masi, G, De Temmerman, G, De Tommasi, G, Decker, J, Dendy, R, Di Troia, C, Dimitrova, M, Doric, V, Dudson, B, Dunai, D, Easy, L, Elmore, S, Fedorczak, N, Ferreira, J, Fevrier, O, Ficker, O, Figini, L, Figueiredo, A, Fil, A, Fishpool, G, Fitzgerald, M, Fontana, M, Fridstrom, R, Frigione, D, Fuchs, C, Furno Palumbo, M, Futatani, S, Gabellieri, L, Galeani, S, Gallart, D, Galperti, C, Garcia, J, Garcia-Carrasco, A, Garcia-Lopez, J, Gardarein, J, Garzotti, L, Gaspar, J, Gauthier, E, Geelen, P, Ghendrih, P, Ghezzi, F, Giacomelli, L, Giroud, C, Gleason Gonzalez, C, Gospodarczyk, M, Gruber, M, Guirlet, R, Gunn, J, Hacek, P, Hacquin, S, Hall, S, Harting, D, Havlickova, E, Helou, W, Heyn, M, Hnat, B, Holzl, M, Hogeweij, D, Honore, C, Horacek, J, Hornung, G, Huang, Z, Igitkhanov, J, Imrisek, M, Innocente, P, Ionita-Schrittwieser, C, Isliker, H, Jacquet, P, Jakubowski, M, Jeppe Miki Busk, O, Jessen, M, Jones, O, Jonsson, T, Kallinikos, N, Karpushov, A, Kasprowicz, G, Kendl, A, Kjer, S, Klimek, I, Kogut, D, Komm, M, Korsholm, S, Koslowski, H, Koubiti, M, Kovacic, J, Kovarik, K, Krawczyk, N, Krbec, J, Kube, R, Kudlacek, O, Labit, B, Laguardia, L, Lahtinen, A, Lalousis, P, Le, H, Leddy, J, Lefevre, L, Lehnen, M, Leipold, F, Lessig, A, Leyland, M, Loarer, T, Loarte, A, Loewenhoff, T, Lomanowski, B, Loschiavo, V, Lupelli, I, Lux, H, Maget, P, Maggi, C, Maggiora, R, Magnussen, M, Maljaars, B, Malygin, A, Marchand, B, Marconato, N, Marini, C, Marinucci, M, Markovic, T, Marocco, D, Martin Solis, J, Mattei, M, Matthews, G, Mavridis, M, Mayoral, M, McAdams, R, McArdle, G, McClements, K, McMillan, B, Militello, F, Miron, I, Mitosinkova, K, Mlynar, J, Molina, D, Molina, P, Monakhov, I, Morales, J, Moreau, D, Morel, P, Moret, J, Moro, A, Moulton, D, Mueller, H, Nabais, F, Nardon, E, Naulin, V, Nespoli, F, Nimb, S, Nouailletas, R, Omotani, J, O'Mullane, M, Osterman, N, Paccagnella, R, Pamela, S, Pangione, L, Panjan, M, Paprok, R, Parail, V, Parra, F, Pehkonen, S, Pereira, A, Perelli Cippo, E, Pericoli Ridolfini, V, Peterka, M, Petrzilka, V, Pironti, A, Pisano, F, Pisokas, T, Pitts, R, Ploumistakis, I, Poljak, D, Poloskei, P, Popovic, Z, Por, G, Predebon, I, Preynas, M, Primc, G, Pucella, G, Puiatti, M, Puetterich, T, Rack, M, Ramogida, G, Juul Rasmussen, J, Ratta, G, Ratynskaia, S, Ravera, G, Reimerdes, H, Reinke, M, Reiser, D, Resnik, M, Reux, C, Ripamonti, D, Riva, G, Rosato, J, Sabot, R, Saint-Laurent, F, Samaddar, D, Scannell, R, Scheffer, M, Schneider, M, Schneider, B, Seidl, J, Sesnic, S, Shanahan, B, Sheikh, U, Silva Fuglister, M, Spagnolo, S, Spizzo, G, Stange, T, Stejner Pedersen, M, Stepanov, I, Strand, P, Susnjara, A, Tamain, P, Teplukhina, A, Testa, D, Theiler, C, Thornton, A, Tolias, P, Tophoj, L, Tsironis, C, Tsui, C, Uccello, A, Urban, J, Vallejos, P, Vartanian, S, Vega, J, Verhaegh, K, Vermare, L, Vignitchouk, L, Vijvers, W, Villone, F, Viola, B, Vlahos, L, Voitsekhovitch, I, Vondracek, P, Vu, N, Walkden, N, Wauters, T, Weinzettl, V, Westerhof, E, Wiesenberger, M, Wodniak, I, Yadykin, D, Zanca, P, Zaplotnik, R, Zestanakis, P, Zuin, M, Litaudon, X, Abduallev, S, Abhangi, M, Abreu, P, Afzal, M, Aggarwal, K, Ahlgren, T, Aiba, N, Airila, M, Aldred, V, Alegre, D, Aleynikov, P, Alfier, A, Alkseev, A, Allinson, M, Alper, B, Alves, E, Ambrosino, G, Amosov, V, Andersson Sunden, E, Angelone, M, Anghel, M, Appel, L, Appelbee, C, Arena, P, Arshad, S, Ash, A, Ashikawa, N, Aslanyan, V, Auriemma, F, Austin, Y, Avotina, L, Axton, M, Ayres, C, Bacharis, M, Baiao, D, Bailey, S, Baker, A, Balboa, I, Balshaw, N, Bament, R, Banks, J, Baranov, Y, Barnard, M, Barnes, D, Barnsley, R, Baron Wiechec, A, Barrera Orte, L, Baruzzo, M, Basiuk, V, Bassan, M, Bastow, R, Batista, A, Batistoni, P, Baughan, R, Bauvir, B, Baylor, L, Bazylev, B, Beal, J, Beaumont, P, Beckers, M, Beckett, B, Becoulet, A, Bekris, N, Beldishevski, M, Bell, K, Belli, F, Bellinger, M, Belonohy, E, Ben Ayed, N, Benterman, N, Bergsaker, H, Berry, M, Bertalot, L, Besliu, C, Bieg, B, Bielecki, J, Biewer, T, Bigi, M, Binda, F, Bisoffi, A, Bjorkas, C, Blackburn, J, Blackman, K, Blackman, T, Blatchford, P, Boboc, A, Bodnar, G, Bolshakova, I, Bonanomi, N, Bonelli, F, Boom, J, Booth, J, Borba, D, Borodin, D, Borodkina, I, Boulting, P, Bowden, M, Bower, C, Bowman, C, Boyce, T, Boyd, C, Boyer, H, Bradshaw, J, Braic, V, Bravanec, R, Breizman, B, Brennan, P, Breton, S, Brett, A, Bright, M, Brix, M, Broeckx, W, Brombin, M, Broslawski, A, Brown, D, Brown, M, Bruno, E, Bucalossi, J, Buch, J, Buckley, M, Budny, R, Bulman, M, Bulmer, N, Bunting, P, Buscarino, A, Busse, A, Butler, N, Bykov, I, Byrne, J, Calvo, I, Camp, P, Campling, D, Cane, J, Capel, A, Card, P, Carman, P, Carraro, L, Carvalho, B, Carvalho, I, Casson, F, Catarino, N, Caumont, J, Cave-Ayland, K, Cavinato, M, Cecil, E, Cenedese, A, Chandler, M, Chandra, D, Chang, C, Chitarin, G, Ciric, D, Clark, E, Clark, M, Clarkson, R, Clatworthy, D, Clements, C, Cleverly, M, Coad, J, Coates, P, Cobalt, A, Coccorese, V, Cocilovo, V, Coffey, I, Collins, S, Conka, D, Conroy, S, Conway, N, Coombs, D, Cooper, D, Cooper, S, Corradino, C, Corrigan, G, Cortes, S, Couchman, A, Cox, M, Craciunescu, T, Cramp, S, Craven, R, Croft, D, Crombe, K, Crowe, R, Cufar, A, Cullen, A, Curuia, M, Dabirikhah, H, Dalgliesh, P, Dalley, S, Dankowski, J, Darrow, D, Davies, O, Davis, W, Day, I, De Bock, M, de Castro, A, de la Cal, E, de la Luna, E, de Pablos, J, de Vries, P, Deakin, K, Deane, J, Degli Agostini, F, Dejarnac, R, Delabie, E, den Harder, N, Denis, J, Devaux, S, Devynck, P, Di Maio, F, Dinca, P, Ding, B, Dittmar, T, Doerk, H, Doerner, R, Donne, T, Dorling, S, Dormido-Canto, S, Doswon, S, Doyle, P, Drewelow, P, Drews, P, Duckworth, P, Dumont, R, Dumortier, P, Duran, I, Durodie, F, Dutta, P, Dylst, K, Dzysiuk, N, Edappala, P, Edmond, J, Edwards, A, Edwards, J, Ekedahl, A, El-Jorf, R, Elsmore, C, Enachescu, M, Ericsson, G, Eriksson, F, Eriksson, J, Eriksson, L, Esquembri, S, Esser, H, Esteve, D, Evans, B, Evans, G, Evison, G, Ewart, G, Fagan, D, Falie, D, Fasoli, A, Faustin, J, Fawlk, N, Fazendeiro, L, Felton, R, Fenton, K, Fernades, A, Fernandes, H, Fessey, J, Field, A, Figueiredo, J, Finburg, P, Firdaouss, M, Fischer, U, Fittill, L, Flammini, D, Flanagan, J, Fleming, C, Flinders, K, Fonnesu, N, Fontdecaba, J, Formisano, A, Forsythe, L, Fortuna, L, Fortuna-Zalesna, E, Fortune, M, Foster, S, Franke, T, Franklin, T, Frasca, M, Freisinger, M, Fresa, R, Fuchs, V, Fuller, D, Fyvie, J, Gal, K, Galassi, D, Gallagher, J, Galvao, R, Gao, X, Gaudio, P, Gear, D, Gee, S, Gelfusa, M, Gerasimov, S, Gervasini, G, Gethins, M, Ghani, Z, Ghate, M, Gherendi, M, Giacalone, J, Gibson, C, Giegerich, T, Gil, C, Gil, L, Gilligan, S, Gin, D, Girardo, J, Giruzzi, G, Godwin, J, Goff, J, Gohil, P, Goloborod'ko, V, Gomes, R, Goncalves, B, Goniche, M, Goodliffe, M, Goodyear, A, Gosk, M, Goulding, R, Goussarov, A, Gowland, R, Graham, B, Graham, M, Graves, J, Grazier, N, Grazier, P, Green, N, Grierson, B, Griph, F, Grisolia, C, Grist, D, Grove, R, Grundy, C, Grzonka, J, Guard, D, Guerard, C, Guillemaut, C, Gurl, C, Utoh, H, Hackett, L, Hagar, A, Hager, R, Halitovs, M, Hallworth Cook, S, Hamlyn-Harris, C, Hammond, K, Harrington, C, Hasenbeck, F, Hatano, Y, Haupt, T, Hawes, J, Hawkes, N, Hawkins, J, Hawkins, P, Haydon, P, Hayter, N, Hazel, S, Heesterman, P, Heinola, K, Hellesen, C, Hemming, O, Hender, T, Henderson, M, Henriques, R, Hepple, D, Hermon, G, Hertout, P, Hidalgo, C, Highcock, E, Hill, M, Hillairet, J, Hillesheim, J, Hillis, D, Hizanidis, K, Hjalmarsson, A, Hodille, E, Hogben, C, Hogeweij, G, Hollingsworth, A, Hollis, S, Homfray, D, Horton, A, Horton, L, Hotchin, S, Hough, M, Howarth, P, Hubbard, A, Huber, V, Huddleston, T, Hughes, M, Huijsmans, G, Hunter, C, Huynh, P, Hynes, A, Iglesias, D, Imazawa, N, Imbeaux, F, Incelli, M, Irishkin, M, Jachmich, S, Jansons, J, Jarvinen, A, Jednorog, S, Jenkins, I, Jeong, C, Jepu, I, Johnson, R, Johnson, T, Johnston, J, Joita, L, Jones, G, Jones, T, Hoshino, K, Kamiya, K, Kaniewski, J, Kantor, A, Karkinsky, D, Karnowska, I, Kaufman, M, Kaveney, G, Kazakov, Y, Kazantzidis, V, Keeling, D, Keenan, T, Keep, J, Kempenaars, M, Kennedy, C, Kenny, D, Kent, J, Kent, O, Khilkevich, E, Kim, H, Kinch, A, King, C, King, D, King, R, Kinna, D, Kiptily, V, Kirov, K, Kirschner, A, Kizane, G, Klepper, C, Klix, A, Knight, P, Knipe, S, Knott, S, Kobuchi, T, Koechl, F, Kodeli, I, Kogan, L, Koivuranta, S, Kominis, Y, Koeppen, M, Kos, B, Koskela, T, Kovari, M, Kowalska-Strzeciwilk, E, Krasilnikov, A, Krasilnikov, V, Kresina, M, Kruezi, U, Ksiazek, I, Kukushkin, A, Kundu, A, Kwak, S, Kwiatkowski, R, Kwon, O, Laing, A, Lam, N, Lambertz, H, Lane, C, Lanthaler, S, Lapins, J, Lasa, A, Last, J, Laszynska, E, Lawless, R, Lawson, A, Lawson, K, Lazzaro, E, Lee, S, Lefebvre, X, Leggate, H, Lehmann, J, Leichtle, D, Leichuer, P, Lengar, I, Lennholm, M, Lerche, E, Lescinskis, A, Lesnoj, S, Letellier, E, Leysen, W, Likonen, J, Linke, J, Linsmeier, C, Liu, G, Lo Schiavo, V, Lobel, R, Lomas, P, Lonnroth, J, Lopez, J, Lopez-Razola, J, Lorenzini, R, Losada, U, Lovell, J, Loving, A, Lowry, C, Luce, T, Lucock, R, Lukin, A, Luna, C, Lungaroni, M, Lungu, C, Lungu, M, Lunniss, A, Macdonald, N, Macheta, P, Maczewa, K, Magesh, B, Makkonen, T, Makwana, R, Malaquias, A, Malizia, A, Manning, A, Manzanares, A, Maquet, P, Marandet, Y, Marcenko, N, Marchetto, C, Marchuk, O, Marinelli, M, Marot, L, Marren, C, Marshal, R, Martin de Aguilera, A, Martinez, F, Martin-Solis, J, Martynova, Y, Maruyama, S, Masiello, A, Maslov, M, Matejcik, S, Maviglia, F, May-Smith, T, Mazzotta, C, McCormack, O, McCullen, P, McDonald, D, McIntosh, S, McKean, R, McKehon, J, Meadows, R, Meakins, A, Medina, F, Medland, M, Medley, S, Meigh, S, Meigs, A, Meitner, S, Meneses, L, Menmuir, S, Mergia, K, Merrigan, I, Mertens, P, Meshchaninov, S, Messiaen, A, Mianowski, S, Michling, R, Middleton-Gear, D, Militello-Asp, E, Miloshevsky, G, Minucci, S, Miyoshi, Y, Moneti, M, Mooney, R, Moradi, S, Mordijck, S, Moreira, L, Moreno, R, Moro, F, Morris, A, Morris, J, Moser, L, Mosher, S, Muraro, A, Murphy, S, Asakura, N, Na, Y, Naish, R, Nakano, T, Nave, M, Nedzelski, I, Nemtsev, G, Neto, A, Neverov, V, Newman, M, Nicholls, K, Nicolas, T, Nielsen, P, Nilsson, E, Nishijima, D, Noble, C, Nodwell, D, Nordlund, K, Nordman, H, Nunes, I, Odupitan, T, Ogawa, M, O'Gorman, T, Okabayashi, M, Olney, R, Omolayo, O, Ongena, J, Orsitto, F, Orszagh, J, Oswuigwe, B, Otin, R, Owen, A, Pace, N, Pacella, D, Packer, L, Page, A, Pajuste, E, Palazzo, S, Panja, S, Papp, P, Park, M, Parra Diaz, F, Parsons, M, Pasqualotto, R, Patel, A, Pathak, S, Paton, D, Patten, H, Pawelec, E, Paz Soldan, C, Peackoc, A, Pearson, I, Peluso, E, Penot, C, Pereira, R, Pereira Puglia, P, Perez von Thun, C, Peruzzo, S, Peschanyi, S, Petravich, G, Petre, A, Petrella, N, Peysson, Y, Pfefferle, D, Philipps, V, Pillon, M, Pintsuk, G, Pires dos Reis, A, Piron, L, Pizzo, F, Pomaro, N, Pompilian, O, Pool, P, Popovichev, S, Porfiri, M, Porosnicu, C, Porton, M, Possnert, G, Powell, T, Pozzi, J, Prajapati, V, Prakash, R, Prestopino, G, Price, D, Price, M, Price, R, Prior, P, Proudfoot, R, Puglia, P, Pulley, D, Purahoo, K, Rachlew, E, Ragona, R, Rainford, M, Rakha, A, Ranjan, S, Rathod, K, Rayner, C, Rebai, M, Reece, D, Reed, A, Regan, B, Regana, J, Reid, N, Reinhart, M, Rendell, D, Reyes Cortes, S, Reynolds, S, Riccardo, V, Richardson, N, Riddle, K, Rigamonti, D, Rimini, F, Risner, J, Riva, M, Roach, C, Robins, R, Robinson, S, Robinson, T, Robson, D, Roccella, R, Rodionov, R, Rodrigues, P, Rodriguez, J, Romanelli, F, Romanelli, M, Romanelli, S, Romazanov, J, Rowe, S, Rubinacci, G, Rubino, G, Ruchko, L, Ruiz, M, Ruset, C, Rzadkiewicz, J, Safi, E, Sagar, P, Saibene, G, Salmon, R, Salzedas, F, Samm, U, Sandiford, D, Santa, P, Santala, M, Santos, B, Santucci, A, Sartori, F, Sartori, R, Schlummer, T, Schmidt, V, Schmuck, S, Schoepf, K, Schworer, D, Scott, S, Sergienko, G, Shaw, A, Shaw, R, Sheikh, H, Shepherd, A, Shevelev, A, Shumack, A, Sibbald, M, Silburn, S, Simmons, P, Simpson-Hutchinson, J, Sinha, A, Sipila, S, Sips, A, Siren, P, Sirinelli, A, Sjostrand, H, Skiba, M, Skilton, R, Slabkowska, K, Slade, B, Smith, N, Smith, P, Smith, R, Smith, T, Smithies, M, Snoj, L, Soare, S, Solano, E, Somers, A, Sonato, P, Sopplesa, A, Sousa, J, Spelzini, T, Spineanu, F, Stables, G, Stamatelatos, I, Stamp, M, Staniec, P, Stankunas, G, Stan-Sion, C, Stead, M, Stefanikova, E, Stephen, A, Stephen, M, Stevens, A, Stevens, B, Strachan, J, Strauss, H, Strom, P, Stubbs, G, Studholme, W, Subba, F, Summers, H, Svensson, J, Swiderski, L, Szabolics, T, Szawlowski, M, Szepesi, G, Suzuki, T, Talbot, A, Talebzadeh, S, Taliercio, C, Tame, C, Tang, W, Taroni, L, Taylor, D, Taylor, K, Tegnered, D, Telesca, G, Teplova, N, Tholerus, E, Thomas, J, Thomas, P, Thompson, A, Thompson, C, Thompson, V, Thorne, L, Thrysoe, A, Tigwell, P, Tipton, N, Tiseanu, I, Tojo, H, Tokitani, M, Tomes, M, Tonner, P, Towndrow, M, Trimble, P, Tsalas, M, Tsavalas, P, Tskhakaya Jun, D, Turner, I, Turner, M, Turnyanskiy, M, Tvalashvili, G, Tyrrell, S, Ul-Abidin, Z, Uljanovs, J, Ulyatt, D, Urano, H, Uytdenhouwen, I, Vadgama, A, Valcarcel, D, Valentinuzzi, M, Vallejos Olivares, P, Van De Mortel, M, Van Eester, D, Van Renterghem, W, van Rooij, G, Varje, J, Vasava, K, Vasilopoulou, T, Verhoeven, R, Verona, C, Verona Rinati, G, Veshchev, E, Villari, S, Vincenzi, P, Vinyar, I, Vitins, A, Vizvary, Z, Vlad, M, Vora, N, Vu, T, Pires de Sa, W, Wakeling, B, Waldon, C, Walker, M, Walker, R, Walsh, M, Wang, E, Warder, S, Warren, R, Waterhouse, J, Watkins, N, Watts, C, Weckmann, A, Weiland, J, Weisen, H, Weiszflog, M, Wellstood, C, West, A, Wheatley, M, Whetham, S, Whitehead, A, Whitehead, B, Widdowson, A, Wiesen, S, Wilkinson, J, Williams, J, Williams, M, Wilson, A, Wilson, D, Wilson, H, Wilson, J, Withenshaw, G, Withycombe, A, Witts, D, Wood, D, Wood, R, Woodley, C, Wray, S, Wright, J, Wu, J, Wukitch, S, Wynn, A, Xu, T, Yadikin, D, Yanling, W, Yao, L, Yavorskij, V, Yoo, M, Young, C, Young, D, Young, I, Young, R, Zacks, J, Zaitsev, F, Zanino, R, Zarins, A, Zastrow, K, Zerbini, M, Zhou, Y, Zilli, E, Zoita, V, Zychor, I, Sias, G, Cannas, B, Fanni, A, Murari, A, Pau, A, Kallenbach, A, Aguiam, D, Aho-Mantila, L, Angioni, C, Arden, N, Arredondo Parra, R, Asunta, O, de Baar, M, Balden, M, Behler, K, Bergmann, A, Bernardo, J, Bernert, M, Beurskens, M, Biancalani, A, Bilato, R, Birkenmeier, G, Bobkov, V, Bock, A, Bogomolov, A, Bolzonella, T, Boeswirth, B, Bottereau, C, Bottino, A, van den Brand, H, Brezinsek, S, Brida, D, Brochard, F, Bruhn, C, Buchanan, J, Buhler, A, Burckhart, A, Cambon-Silva, D, Camenen, Y, Carvalho, P, Carrasco, G, Cazzaniga, C, Carr, M, Carralero, D, Casali, L, Castaldo, C, Cavedon, M, Challis, C, Chankin, A, Chapman, I, Clairet, F, Classen, I, Coda, S, Coelho, R, Coenen, J, Colas, L, Conway, G, Costea, S, Coster, D, Croci, G, Cseh, G, Czarnecka, A, D'Arcangelo, O, Day, C, Delogu, R, de Marne, P, Denk, S, Denner, P, Dibon, M, D'Inca, R, Di Siena, A, Douai, D, Drenik, A, Drube, R, Dunne, M, Duval, B, Dux, R, Eich, T, Elgeti, S, Engelhardt, K, Erdos, B, Erofeev, I, Esposito, B, Fable, E, Faitsch, M, Fantz, U, Faugel, H, Felici, F, Fietz, S, Figueredo, A, Fischer, R, Ford, O, Frassinetti, L, Freethy, S, Froeschle, M, Fuchert, G, Fuchs, J, Fuenfgelder, H, Galazka, K, Galdon-Quiroga, J, Gallo, A, Gao, Y, Garavaglia, S, Garcia-Munoz, M, Geiger, B, Cianfarani, C, Giannone, L, Giovannozzi, E, Gleason-Gonzalez, C, Gloeggler, S, Gobbin, M, Goerler, T, Goodman, T, Gorini, G, Gradic, D, Graeter, A, Granucci, G, Greuner, H, Griener, M, Groth, M, Gude, A, Guenter, S, Guimarais, L, Haas, G, Hakola, A, Ham, C, Happel, T, Harrison, J, Hatch, D, Hauer, V, Hayward, T, Heinemann, B, Heinzel, S, Hellsten, T, Henderson, S, Hennequin, P, Herrmann, A, Heyn, E, Hitzler, F, Hobirk, J, Hoelzl, M, Hoeschen, T, Holm, J, Hopf, C, Hoppe, F, Horvath, L, Houben, A, Huber, A, Igochine, V, Ilkei, T, Ivanova-Stanik, I, Jacob, W, Jacobsen, A, Jacquot, J, Janky, F, Jardin, A, Jaulmes, F, Jenko, F, Jensen, T, Joffrin, E, Kasemann, C, Kalvin, S, Kantor, M, Kappatou, A, Kardaun, O, Karhunen, J, Kasilov, S, Kernbichler, W, Kim, D, Kimmig, S, Kirk, A, Klingshirn, H, Koch, F, Kocsis, G, Koehn, A, Kraus, M, Krieger, K, Krivska, A, Kraemer-Flecken, A, Kurki-Suonio, T, Kurzan, B, Lackner, K, Laggner, F, Lang, P, Lauber, P, Lazanyi, N, Lazaros, A, Lebschy, A, Li, L, Li, M, Liang, Y, Lipschultz, B, Liu, Y, Lohs, A, Luhmann, N, Lunt, T, Lyssoivan, A, Madsen, J, Maier, H, Maj, O, Mailloux, J, Maljaars, E, Manas, P, Mancini, A, Manhard, A, Manso, M, Mantica, P, Mantsinen, M, Manz, P, Maraschek, M, Martens, C, Martin, P, Marrelli, L, Martitsch, A, Mastrostefano, S, Mayer, A, Mayer, M, Mazon, D, McCarthy, P, McDermott, R, Meisl, G, Meister, H, Medvedeva, A, Merkel, P, Merkel, R, Merle, A, Mertens, V, Meshcheriakov, D, Meyer, H, Meyer, O, Miettunen, J, Milanesio, D, Mink, F, Mlynek, A, Monaco, F, Moon, C, Nazikian, R, Nemes-Czopf, A, Neu, G, Neu, R, Nielsen, A, Nielsen, S, Nikolaeva, V, Nocente, M, Noterdaeme, J, Nowak, S, Oberkofler, M, Oberparleiter, M, Ochoukov, R, Odstrcil, T, Olsen, J, Orain, F, Palermo, F, Papp, G, Paradela Perez, I, Pautasso, G, Enzel, F, Petersson, P, Pinzon, J, Piovesan, P, Piron, C, Plaum, B, Ploeckl, B, Plyusnin, V, Pokol, G, Poli, E, Porte, L, Potzel, S, Prisiazhniuk, D, Putterich, T, Ramisch, M, Rapson, C, Rasmussen, J, Raupp, G, Refy, D, Reich, M, Reimold, F, Ribeiro, T, Riedl, R, Rittich, D, Rocchi, G, Rodriguez-Ramos, M, Rohde, V, Ross, A, Rott, M, Rubel, M, Ryan, D, Ryter, F, Saarelma, S, Salewski, M, Salmi, A, Sanchis-Sanchez, L, Santos, G, Santos, J, Sauter, O, Scarabosio, A, Schall, G, Schmid, K, Schmitz, O, Schneider, P, Schneller, M, Schrittwieser, R, Schubert, M, Schwarz-Selinger, T, Schweinzer, J, Scott, B, Sehmer, T, Sertoli, M, Shabbir, A, Shalpegin, A, Shao, L, Sharapov, S, Siccinio, M, Sieglin, B, Sigalov, A, Silva, A, Silva, C, Simon, P, Simpson, J, Snicker, A, Sommariva, C, Sozzi, C, Spolaore, M, Stejner, M, Stober, J, Stobbe, F, Stroth, U, Strumberger, E, Suarez, G, Sugiyama, K, Sun, H, Suttrop, W, Szepesi, T, Tal, B, Tala, T, Tardini, G, Tardocchi, M, Terranova, D, Tierens, W, Told, D, Tudisco, O, Trevisan, G, Treutterer, W, Trier, E, Tripsky, M, Valisa, M, Valovic, M, Vanovac, B, Varela, P, Varoutis, S, Verdoolaege, G, Vezinet, D, Vianello, N, Vicente, J, Vierle, T, Viezzer, E, von Toussaint, U, Wagner, D, Wang, N, Wang, X, Weidl, M, Weiland, M, White, A, Willensdorfer, M, Wiringer, B, Wischmeier, M, Wolf, R, Wolfrum, E, Xiang, L, Yang, Q, Yang, Z, Yu, Q, Zagorski, R, Zammuto, I, Zarzoso, D, Zhang, W, van Zeeland, M, Zehetbauer, T, Zilker, M, Zoletnik2, S, Zohm, H, Adamek, J, Agostini, M, Ahn, J, Akers, R, Albanese, R, Aledda, R, Alessi, E, Allan, S, Alves, D, Ambrosino, R, Amicucci, L, Anand, H, Anastassiou, G, Andrebe, Y, Apruzzese, G, Ariola, M, Arnichand, H, Arter, W, Baciero, A, Barnes, M, Barrera, L, Behn, R, Bencze, A, Bettini, P, Bilkova, P, Bin, W, Bizarro, J, Blanchard, P, Blanken, T, Bluteau, M, Bogar, O, Bohm, P, Boncagni, L, Botrugno, A, Bouquey, F, Bourdelle, C, Bremond, S, Bufferand, H, Buratti, P, Cahyna, P, Calabro, G, Caniello, R, Canton, A, Cardinali, A, Carnevale, D, Castejon, F, Castro, R, Causa, F, Cavazzana, R, Cecconello, M, Ceccuzzi, S, Cesario, R, Chapman, S, Chernyshova, M, Choi, D, Ciraolo, G, Citrin, J, Corre, Y, Crisanti, F, Cruz, N, De Angeli, M, De Masi, G, De Temmerman, G, De Tommasi, G, Decker, J, Dendy, R, Di Troia, C, Dimitrova, M, Doric, V, Dudson, B, Dunai, D, Easy, L, Elmore, S, Fedorczak, N, Ferreira, J, Fevrier, O, Ficker, O, Figini, L, Figueiredo, A, Fil, A, Fishpool, G, Fitzgerald, M, Fontana, M, Fridstrom, R, Frigione, D, Fuchs, C, Furno Palumbo, M, Futatani, S, Gabellieri, L, Galeani, S, Gallart, D, Galperti, C, Garcia, J, Garcia-Carrasco, A, Garcia-Lopez, J, Gardarein, J, Garzotti, L, Gaspar, J, Gauthier, E, Geelen, P, Ghendrih, P, Ghezzi, F, Giacomelli, L, Giroud, C, Gleason Gonzalez, C, Gospodarczyk, M, Gruber, M, Guirlet, R, Gunn, J, Hacek, P, Hacquin, S, Hall, S, Harting, D, Havlickova, E, Helou, W, Heyn, M, Hnat, B, Holzl, M, Hogeweij, D, Honore, C, Horacek, J, Hornung, G, Huang, Z, Igitkhanov, J, Imrisek, M, Innocente, P, Ionita-Schrittwieser, C, Isliker, H, Jacquet, P, Jakubowski, M, Jeppe Miki Busk, O, Jessen, M, Jones, O, Jonsson, T, Kallinikos, N, Karpushov, A, Kasprowicz, G, Kendl, A, Kjer, S, Klimek, I, Kogut, D, Komm, M, Korsholm, S, Koslowski, H, Koubiti, M, Kovacic, J, Kovarik, K, Krawczyk, N, Krbec, J, Kube, R, Kudlacek, O, Labit, B, Laguardia, L, Lahtinen, A, Lalousis, P, Le, H, Leddy, J, Lefevre, L, Lehnen, M, Leipold, F, Lessig, A, Leyland, M, Loarer, T, Loarte, A, Loewenhoff, T, Lomanowski, B, Loschiavo, V, Lupelli, I, Lux, H, Maget, P, Maggi, C, Maggiora, R, Magnussen, M, Maljaars, B, Malygin, A, Marchand, B, Marconato, N, Marini, C, Marinucci, M, Markovic, T, Marocco, D, Martin Solis, J, Mattei, M, Matthews, G, Mavridis, M, Mayoral, M, McAdams, R, McArdle, G, McClements, K, McMillan, B, Militello, F, Miron, I, Mitosinkova, K, Mlynar, J, Molina, D, Molina, P, Monakhov, I, Morales, J, Moreau, D, Morel, P, Moret, J, Moro, A, Moulton, D, Mueller, H, Nabais, F, Nardon, E, Naulin, V, Nespoli, F, Nimb, S, Nouailletas, R, Omotani, J, O'Mullane, M, Osterman, N, Paccagnella, R, Pamela, S, Pangione, L, Panjan, M, Paprok, R, Parail, V, Parra, F, Pehkonen, S, Pereira, A, Perelli Cippo, E, Pericoli Ridolfini, V, Peterka, M, Petrzilka, V, Pironti, A, Pisano, F, Pisokas, T, Pitts, R, Ploumistakis, I, Poljak, D, Poloskei, P, Popovic, Z, Por, G, Predebon, I, Preynas, M, Primc, G, Pucella, G, Puiatti, M, Puetterich, T, Rack, M, Ramogida, G, Juul Rasmussen, J, Ratta, G, Ratynskaia, S, Ravera, G, Reimerdes, H, Reinke, M, Reiser, D, Resnik, M, Reux, C, Ripamonti, D, Riva, G, Rosato, J, Sabot, R, Saint-Laurent, F, Samaddar, D, Scannell, R, Scheffer, M, Schneider, M, Schneider, B, Seidl, J, Sesnic, S, Shanahan, B, Sheikh, U, Silva Fuglister, M, Spagnolo, S, Spizzo, G, Stange, T, Stejner Pedersen, M, Stepanov, I, Strand, P, Susnjara, A, Tamain, P, Teplukhina, A, Testa, D, Theiler, C, Thornton, A, Tolias, P, Tophoj, L, Tsironis, C, Tsui, C, Uccello, A, Urban, J, Vallejos, P, Vartanian, S, Vega, J, Verhaegh, K, Vermare, L, Vignitchouk, L, Vijvers, W, Villone, F, Viola, B, Vlahos, L, Voitsekhovitch, I, Vondracek, P, Vu, N, Walkden, N, Wauters, T, Weinzettl, V, Westerhof, E, Wiesenberger, M, Wodniak, I, Yadykin, D, Zanca, P, Zaplotnik, R, Zestanakis, P, Zuin, M, Litaudon, X, Abduallev, S, Abhangi, M, Abreu, P, Afzal, M, Aggarwal, K, Ahlgren, T, Aiba, N, Airila, M, Aldred, V, Alegre, D, Aleynikov, P, Alfier, A, Alkseev, A, Allinson, M, Alper, B, Alves, E, Ambrosino, G, Amosov, V, Andersson Sunden, E, Angelone, M, Anghel, M, Appel, L, Appelbee, C, Arena, P, Arshad, S, Ash, A, Ashikawa, N, Aslanyan, V, Auriemma, F, Austin, Y, Avotina, L, Axton, M, Ayres, C, Bacharis, M, Baiao, D, Bailey, S, Baker, A, Balboa, I, Balshaw, N, Bament, R, Banks, J, Baranov, Y, Barnard, M, Barnes, D, Barnsley, R, Baron Wiechec, A, Barrera Orte, L, Baruzzo, M, Basiuk, V, Bassan, M, Bastow, R, Batista, A, Batistoni, P, Baughan, R, Bauvir, B, Baylor, L, Bazylev, B, Beal, J, Beaumont, P, Beckers, M, Beckett, B, Becoulet, A, Bekris, N, Beldishevski, M, Bell, K, Belli, F, Bellinger, M, Belonohy, E, Ben Ayed, N, Benterman, N, Bergsaker, H, Berry, M, Bertalot, L, Besliu, C, Bieg, B, Bielecki, J, Biewer, T, Bigi, M, Binda, F, Bisoffi, A, Bjorkas, C, Blackburn, J, Blackman, K, Blackman, T, Blatchford, P, Boboc, A, Bodnar, G, Bolshakova, I, Bonanomi, N, Bonelli, F, Boom, J, Booth, J, Borba, D, Borodin, D, Borodkina, I, Boulting, P, Bowden, M, Bower, C, Bowman, C, Boyce, T, Boyd, C, Boyer, H, Bradshaw, J, Braic, V, Bravanec, R, Breizman, B, Brennan, P, Breton, S, Brett, A, Bright, M, Brix, M, Broeckx, W, Brombin, M, Broslawski, A, Brown, D, Brown, M, Bruno, E, Bucalossi, J, Buch, J, Buckley, M, Budny, R, Bulman, M, Bulmer, N, Bunting, P, Buscarino, A, Busse, A, Butler, N, Bykov, I, Byrne, J, Calvo, I, Camp, P, Campling, D, Cane, J, Capel, A, Card, P, Carman, P, Carraro, L, Carvalho, B, Carvalho, I, Casson, F, Catarino, N, Caumont, J, Cave-Ayland, K, Cavinato, M, Cecil, E, Cenedese, A, Chandler, M, Chandra, D, Chang, C, Chitarin, G, Ciric, D, Clark, E, Clark, M, Clarkson, R, Clatworthy, D, Clements, C, Cleverly, M, Coad, J, Coates, P, Cobalt, A, Coccorese, V, Cocilovo, V, Coffey, I, Collins, S, Conka, D, Conroy, S, Conway, N, Coombs, D, Cooper, D, Cooper, S, Corradino, C, Corrigan, G, Cortes, S, Couchman, A, Cox, M, Craciunescu, T, Cramp, S, Craven, R, Croft, D, Crombe, K, Crowe, R, Cufar, A, Cullen, A, Curuia, M, Dabirikhah, H, Dalgliesh, P, Dalley, S, Dankowski, J, Darrow, D, Davies, O, Davis, W, Day, I, De Bock, M, de Castro, A, de la Cal, E, de la Luna, E, de Pablos, J, de Vries, P, Deakin, K, Deane, J, Degli Agostini, F, Dejarnac, R, Delabie, E, den Harder, N, Denis, J, Devaux, S, Devynck, P, Di Maio, F, Dinca, P, Ding, B, Dittmar, T, Doerk, H, Doerner, R, Donne, T, Dorling, S, Dormido-Canto, S, Doswon, S, Doyle, P, Drewelow, P, Drews, P, Duckworth, P, Dumont, R, Dumortier, P, Duran, I, Durodie, F, Dutta, P, Dylst, K, Dzysiuk, N, Edappala, P, Edmond, J, Edwards, A, Edwards, J, Ekedahl, A, El-Jorf, R, Elsmore, C, Enachescu, M, Ericsson, G, Eriksson, F, Eriksson, J, Eriksson, L, Esquembri, S, Esser, H, Esteve, D, Evans, B, Evans, G, Evison, G, Ewart, G, Fagan, D, Falie, D, Fasoli, A, Faustin, J, Fawlk, N, Fazendeiro, L, Felton, R, Fenton, K, Fernades, A, Fernandes, H, Fessey, J, Field, A, Figueiredo, J, Finburg, P, Firdaouss, M, Fischer, U, Fittill, L, Flammini, D, Flanagan, J, Fleming, C, Flinders, K, Fonnesu, N, Fontdecaba, J, Formisano, A, Forsythe, L, Fortuna, L, Fortuna-Zalesna, E, Fortune, M, Foster, S, Franke, T, Franklin, T, Frasca, M, Freisinger, M, Fresa, R, Fuchs, V, Fuller, D, Fyvie, J, Gal, K, Galassi, D, Gallagher, J, Galvao, R, Gao, X, Gaudio, P, Gear, D, Gee, S, Gelfusa, M, Gerasimov, S, Gervasini, G, Gethins, M, Ghani, Z, Ghate, M, Gherendi, M, Giacalone, J, Gibson, C, Giegerich, T, Gil, C, Gil, L, Gilligan, S, Gin, D, Girardo, J, Giruzzi, G, Godwin, J, Goff, J, Gohil, P, Goloborod'ko, V, Gomes, R, Goncalves, B, Goniche, M, Goodliffe, M, Goodyear, A, Gosk, M, Goulding, R, Goussarov, A, Gowland, R, Graham, B, Graham, M, Graves, J, Grazier, N, Grazier, P, Green, N, Grierson, B, Griph, F, Grisolia, C, Grist, D, Grove, R, Grundy, C, Grzonka, J, Guard, D, Guerard, C, Guillemaut, C, Gurl, C, Utoh, H, Hackett, L, Hagar, A, Hager, R, Halitovs, M, Hallworth Cook, S, Hamlyn-Harris, C, Hammond, K, Harrington, C, Hasenbeck, F, Hatano, Y, Haupt, T, Hawes, J, Hawkes, N, Hawkins, J, Hawkins, P, Haydon, P, Hayter, N, Hazel, S, Heesterman, P, Heinola, K, Hellesen, C, Hemming, O, Hender, T, Henderson, M, Henriques, R, Hepple, D, Hermon, G, Hertout, P, Hidalgo, C, Highcock, E, Hill, M, Hillairet, J, Hillesheim, J, Hillis, D, Hizanidis, K, Hjalmarsson, A, Hodille, E, Hogben, C, Hogeweij, G, Hollingsworth, A, Hollis, S, Homfray, D, Horton, A, Horton, L, Hotchin, S, Hough, M, Howarth, P, Hubbard, A, Huber, V, Huddleston, T, Hughes, M, Huijsmans, G, Hunter, C, Huynh, P, Hynes, A, Iglesias, D, Imazawa, N, Imbeaux, F, Incelli, M, Irishkin, M, Jachmich, S, Jansons, J, Jarvinen, A, Jednorog, S, Jenkins, I, Jeong, C, Jepu, I, Johnson, R, Johnson, T, Johnston, J, Joita, L, Jones, G, Jones, T, Hoshino, K, Kamiya, K, Kaniewski, J, Kantor, A, Karkinsky, D, Karnowska, I, Kaufman, M, Kaveney, G, Kazakov, Y, Kazantzidis, V, Keeling, D, Keenan, T, Keep, J, Kempenaars, M, Kennedy, C, Kenny, D, Kent, J, Kent, O, Khilkevich, E, Kim, H, Kinch, A, King, C, King, D, King, R, Kinna, D, Kiptily, V, Kirov, K, Kirschner, A, Kizane, G, Klepper, C, Klix, A, Knight, P, Knipe, S, Knott, S, Kobuchi, T, Koechl, F, Kodeli, I, Kogan, L, Koivuranta, S, Kominis, Y, Koeppen, M, Kos, B, Koskela, T, Kovari, M, Kowalska-Strzeciwilk, E, Krasilnikov, A, Krasilnikov, V, Kresina, M, Kruezi, U, Ksiazek, I, Kukushkin, A, Kundu, A, Kwak, S, Kwiatkowski, R, Kwon, O, Laing, A, Lam, N, Lambertz, H, Lane, C, Lanthaler, S, Lapins, J, Lasa, A, Last, J, Laszynska, E, Lawless, R, Lawson, A, Lawson, K, Lazzaro, E, Lee, S, Lefebvre, X, Leggate, H, Lehmann, J, Leichtle, D, Leichuer, P, Lengar, I, Lennholm, M, Lerche, E, Lescinskis, A, Lesnoj, S, Letellier, E, Leysen, W, Likonen, J, Linke, J, Linsmeier, C, Liu, G, Lo Schiavo, V, Lobel, R, Lomas, P, Lonnroth, J, Lopez, J, Lopez-Razola, J, Lorenzini, R, Losada, U, Lovell, J, Loving, A, Lowry, C, Luce, T, Lucock, R, Lukin, A, Luna, C, Lungaroni, M, Lungu, C, Lungu, M, Lunniss, A, Macdonald, N, Macheta, P, Maczewa, K, Magesh, B, Makkonen, T, Makwana, R, Malaquias, A, Malizia, A, Manning, A, Manzanares, A, Maquet, P, Marandet, Y, Marcenko, N, Marchetto, C, Marchuk, O, Marinelli, M, Marot, L, Marren, C, Marshal, R, Martin de Aguilera, A, Martinez, F, Martin-Solis, J, Martynova, Y, Maruyama, S, Masiello, A, Maslov, M, Matejcik, S, Maviglia, F, May-Smith, T, Mazzotta, C, McCormack, O, McCullen, P, McDonald, D, McIntosh, S, McKean, R, McKehon, J, Meadows, R, Meakins, A, Medina, F, Medland, M, Medley, S, Meigh, S, Meigs, A, Meitner, S, Meneses, L, Menmuir, S, Mergia, K, Merrigan, I, Mertens, P, Meshchaninov, S, Messiaen, A, Mianowski, S, Michling, R, Middleton-Gear, D, Militello-Asp, E, Miloshevsky, G, Minucci, S, Miyoshi, Y, Moneti, M, Mooney, R, Moradi, S, Mordijck, S, Moreira, L, Moreno, R, Moro, F, Morris, A, Morris, J, Moser, L, Mosher, S, Muraro, A, Murphy, S, Asakura, N, Na, Y, Naish, R, Nakano, T, Nave, M, Nedzelski, I, Nemtsev, G, Neto, A, Neverov, V, Newman, M, Nicholls, K, Nicolas, T, Nielsen, P, Nilsson, E, Nishijima, D, Noble, C, Nodwell, D, Nordlund, K, Nordman, H, Nunes, I, Odupitan, T, Ogawa, M, O'Gorman, T, Okabayashi, M, Olney, R, Omolayo, O, Ongena, J, Orsitto, F, Orszagh, J, Oswuigwe, B, Otin, R, Owen, A, Pace, N, Pacella, D, Packer, L, Page, A, Pajuste, E, Palazzo, S, Panja, S, Papp, P, Park, M, Parra Diaz, F, Parsons, M, Pasqualotto, R, Patel, A, Pathak, S, Paton, D, Patten, H, Pawelec, E, Paz Soldan, C, Peackoc, A, Pearson, I, Peluso, E, Penot, C, Pereira, R, Pereira Puglia, P, Perez von Thun, C, Peruzzo, S, Peschanyi, S, Petravich, G, Petre, A, Petrella, N, Peysson, Y, Pfefferle, D, Philipps, V, Pillon, M, Pintsuk, G, Pires dos Reis, A, Piron, L, Pizzo, F, Pomaro, N, Pompilian, O, Pool, P, Popovichev, S, Porfiri, M, Porosnicu, C, Porton, M, Possnert, G, Powell, T, Pozzi, J, Prajapati, V, Prakash, R, Prestopino, G, Price, D, Price, M, Price, R, Prior, P, Proudfoot, R, Puglia, P, Pulley, D, Purahoo, K, Rachlew, E, Ragona, R, Rainford, M, Rakha, A, Ranjan, S, Rathod, K, Rayner, C, Rebai, M, Reece, D, Reed, A, Regan, B, Regana, J, Reid, N, Reinhart, M, Rendell, D, Reyes Cortes, S, Reynolds, S, Riccardo, V, Richardson, N, Riddle, K, Rigamonti, D, Rimini, F, Risner, J, Riva, M, Roach, C, Robins, R, Robinson, S, Robinson, T, Robson, D, Roccella, R, Rodionov, R, Rodrigues, P, Rodriguez, J, Romanelli, F, Romanelli, M, Romanelli, S, Romazanov, J, Rowe, S, Rubinacci, G, Rubino, G, Ruchko, L, Ruiz, M, Ruset, C, Rzadkiewicz, J, Safi, E, Sagar, P, Saibene, G, Salmon, R, Salzedas, F, Samm, U, Sandiford, D, Santa, P, Santala, M, Santos, B, Santucci, A, Sartori, F, Sartori, R, Schlummer, T, Schmidt, V, Schmuck, S, Schoepf, K, Schworer, D, Scott, S, Sergienko, G, Shaw, A, Shaw, R, Sheikh, H, Shepherd, A, Shevelev, A, Shumack, A, Sibbald, M, Silburn, S, Simmons, P, Simpson-Hutchinson, J, Sinha, A, Sipila, S, Sips, A, Siren, P, Sirinelli, A, Sjostrand, H, Skiba, M, Skilton, R, Slabkowska, K, Slade, B, Smith, N, Smith, P, Smith, R, Smith, T, Smithies, M, Snoj, L, Soare, S, Solano, E, Somers, A, Sonato, P, Sopplesa, A, Sousa, J, Spelzini, T, Spineanu, F, Stables, G, Stamatelatos, I, Stamp, M, Staniec, P, Stankunas, G, Stan-Sion, C, Stead, M, Stefanikova, E, Stephen, A, Stephen, M, Stevens, A, Stevens, B, Strachan, J, Strauss, H, Strom, P, Stubbs, G, Studholme, W, Subba, F, Summers, H, Svensson, J, Swiderski, L, Szabolics, T, Szawlowski, M, Szepesi, G, Suzuki, T, Talbot, A, Talebzadeh, S, Taliercio, C, Tame, C, Tang, W, Taroni, L, Taylor, D, Taylor, K, Tegnered, D, Telesca, G, Teplova, N, Tholerus, E, Thomas, J, Thomas, P, Thompson, A, Thompson, C, Thompson, V, Thorne, L, Thrysoe, A, Tigwell, P, Tipton, N, Tiseanu, I, Tojo, H, Tokitani, M, Tomes, M, Tonner, P, Towndrow, M, Trimble, P, Tsalas, M, Tsavalas, P, Tskhakaya Jun, D, Turner, I, Turner, M, Turnyanskiy, M, Tvalashvili, G, Tyrrell, S, Ul-Abidin, Z, Uljanovs, J, Ulyatt, D, Urano, H, Uytdenhouwen, I, Vadgama, A, Valcarcel, D, Valentinuzzi, M, Vallejos Olivares, P, Van De Mortel, M, Van Eester, D, Van Renterghem, W, van Rooij, G, Varje, J, Vasava, K, Vasilopoulou, T, Verhoeven, R, Verona, C, Verona Rinati, G, Veshchev, E, Villari, S, Vincenzi, P, Vinyar, I, Vitins, A, Vizvary, Z, Vlad, M, Vora, N, Vu, T, Pires de Sa, W, Wakeling, B, Waldon, C, Walker, M, Walker, R, Walsh, M, Wang, E, Warder, S, Warren, R, Waterhouse, J, Watkins, N, Watts, C, Weckmann, A, Weiland, J, Weisen, H, Weiszflog, M, Wellstood, C, West, A, Wheatley, M, Whetham, S, Whitehead, A, Whitehead, B, Widdowson, A, Wiesen, S, Wilkinson, J, Williams, J, Williams, M, Wilson, A, Wilson, D, Wilson, H, Wilson, J, Withenshaw, G, Withycombe, A, Witts, D, Wood, D, Wood, R, Woodley, C, Wray, S, Wright, J, Wu, J, Wukitch, S, Wynn, A, Xu, T, Yadikin, D, Yanling, W, Yao, L, Yavorskij, V, Yoo, M, Young, C, Young, D, Young, I, Young, R, Zacks, J, Zaitsev, F, Zanino, R, Zarins, A, Zastrow, K, Zerbini, M, Zhou, Y, Zilli, E, Zoita, V, and Zychor, I
Abstract: The aim of this paper is to present a signal processing algorithm that, applied to the raw Locked Mode signal, allows us to obtain a disruption indicator in principle exploitable on different tokamaks. A common definition of such an indicator for different machines would facilitate the development of portable systems for disruption prediction, which is becoming of increasingly importance for the next tokamak generations. Moreover, the indicator allows us to overcome some intrinsic problems in the diagnostic system such as drift and offset. The behavior of the proposed indicator as disruption predictor, based on crossing optimized thresholds of the signal amplitude, has been analyzed using data of both JET and ASDEX Upgrade experiments. A thorough analysis of the disruption prediction performance shows how the indicator is able to recover some missed and tardy detections of the raw signal. Moreover, it intervenes and corrects premature or even wrong alarms due to, e.g., drifts and/or offsets.
Published: 2019

46. Adaptive Learning for Disruption Prediction

Author: GELFUSA Michela, MURARI Andrea, LUNGARONI Michele, PELUSO Emmanuele, GAUDIO Pasqualino, VEGA Jesus, and DORMIDO-CANTO Sebastian
Subjects: Tokamak, Disruption Prediction
Abstract: Accurate prediction of catastrophic events is becoming an important area of investigation in many research fields. In Tokamaks, detecting disruptions with sufficient anticipation time is a prerequisite to undertaking any remedial strategy, either for mitigation or for avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but tend to age very quickly. Such a weakness is a consequence of the i.i.d. (independent an identically distributed) assumption on which they are based, which means that the input data are independent and are sampled from exactly the same probability distribution for the training set, the test set and the final actual discharges. These hypotheses are certainly not verified in practice, since nowadays the experimental programmes of fusion devices evolve quite rapidly and metallic machines are very sensitive to small changes in the plasma conditions. This paper describes various adaptive training strategies that have been develop to preserve the performance of disruption predictors in non-stationary conditions. The proposed techniques are based new ensembles of classifiers, belonging to the CART (Classification and Regression Trees) family. The improvements in performance are remarkable and the final predictors satisfy the requirements of the next generation of experimental devices.
Published: 2018

47. A database for developing machine learning based disruption predictors.

Author: Zhang, Ming, Wu, Qiqi, Zheng, Wei, Shang, Yangxing, and Wang, Yuxing
Subjects: *MACHINE learning, *NONRELATIONAL databases, *TOKAMAKS, *SIGNAL processing, *DATABASES, *DATA structures
Abstract: • A database built with disruption related information. • This database makes it easy to develop disruption predictors. • The disruption database can be applied to different Tokamak devices. • The process of generating disruption related information can be automated. Machine learning based disruption prediction methods have exhibited good prediction performance with higher success rate, lower false alarm rate and earlier warning time than physical based methods. One important thing pushed recent advances in machine learning field is high-quality training data. So a database with rich set of accurate disruption related information is crucial to the development of a high performance disruption predictor. In order to develop machine learning based disruption predictors fast and iteratively on J-TEXT, a database dedicated for machine learning disruption prediction has been built. This database provides not only disruption related labels, interfaces for querying the data based on user proved filter and auto generation of training and test data, but also interface for benchmark the predictors. Its modular design allows us to plug-in various shot analysis modules which process diagnostic signals and generate different labels automatically. These modules can be scheduled and run parallel on a cluster which will speed up the shot analysis process. The generated labels are inserted into a MongoDB NoSQL database for later querying. But one major hurdle for machine learning disruption prediction is they perform un-acceptably poor on devices other than they are trained on. It requires data from many different tokamaks to possibly develop a cross machine predictor, so this database also has a data import module which reads diagnostic data from different data sources such as MDSplus and store them as HDF5 files with unified data structure on a parallel file system. It is easy to import data from different machines and provide a unified data access interface. [ABSTRACT FROM AUTHOR]
Published: 2020
Full Text: View/download PDF

48. Overview of the JET results in support to ITER

Author: Alfredo Pironti, J. Simpson-Hutchinson, Sean Conroy, J. Uljanovs, D. Middleton-Gear, G. Possnert, C. Angioni, R. McAdams, Nicholas Watkins, E. Fortuna-Zalesna, A. Garcia-Carrasco, K. Gałązka, D. Nodwell, Pasquale Gaudio, R.A. Pitts, Svetlana V. Ratynskaia, Seppo Koivuranta, O. J. Kwon, C. Boyd, A. Boboc, M. Reinhart, Igor Lengar, Jarrod Leddy, Hiroyasu Utoh, J. H. Ahn, A. Stevens, J. Lönnroth, U. Kruezi, C. Guillemaut, N. Fonnesu, W. Studholme, Marek Rubel, P. Cahyna, O. McCormack, A. S. Jacobsen, D. Mazon, Gunta Kizane, N. Ashikawa, William Tang, J. Goff, F. Nespoli, Thomas Giegerich, G. Petravich, Angela Busse, Corneliu Porosnicu, M. Bigi, M. Wheatley, Christopher N. Bowman, J. Zacks, Ivan Calvo, U. Losada, H. Weisen, B. Bauvir, Stanislas Pamela, Sylvain Brémond, M.F. Stamp, Scott W. McIntosh, A. Rakha, S. Glöggler, V. Braic, C. Bottereau, S. Murphy, S. Knott, Luigi Fortuna, P. Bunting, N. Vora, S. D. Scott, A. Lazaros, R. Dejarnac, P. Buratti, H.R. Strauss, Gabriele Croci, M. Nocente, A. Hollingsworth, S. Reynolds, D. J. Wilson, D. D. Brown, T.C. Luce, S. Zoletnik, E. Nilsson, L. Laguardia, O. Marchuk, F.P. Orsitto, E. Cecil, V. Huber, J. B. Girardo, Stylianos Varoutis, M. D. Axton, Hyun-Tae Kim, E. Safi, Ch. Day, S. Arshad, J. Rzadkiewicz, P. Prior, A. Meigs, S. Esquembri, P. Gohil, K. Purahoo, Torbjörn Hellsten, N. Tipton, R. Guirlet, E. Joffrin, V. Aldred, Calin Besliu, M. Valentinuzzi, G. T. Jones, J. Edwards, Giuseppe Ambrosino, Laurent Marot, N. Lam, F. Crisanti, G. Verona Rinati, R. Marshal, Michael L. Brown, D. Frigione, D. Chandra, Michaele Freisinger, R. Olney, Jari Varje, S. Whetham, F. Parra Diaz, M. R. Hough, P. Dinca, F. Salzedas, A. Goodyear, R. Gowland, J. A. Wilson, J. Horacek, D. King, K. Flinders, I. R. Merrigan, M. Ghate, R. Michling, F. Saint-Laurent, G. Kocsis, D. Van Eester, C. Young, R. O. Dendy, A. Meakins, N. Pace, C. L. Hunter, D. Alegre, S. Foster, V. Riccardo, M. Bulman, C. Jeong, Marek Szawlowski, B. D. Whitehead, Vasily Kiptily, James Harrison, Hiroshi Tojo, G. T. A. Huijsmans, J. W. Coenen, X. Litaudon, Justin Williams, C. Hidalgo, S. Lesnoj, I.E. Day, A. W. Morris, R. Mooney, Yann Corre, S. Brezinsek, B. Gonçalves, M. Kresina, D. Coombs, F. Köchl, J. L. Gardarein, W. Davis, Aqsa Shabbir, Kanti M. Aggarwal, L. Colas, A. B. Kukushkin, Seppo Sipilä, Elisabeth Rachlew, Leena Aho-Mantila, O. G. Pompilian, E. Viezzer, Shane Cooper, Fabio Villone, P. Blanchard, Patrick Tamain, P. Camp, T. Szabolics, C. Luna, Kalle Heinola, H. G. Esser, V. Bobkov, James Buchanan, Andrew West, Hajime Urano, Roberta Lima Gomes, J.P. Coad, Th. Pütterich, A. Sinha, S. Hollis, R. D. Wood, G. D. Ewart, F. S. Griph, T. Kobuchi, X. Lefebvre, S. Warder, A.J. Thornton, S. Peschanyi, B. Graham, Giuseppe Telesca, M. Kempenaars, J. Bernardo, M. Hughes, Eva Belonohy, S. Schmuck, Kai Nordlund, T. J. Smith, P. Hertout, K. D. Lawson, M. Brix, Matthew Sibbald, Grégoire Hornung, C. Tame, Matthew Carr, S. Wray, P. T. Doyle, A. Somers, Giuseppe Chitarin, D. C. Campling, Mitul Abhangi, I. Jepu, David A. Wood, J. Miettunen, A. Sopplesa, Raffaele Fresa, S. Saarelma, M. Bacharis, J. Pozzi, P. Vallejos Olivares, Teddy Craciunescu, Raffaele Albanese, S. Knipe, Jason P. Byrne, A. C. C. Sips, S. Hazel, V. Kazantzidis, G. Stankūnas, A. Kundu, J. Mailloux, C. Guerard, Pramit Dutta, J. E. Boom, Eduardo Alves, P. Grazier, Saskia Mordijck, V.S. Neverov, Kazuo Hoshino, A. P. Vadgama, P. D. Brennan, P. Innocente, Piergiorgio Sonato, M. Irishkin, M. Berry, D. W. Robson, Dieter Leichtle, Fabio Pisano, P. McCullen, T. M. Huddleston, Kensaku Kamiya, D. Pacella, Tommy Ahlgren, A. Kirschner, B. Magesh, A. Ash, J. Mlynář, C. Castaldo, C. Marchetto, D. L. Hillis, M. Incelli, B. Viola, R. J. Robins, E. Andersson Sundén, G. Ramogida, Matthew Reinke, Gerd Meisl, Yannis Kominis, R. Proudfoot, C. Noble, N. J. Conway, V. P. Lo Schiavo, Jorge Luis Rodriguez, Hugo Bufferand, C. H. A. Hogben, B. Evans, R. Sartori, H. Greuner, M. G. Dunne, K. Schöpf, M. I. K. Santala, E. Giovannozzi, A. E. Shevelev, C. Gil, P. Boulting, P. Sagar, A.E. Shumack, P. A. Coates, C. Ayres, R. Prakash, C. Giroud, M. Parsons, J. C. Giacalone, S. Meshchaninov, A. Peackoc, G. De Temmerman, A.C.A. Figueiredo, D. Gallart, P. Santa, Sergey Popovichev, Ivan Lupelli, M. Valovic, Thomas Johnson, Y. Martynova, M. Rack, Olivier Sauter, J. Garcia, P. Siren, I. Balboa, S. Lee, Hans Nordman, R. Roccella, M. Faitsch, Julien Hillairet, Patrick J. McCarthy, C. Reux, Irena Ivanova-Stanik, V. Coccorese, Ye. O. Kazakov, R. El-Jorf, C. Hamlyn-Harris, Matthias Weiszflog, C. F. Maggi, Panagiotis Tolias, N. C. Hawkes, E. Clark, Bruno Santos, B. Sieglin, R. Rodionov, Roch Kwiatkowski, P. Denner, C. Woodley, Hugh Summers, Francesco Pizzo, G. Pucella, D. Croft, F. Di Maio, M. Tomes, D. Molina, A. Fernades, L. Amicucci, Marco Cecconello, A. Bisoffi, Z. Ul-Abidin, J. Wilkinson, H. Maier, S. Rowe, M. Beckers, P.J. Knight, E. Pajuste, Choong-Seock Chang, K. Deakin, M. Enachescu, A. Cobalt, D. Tskhakaya Jun, Michela Gelfusa, Rémy Nouailletas, R. Ragona, N. Bonanomi, D. A. Homfray, K. Riddle, Yann Camenen, J. D. Thomas, R.P. Doerner, Timothy P. Robinson, Y. Miyoshi, Ph. Jacquet, H. T. Lambertz, D. Pulley, A. Bécoulet, E. Tholerus, O. Bogar, M. Peterka, R. Crowe, C. Sommariva, A. R. Talbot, N. K. Butler, N. Reid, R. Zagórski, Gerald Pintsuk, Juri Romazanov, Andre Neto, G. L. Ravera, Paolo Arena, A. Manning, F. Durodié, Maryna Chernyshova, D. Karkinsky, Štefan Matejčík, J. P. Thomas, A. Wilson, L. Joita, R. Naish, P. Strand, M. Balden, M. Kaufman, T. Powell, V. Schmidt, D. Barnes, José Vicente, S. Doswon, Daniel F. Valcarcel, Claudia Corradino, R. Warren, Annette M. Hynes, J. D. Strachan, A. M. Messiaen, M. Kovari, O. Omolayo, D. M. Witts, R. C. Felton, C. Fleming, C. A. Marren, Patrick Maget, J. Galdon-Quiroga, H. R. Koslowski, Bruce Lipschultz, Ana Elisa Bauer de Camargo Silva, J. Waterhouse, R. J. Dumont, M. Schneider, Sara Moradi, K. J. Nicholls, M. Beldishevski, Benedikt Geiger, A. Jardin, A. Ekedahl, A. Lyssoivan, C. Waldon, Davide Galassi, F. Jaulmes, A. Kirk, Yannick Marandet, F. Hasenbeck, Gabor Szepesi, R. C. Pereira, J. Juul Rasmussen, Nobuyuki Aiba, Michelle E. Walker, Gábor Cseh, Scott W. Mosher, R. Bastow, A. Di Siena, E. Lazzaro, M. Curuia, C. D. Challis, Z. Ghani, J. Deane, João M. C. Sousa, Henrik Sjöstrand, T. O'Gorman, H. R. Wilson, P. Devynck, M. Price, C. A. Thompson, Daniele Marocco, A. Cullen, M. Clark, M. Lennholm, D. Carralero, N. Balshaw, Roland Sabot, I. Stepanov, N. Petrella, Filippo Sartori, L. W. Packer, P. Thomas, M. Lungu, A. V. Krasilnikov, R. Young, Jonathan Graves, J. C. Hillesheim, Mǎdǎlina Vlad, Duccio Testa, Pierre Dumortier, Paulo Carvalho, M. Gosk, Yong-Su Na, M. Buckley, Carlos A. Silva, V. Fuchs, K. Vasava, P. A. Tigwell, B. Wakeling, M. Medland, M. Bellinger, K. Gal, Petter Ström, E. Veshchev, F. Nabais, A. Wynn, L. Lauro Taroni, B. Beckett, L. Gil, M. Towndrow, Brian Grierson, Harry M. Meyer, V. Philipps, A. de Castro, D. Kinna, D. Conka, Göran Ericsson, L. Piron, J. Hawkins, D. Cooper, Kenneth Hammond, V.V. Parail, Cristian Ruset, G.J. van Rooij, M. N. A. Beurskens, N. Fawlk, G. Evison, M. Van De Mortel, N. Marcenko, B. Slade, Th. Franke, Simone Peruzzo, N. den Harder, D. Baião, A. Martin de Aguilera, Frederic Imbeaux, Carlo Sozzi, J.L. de Pablos, J. Svensson, A. Withycombe, Ane Lasa, H. Sheikh, V.A. Yavorskij, Nick Walkden, E. Lerche, C. S. Gibson, Roberto Zanino, Y. Peysson, David Hatch, B. Bazylev, E. de la Cal, S. Hacquin, T. D. V. Haupt, S. A. Silburn, T.T.C. Jones, Maria Teresa Porfiri, Walid Helou, S. E. Sharapov, M. Zerbini, Ken W Bell, Marco Marinelli, Kyriakos Hizanidis, J. M. Fontdecaba, N. Teplova, K. K. Kirov, S. Vartanian, W. W. Pires de Sa, T. C. Hender, J. K. Blackburn, I. Monakhov, H. Patten, P. A. Simmons, Y. Austin, J. Regana, Stefano Coda, Amanda J. Page, D. Fuller, António J.N. Batista, A. Horton, P. Heesterman, S. Cramp, J. Hobirk, F. Clairet, A. Burckhart, M. Allinson, Larry R. Baylor, W. Leysen, D. B. Gin, P. Nielsen, A. Kantor, Yueqiang Liu, A.V. Stephen, Jose Ramon Martin-Solis, P. Mantica, B. C. Regan, Aleksander Drenik, A. Lukin, L. Thorne, G. Nemtsev, J. Denis, M. E. Graham, D. Rigamonti, W. Van Renterghem, M. Tardocchi, M. Koubiti, A. Malaquias, M. Tsalas, A. Cufar, Giuseppe Prestopino, D. Kogut, N. Pomaro, J. Keep, Jochen Linke, Shimpei Futatani, Boris Breizman, A. Sirinelli, M. Chandler, M. Fortune, F. Degli Agostini, I. Jenkins, T. Spelzini, G. Calabrò, O. N. Kent, A. Lunniss, Etienne Hodille, Z. Vizvary, Volker Naulin, T. Eich, F. Mink, A. Alkseev, P. W. Haydon, Massimo Angelone, Norberto Catarino, J. Lapins, Roberto Pasqualotto, R. Lawless, T. Schlummer, F. Bonelli, M. Wischmeier, Stéphane Devaux, G. Saibene, Dirk Reiser, Y. R. Martin, H. Bergsåker, Jon Godwin, Alessia Santucci, C. Lane, Justyna Grzonka, Ph. Mertens, Claudio Verona, David Moulton, E. Delabie, Anna Salmi, P. G. Smith, T. Bolzonella, Silvio Ceccuzzi, Ulrich Fischer, G. Liu, M. A. Henderson, M. Marinucci, T. Suzuki, Jakub Bielecki, João Figueiredo, M. Afzal, J. Cane, Robert Hager, Luciano Bertalot, M. Firdaouss, G. Tvalashvili, D. Hepple, D. Esteve, M. De Bock, Y. Baranov, R. D'Inca, G. De Tommasi, Ch. Linsmeier, T. Nicolas, I. J. Pearson, P. Finburg, Ireneusz Książek, S. Talebzadeh, A. Czarnecka, A. Botrugno, M. Gethins, Bohdan Bieg, R. Baughan, I. Borodkina, B. Kos, A. Muraro, T. Vasilopoulou, G. Hermon, S.J. Wukitch, Jari Likonen, D. P. Coster, Guglielmo Rubinacci, I. H. Coffey, Justine M. Kent, S. E. Dorling, J. Dankowski, Geert Verdoolaege, Daisuke Nishijima, R. Clarkson, E. R. Solano, M. Stephen, A. Lescinskis, P. Staniec, Karl Schmid, M. Mayer, Peter Lang, T. Franklin, M.I. Williams, C. G. Elsmore, F. Maviglia, C. Di Troia, C. Penot, A. Zarins, Pierre Manas, D. F. Gear, Yu Gao, Philipp Drews, E. Letellier, A. S. Thompson, L. Forsythe, I. Zychor, E. Khilkevich, A. Manzanares, T. Nakano, Paulo Rodrigues, J. Edmond, Sebastián Dormido-Canto, R. Dux, C. Appelbee, L. Moser, Angelo Cenedese, D. Fagan, N. Richardson, Giuseppe Gorini, V. Rohde, R. Paprok, João P. S. Bizarro, P. Aleynikov, M. Sertoli, Ł. Świderski, Simone Palazzo, O. W. Davies, D. Douai, N. Macdonald, M. Baruzzo, J. López-Razola, M. Lungaroni, D. Clatworthy, R. Bravanec, J. Lovell, Ambrogio Fasoli, S.-P. Pehkonen, M. E. Puiatti, P. Papp, G. Bodnar, V. Aslanyan, A. Weckmann, K. A. Taylor, R. Henriques, I. T. Chapman, Ewa Pawelec, Miles M. Turner, Steven J. Meitner, M. Bernert, Ph. Maquet, R. C. Meadows, A. Shaw, N. Vianello, L. Barrera Orte, Tomas Markovic, A. Fil, A. S. Couchman, Inessa Bolshakova, J. Fyvie, Konstantina Mergia, J. Gallagher, R.V. Budny, Frank Leipold, C. J. Rapson, R. C. Lobel, Gennady V. Miloshevsky, K.-D. Zastrow, Ph. Duckworth, Gianluca Rubino, G. Withenshaw, S. Maruyama, S. P. Hallworth Cook, M. Newman, Jérôme Bucalossi, P. Drewelow, Nuno Cruz, D. Iglesias, I. Nedzelski, T. Donne, P. Leichuer, R. Cesario, M. D. J. Bright, T. Boyce, N. Imazawa, Per Petersson, R. King, A. Loving, L. Garzotti, Jorge Ferreira, G. Corrigan, D. Sandiford, B. Tal, P. Puglia, Daniel Tegnered, J. Karhunen, James S. Wright, Tom Wauters, J. McKehon, K. Rathod, Olivier Février, Alessandro Formisano, Petra Bilkova, M. Groth, Ricardo Magnus Osorio Galvao, F. Medina, S. Collins, H. J. Boyer, Elena Bruno, Horacio Fernandes, M. J. Stead, R. Paccagnella, J. Kaniewski, Ion E. Stamatelatos, F. Causa, M. F. F. Nave, A. Patel, D. C. McDonald, L. Moreira, Mariano Ruiz, K. Dylst, Raymond A. Shaw, A. Brett, Jane Johnston, P. P. Pereira Puglia, J. Ongena, N. A. Benterman, V. N. Amosov, Christian Grisolia, J. Simpson, C. Perez von Thun, Jan Weiland, P. Tonner, F. Belli, T. Odupitan, T. Dittmar, Edmund Highcock, Taina Kurki-Suonio, I. Uytdenhouwen, Estelle Gauthier, M. Oberkofler, B. Alper, Iris D. Young, S. Soare, Yuji Hatano, D. Reece, D. Borodin, M. Moneti, W. Yanling, S. Mianowski, K. Fenton, Stephen J. Bailey, R. Coelho, Sandra C. Chapman, E. Łaszyńska, A. R. Field, F.J. Martínez, Anders Nielsen, M. Smithies, M. J. Mantsinen, A. J. Capel, N. D. Smith, A. Pires dos Reis, M.-L. Mayoral, T. Loarer, P. Carman, N. Grazier, S. Breton, J. M. A. Bradshaw, Alexandre C. Pereira, Fulvio Auriemma, Fulvio Militello, Barbara Cannas, D. Ulyatt, A. Kappatou, P. Blatchford, R. Scannell, B. I. Oswuigwe, Darren Price, Robert E. Grove, D. Guard, M. Leyland, G. Stubbs, J. W. Banks, V.V. Plyusnin, M. S. J. Rainford, Andrea Murari, Sanjeev Ranjan, A. Huber, V. Krasilnikov, C. Bower, H. Leggate, S. Abduallev, P. Tsavalas, G. Giruzzi, K. Maczewa, Colin Roach, P. Beaumont, R. P. Johnson, Anna Widdowson, L. A. Kogan, A. Baron Wiechec, Markus Airila, J. Morris, Robert Skilton, Katarzyna Słabkowska, M. A. Barnard, Jean-Paul Booth, Alessandro Pau, R. Price, R. Bament, M. Tokitani, I. Turner, T. Vu, P. Huynh, S.N. Gerasimov, D. I. Refy, Yunfeng Liang, Anders Hjalmarsson, S. Dalley, Roberto Ambrosino, O. Hemming, T. R. Blackman, Y. Zhou, Vasile Zoita, P. Vincenzi, A. Loarte, C. Rayner, Martin Imrisek, M. Tripsky, C. Mazzotta, A. Uccello, V. Basiuk, Lide Yao, V. Goloborod'ko, S. Villari, B. P. Duval, N. Bulmer, W. Zhang, L. Hackett, D. N. Borba, M. Halitovs, Mario Pillon, H. Arnichand, Alberto Alfier, A. Lawson, A. Masiello, T. Makkonen, A. Vitins, D. Rendell, D. Paton, L. Avotina, A. Krivska, M. Maslov, Richard Verhoeven, Marc Goniche, A. Broslawski, Marica Rebai, E. de la Luna, E. Militello-Asp, V. Cocilovo, L. Carraro, Michael Fitzgerald, Bernardo B. Carvalho, D. Young, C.G. Lowry, F. J. Casson, L.-G. Eriksson, T. M. Biewer, B. Esposito, F.G. Rimini, J. Fessey, G. Kaveney, S. Hall, Robin Barnsley, Michael Lehnen, N. Bekris, L. F. Ruchko, P. Batistoni, E. Alessi, M. G. O'Mullane, D. S. Darrow, C. N. Grundy, N. Hayter, Ivo S. Carvalho, M. Brombin, Enrico Zilli, M. Valisa, M. Reich, S. Panja, C. Gurl, Charles Harrington, Emmanuele Peluso, M. Porton, Michael Walsh, D. Falie, A. Reed, Jacob Eriksson, P. Macheta, J. M. Faustin, S. Cortes, S. Fietz, P. Piovesan, D. Ciric, Eric Nardon, R. Neu, Bojiang Ding, G.A. Rattá, F. Reimold, R. Craven, M. Cox, J. Orszagh, Aaro Järvinen, A. S. Thrysøe, A. Shepherd, I. Ďuran, Andrew M. Edwards, A. Kinch, J. Beal, M. Gherendi, Martin Köppen, D. Samaddar, P. Dalgliesh, I. Vinyar, J. Jansons, Nengchao Wang, J. Wu, John Wright, S. Wiesen, C. King, Alessandra Fanni, L. D. Horton, N. Krawczyk, J. Buch, K. Krieger, Václav Petržílka, D. Schworer, C. Watts, T. Keenan, Andrea Malizia, B. D. Stevens, P. Trimble, C. P. Lungu, V. Prajapati, Marco Ariola, C. Wellstood, S. Gilligan, Mirko Salewski, Michael Barnes, Florin Spineanu, H. Doerk, C. Kennedy, S. Jachmich, J. Caumont, Isabel L. Nunes, A. Petre, A. Kallenbach, M. Anghel, B. Lomanowski, Marco Riva, M. Romanelli, G. De Masi, T. May-Smith, T. Xu, A. Goussarov, S. Romanelli, M. Okabayashi, A. Baker, R. Salmon, T. Tala, Nicolas Fedorczak, S. Lanthaler, Giuliana Sias, J. Risner, Clarisse Bourdelle, M. E. Manso, Fabio Moro, R. Lucock, M. Bassan, M. T. Ogawa, V. Thompson, A. M. Whitehead, S. D. A. Reyes Cortes, Igor Bykov, Gennady Sergienko, E. Stefanikova, Mattia Frasca, H. Dabirikhah, Lorenzo Frassinetti, N. Dzysiuk, D. L. Keeling, Juan Manuel López, M. Turnyanskiy, Daniel Dunai, David Taylor, Arturo Buscarino, Carolina Björkas, A. Baciero, S. Meigh, M. Garcia-Munoz, Massimiliano Mattei, M. Hill, Gwyndaf Evans, S. Minucci, Xiang Gao, A. V. Chankin, Francesco Romanelli, A. Lahtinen, L. Giacomelli, A. Owen, Jesús Vega, Jonathan Citrin, Antti Hakola, Petr Vondracek, Sehyun Kwak, P. Abreu, L. Meneses, S. S. Medley, G. Gervasini, Surya K. Pathak, Kristel Crombé, M. Cleverly, H.S. Kim, C. Stan-Sion, Nobuyuki Asakura, E. Wang, A. Cardinali, L. Fazendeiro, R. Cavazzana, P. J. Lomas, J. Hawes, G. Stables, Silvia Spagnolo, S. P. Hotchin, N. R. Green, Slawomir Jednorog, Ewa Kowalska-Strzęciwilk, A. Martin, Linwei Li, Rajnikant Makwana, Richard Goulding, I. Voitsekhovitch, M. Bowden, I. Kodeli, Peter Hawkins, S. S. Henderson, Ondrej Ficker, Carl Hellesen, D. Yadikin, Fabio Subba, Luka Snoj, Anthony Laing, N. Ben Ayed, Mario Cavinato, M. Goodliffe, C. Clements, D. Kenny, Axel Klix, S. Gee, R. J. E. Smith, P. de Vries, L. Fittill, Min-Gu Yoo, S. Menmuir, K. Cave-Ayland, S. Potzel, D. Grist, K. Blackman, S. A. Robinson, Rodney Walker, David Pfefferlé, W. Broeckx, D. Harting, S. G. J. Tyrrell, F. Binda, L. Horvath, Davide Flammini, P. V. Edappala, Raul Moreno, G. M. D. Hogeweij, P. Card, A. Hagar, Ion Tiseanu, Rita Lorenzini, L. Appel, Jet Contributors, J. Flanagan, C. Paz Soldan, U. Samm, Otto Asunta, F. Eriksson, C. Taliercio, F. S. Zaitsev, G. F. Matthews, Tuomas Koskela, P. J. Howarth, D. Terranova, M. Skiba, Amanda Hubbard, R. Otin, K. G. McClements, M. Park, R. McKean, C. Christopher Klepper, I. Karnowska, Peter J. Pool, G. Ciraolo, Jennifer M. Lehmann, Institut de Mécanique des Fluides et des Solides (IMFS), Université Louis Pasteur - Strasbourg I-Centre National de la Recherche Scientifique (CNRS), VTT Technical Research Centre of Finland (VTT), Association EURATOM-TEKES, Association EURATOM-TEKES, Helsinki University of Technology, Finland, Assoc. Euratom-ENEA-CREATE, Universita Mediterranea of Reggio Calabria [Reggio Calabria], EURATOM/CCFE Fusion Association, Culham Science Centre [Abingdon], Instituto Tecnológico e Nuclear (ITN), ITN, University of Naples Federico II = Università degli studi di Napoli Federico II, Max-Planck-Institut für Plasmaphysik [Garching] (IPP), Università degli studi di Catania = University of Catania (Unict), National Institute for Fusion Science (NIFS), Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), ITER organization (ITER), Karlsruhe Institute of Technology (KIT), Institut de Chimie des Substances Naturelles (ICSN), Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche sur la Fusion par confinement Magnétique (IRFM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), European Fusion Development Agreement [Garching bei München] ( EFDA-CSU), Institut d'ophtalmologie Hédi-Rais de Tunis, Service Cardiologie [CHU Toulouse], Pôle Cardiovasculaire et Métabolique [CHU Toulouse], Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), H. Niewodniczanski Institute of Nuclear Physics, Polska Akademia Nauk = Polish Academy of Sciences (PAN), Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique (LHEEA), École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Euratom/UKAEA Fusion Assoc., Magnetic Sensor laboratory [Lviv] (MSL), National Polytechnic University of Lviv (LPNU), The National Research Nuclear University MEPhI (Moscow Engineering Physics Institute) [Moscow, Russia], Institute of Energy and Climate Research - Plasma Physics (IEK-4), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association, Institute for Problems of Material Science, National Academy of Sciences of Ukraine (NASU), Institute of Plasma Physics [Praha], Czech Academy of Sciences [Prague] (CAS), Physique des interactions ioniques et moléculaires (PIIM), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Département Méthodes et Modèles Mathématiques pour l'Industrie (3MI-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre G2I, Department of Hydraulics, Transportations and Roads, Laboratoire de microbiologie et génétique moléculaires - UMR5100 (LMGM), Centre de Biologie Intégrative (CBI), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Metallurgical & Materials Engineering Department (MS 388), University of Nevada [Reno], AUTRES, Institute of Plasma Physics and Laser Microfusion [Warsaw] (IPPLM), Culham Centre for Fusion Energy (CCFE), Astrophysics Research Centre [Belfast] (ARC), Queen's University [Belfast] (QUB), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), School of Mathematics [Cardiff], Cardiff University, Associazone EURATOM ENEA sulla Fusione, EURATOM, Laboratoire de physique des plasmas de l'ERM, Laboratorium voor plasmafysica van de KMS (LPP ERM KMS), Ecole Royale Militaire / Koninklijke Militaire School (ERM KMS), Paul-Drude-Institut für Festkörperelektronik (PDI), Institut für Physik, University of Basel (Unibas), Dutch Institute for Fundamental Energy Research [Nieuwegein] (DIFFER), Dutch Institute for Fundamental Energy Research [Eindhoven] (DIFFER), Institut Jean Lamour (IJL), Institut de Chimie du CNRS (INC)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), CEA Cadarache, Dipartimento di Energia [Milano], Politecnico di Milano [Milan] (POLIMI), Laboratoire de Mécanique, Modélisation et Procédés Propres (M2P2), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Lille économie management - UMR 9221 (LEM), Université d'Artois (UA)-Université catholique de Lille (UCL)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Euratom research and training programme 633053, Institut de Mécanique des Fluides et des Solides ( IMFS ), Université Louis Pasteur - Strasbourg I-Centre National de la Recherche Scientifique ( CNRS ), VTT Technical Research Centre of Finland ( VTT ), Univ. Mediterranea RC, Culham Science Centre, Instituto Tecnológico e Nuclear ( ITN ), Università degli studi di Napoli Federico II, Max-Planck-Institut für Plasmaphysik [Garching] ( IPP ), Università degli studi di Catania [Catania], National Institute for Fusion Science, National Institutes of Natural Sciences, Laboratoire de Physique Nucléaire et de Hautes Énergies ( LPNHE ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), School of Geography, Earth and Environmental Sciences, ITER Organization, Karlsruhe Institute of Technology ( KIT ), Laboratoire de Nanotechnologie et d'Instrumentation Optique ( LNIO ), Institut Charles Delaunay ( ICD ), Université de Technologie de Troyes ( UTT ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Technologie de Troyes ( UTT ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Chimie des Substances Naturelles ( ICSN ), Centre National de la Recherche Scientifique ( CNRS ), Institut de Recherche sur la Fusion par confinement Magnétique ( IRFM ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), European Fusion Development Agreement [Garching bei München] ( EFDA-CSU ), Service de cardiologie [Toulouse], Université Paul Sabatier - Toulouse 3 ( UPS ) -CHU Toulouse [Toulouse]-Hôpital de Rangueil, ITER [St. Paul-lez-Durance], ITER, Polska Akademia Nauk ( PAN ), Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique ( LHEEA ), École Centrale de Nantes ( ECN ) -Centre National de la Recherche Scientifique ( CNRS ), MSL, Lviv Polytechnic National University ( MSL ), Lviv Polytechnic National University, Centre d'études et de recherches appliquées à la gestion ( CERAG ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Centre National de la Recherche Scientifique ( CNRS ), Institute of Energy and Climate Research - Plasma Physics ( IEK-4 ), Forschungszentrum Jülich GmbH, National Academy of Sciences of Ukraine ( NASU ), Lille - Economie et Management ( LEM ), Université catholique de Lille ( UCL ) -Université de Lille-Centre National de la Recherche Scientifique ( CNRS ), Czech Academy of Sciences [Prague] ( ASCR ), Physique des interactions ioniques et moléculaires ( PIIM ), Aix Marseille Université ( AMU ) -Centre National de la Recherche Scientifique ( CNRS ), Département Méthodes et Modèles Mathématiques pour l'Industrie ( 3MI-ENSMSE ), École des Mines de Saint-Étienne ( Mines Saint-Étienne MSE ), Institut Mines-Télécom [Paris]-Institut Mines-Télécom [Paris]-Centre G2I, Laboratoire de microbiologie et génétique moléculaires ( LMGM ), Université Paul Sabatier - Toulouse 3 ( UPS ) -Centre National de la Recherche Scientifique ( CNRS ), University of Nevada, Institute of Plasma Physics and Laser Microfusion [Warsaw] ( IPPLM ), UCL Department of Space and Climate Physics, University College of London [London] ( UCL ), Astrophysics Research Centre [Belfast] ( ARC ), Queen's University [Belfast] ( QUB ), Laboratoire d'Electronique et des Technologies de l'Information ( CEA-LETI ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Grenoble Alpes [Saint Martin d'Hères], Cardiff School of Mathematics, Laboratoire de physique des plasmas de l'ERM, Laboratorium voor plasmafysica van de KMS ( LPP ERM KMS ), Ecole Royale Militaire / Koninklijke Militaire School ( ERM KMS ), Paul-Drude-Institut für Festkörperelektronik, University of Basel ( Unibas ), Dutch Institute for Fundamental Energy Research [Nieuwegein] ( DIFFER ), Dutch Institute for Fundamental Energy Research [Eindhoven] ( DIFFER ), Institut Jean Lamour ( IJL ), Centre National de la Recherche Scientifique ( CNRS ) -Université de Lorraine ( UL ), Dipartimento di Energia, Politecnico di Milano [Milan], Max Planck Institute for Plasma Physics, Laboratoire de Mécanique, Modélisation et Procédés Propres ( M2P2 ), Aix Marseille Université ( AMU ) -Ecole Centrale de Marseille ( ECM ) -Centre National de la Recherche Scientifique ( CNRS ), Universitat Politècnica de Catalunya. Departament de Física, Universitat Politècnica de Catalunya. ANT - Advanced Nuclear Technologies Research Group, JET Contributors, Litaudon, X, Abduallev, S, Abhangi, M, Abreu, P, Afzal, M, Aggarwal, K, Ahlgren, T, Ahn, J, Aho Mantila, L, Aiba, N, Airila, M, Albanese, R, Aldred, V, Alegre, D, Alessi, E, Aleynikov, P, Alfier, A, Alkseev, A, Allinson, M, Alper, B, Alves, E, Ambrosino, G, Ambrosino, R, Amicucci, L, Amosov, V, Andersson SundÃ©n, E, Angelone, M, Anghel, M, Angioni, C, Appel, L, Appelbee, C, Arena, P, Ariola, M, Arnichand, H, Arshad, S, Ash, A, Ashikawa, N, Aslanyan, V, Asunta, O, Auriemma, F, Austin, Y, Avotina, L, Axton, M, Ayres, C, Bacharis, M, Baciero, A, Baiã¡o, D, Bailey, S, Baker, A, Balboa, I, Balden, M, Balshaw, N, Bament, R, Banks, J, Baranov, Y, Barnard, M, Barnes, D, Barnes, M, Barnsley, R, Baron Wiechec, A, Barrera Orte, L, Baruzzo, M, Basiuk, V, Bassan, M, Bastow, R, Batista, A, Batistoni, P, Baughan, R, Bauvir, B, Baylor, L, Bazylev, B, Beal, J, Beaumont, P, Beckers, M, Beckett, B, Becoulet, A, Bekris, N, Beldishevski, M, Bell, K, Belli, F, Bellinger, M, Belonohy, Ã, Ben Ayed, N, Benterman, N, Bergsã¥ker, H, Bernardo, J, Bernert, M, Berry, M, Bertalot, L, Besliu, C, Beurskens, M, Bieg, B, Bielecki, J, Biewer, T, Bigi, M, Bãlkovã¡, P, Binda, F, Bisoffi, A, Bizarro, J, Bjã¶rkas, C, Blackburn, J, Blackman, K, Blackman, T, Blanchard, P, Blatchford, P, Bobkov, V, Boboc, A, Bodnã¡r, G, Bogar, O, Bolshakova, I, Bolzonella, T, Bonanomi, N, Bonelli, F, Boom, J, Booth, J, Borba, D, Borodin, D, Borodkina, I, Botrugno, A, Bottereau, C, Boulting, P, Bourdelle, C, Bowden, M, Bower, C, Bowman, C, Boyce, T, Boyd, C, Boyer, H, Bradshaw, J, Braic, V, Bravanec, R, Breizman, B, Bremond, S, Brennan, P, Breton, S, Brett, A, Brezinsek, S, Bright, M, Brix, M, Broeckx, W, Brombin, M, Broså‚awski, A, Brown, D, Brown, M, Bruno, E, Bucalossi, J, Buch, J, Buchanan, J, Buckley, M, Budny, R, Bufferand, H, Bulman, M, Bulmer, N, Bunting, P, Buratti, P, Burckhart, A, Buscarino, A, Busse, A, Butler, N, Bykov, I, Byrne, J, Cahyna, P, Calabrã², G, Calvo, I, Camenen, Y, Camp, P, Campling, D, Cane, J, Cannas, B, Capel, A, Card, P, Cardinali, A, Carman, P, Carr, M, Carralero, D, Carraro, L, Carvalho, B, Carvalho, I, Carvalho, P, Casson, F, Castaldo, C, Catarino, N, Caumont, J, Causa, F, Cavazzana, R, Cave Ayland, K, Cavinato, M, Cecconello, M, Ceccuzzi, S, Cecil, E, Cenedese, A, Cesario, R, Challis, C, Chandler, M, Chandra, D, Chang, C, Chankin, A, Chapman, I, Chapman, S, Chernyshova, M, Chitarin, G, Ciraolo, G, Ciric, D, Citrin, J, Clairet, F, Clark, E, Clark, M, Clarkson, R, Clatworthy, D, Clements, C, Cleverly, M, Coad, J, Coates, P, Cobalt, A, Coccorese, V, Cocilovo, V, Coda, S, Coelho, R, Coenen, J, Coffey, I, Colas, L, Collins, S, Conka, D, Conroy, S, Conway, N, Coombs, D, Cooper, D, Cooper, S, Corradino, C, Corre, Y, Corrigan, G, Cortes, S, Coster, D, Couchman, A, Cox, M, Craciunescu, T, Cramp, S, Craven, R, Crisanti, F, Croci, G, Croft, D, Crombã©, K, Crowe, R, Cruz, N, Cseh, G, Cufar, A, Cullen, A, Curuia, M, Czarnecka, A, Dabirikhah, H, Dalgliesh, P, Dalley, S, Dankowski, J, Darrow, D, Davies, O, Davis, W, Day, C, Day, I, De Bock, M, De Castro, A, De La Cal, E, De La Luna, E, De Masi, G, De Pablos, J, De Temmerman, G, De Tommasi, G, De Vries, P, Deakin, K, Deane, J, Degli Agostini, F, Dejarnac, R, Delabie, E, Den Harder, N, Dendy, R, Denis, J, Denner, P, Devaux, S, Devynck, P, Di Maio, F, Di Siena, A, Di Troia, C, Dinca, P, D'Inca, R, Ding, B, Dittmar, T, Doerk, H, Doerner, R, Donnã©, T, Dorling, S, Dormido Canto, S, Doswon, S, Douai, D, Doyle, P, Drenik, A, Drewelow, P, Drews, P, Duckworth, P, Dumont, R, Dumortier, P, Dunai, D, Dunne, M, Äžuran, I, Durodiã©, F, Dutta, P, Duval, B, Dux, R, Dylst, K, Dzysiuk, N, Edappala, P, Edmond, J, Edwards, A, Edwards, J, Eich, T, Ekedahl, A, El Jorf, R, Elsmore, C, Enachescu, M, Ericsson, G, Eriksson, F, Eriksson, J, Eriksson, L, Esposito, B, Esquembri, S, Esser, H, Esteve, D, Evans, B, Evans, G, Evison, G, Ewart, G, Fagan, D, Faitsch, M, Falie, D, Fanni, A, Fasoli, A, Faustin, J, Fawlk, N, Fazendeiro, L, Fedorczak, N, Felton, R, Fenton, K, Fernades, A, Fernandes, H, Ferreira, J, Fessey, J, Fã©vrier, O, Ficker, O, Field, A, Fietz, S, Figueiredo, A, Figueiredo, J, Fil, A, Finburg, P, Firdaouss, M, Fischer, U, Fittill, L, Fitzgerald, M, Flammini, D, Flanagan, J, Fleming, C, Flinders, K, Fonnesu, N, Fontdecaba, J, Formisano, A, Forsythe, L, Fortuna, L, Fortuna Zalesna, E, Fortune, M, Foster, S, Franke, T, Franklin, T, Frasca, M, Frassinetti, L, Freisinger, M, Fresa, R, Frigione, D, Fuchs, V, Fuller, D, Futatani, S, Fyvie, J, Gã¡l, K, Galassi, D, Gaå‚azka, K, Galdon Quiroga, J, Gallagher, J, Gallart, D, Galvã¡o, R, Gao, X, Gao, Y, Garcia, J, Garcia Carrasco, A, GarcÃa MuÃ±oz, M, Gardarein, J, Garzotti, L, Gaudio, P, Gauthier, E, Gear, D, Gee, S, Geiger, B, Gelfusa, M, Gerasimov, S, Gervasini, G, Gethins, M, Ghani, Z, Ghate, M, Gherendi, M, Giacalone, J, Giacomelli, L, Gibson, C, Giegerich, T, Gil, C, Gil, L, Gilligan, S, Gin, D, Giovannozzi, E, Girardo, J, Giroud, C, Giruzzi, G, Glã¶ggler, S, Godwin, J, Goff, J, Gohil, P, Goloborod'Ko, V, Gomes, R, Goncalves, B, Goniche, M, Goodliffe, M, Goodyear, A, Gorini, G, Gosk, M, Goulding, R, Goussarov, A, Gowland, R, Graham, B, Graham, M, Graves, J, Grazier, N, Grazier, P, Green, N, Greuner, H, Grierson, B, Griph, F, Grisolia, C, Grist, D, Groth, M, Grove, R, Grundy, C, Grzonka, J, Guard, D, Guã©rard, C, Guillemaut, C, Guirlet, R, Gurl, C, Utoh, H, Hackett, L, Hacquin, S, Hagar, A, Hager, R, Hakola, A, Halitovs, M, Hall, S, Hallworth Cook, S, Hamlyn Harris, C, Hammond, K, Harrington, C, Harrison, J, Harting, D, Hasenbeck, F, Hatano, Y, Hatch, D, Haupt, T, Hawes, J, Hawkes, N, Hawkins, J, Hawkins, P, Haydon, P, Hayter, N, Hazel, S, Heesterman, P, Heinola, K, Hellesen, C, Hellsten, T, Helou, W, Hemming, O, Hender, T, Henderson, M, Henderson, S, Henriques, R, Hepple, D, Hermon, G, Hertout, P, Hidalgo, C, Highcock, E, Hill, M, Hillairet, J, Hillesheim, J, Hillis, D, Hizanidis, K, Hjalmarsson, A, Hobirk, J, Hodille, E, Hogben, C, Hogeweij, G, Hollingsworth, A, Hollis, S, Homfray, D, Horã¡ä ek, J, Hornung, G, Horton, A, Horton, L, Horvath, L, Hotchin, S, Hough, M, Howarth, P, Hubbard, A, Huber, A, Huber, V, Huddleston, T, Hughes, M, Huijsmans, G, Hunter, C, Huynh, P, Hynes, A, Iglesias, D, Imazawa, N, Imbeaux, F, Imrãå¡ek, M, Incelli, M, Innocente, P, Irishkin, M, Ivanova Stanik, I, Jachmich, S, Jacobsen, A, Jacquet, P, Jansons, J, Jardin, A, Jã¤rvinen, A, Jaulmes, F, Jednorã³g, S, Jenkins, I, Jeong, C, Jepu, I, Joffrin, E, Johnson, R, Johnson, T, Johnston, J, Joita, L, Jones, G, Jones, T, Hoshino, K, Kallenbach, A, Kamiya, K, Kaniewski, J, Kantor, A, Kappatou, A, Karhunen, J, Karkinsky, D, Karnowska, I, Kaufman, M, Kaveney, G, Kazakov, Y, Kazantzidis, V, Keeling, D, Keenan, T, Keep, J, Kempenaars, M, Kennedy, C, Kenny, D, Kent, J, Kent, O, Khilkevich, E, Kim, H, Kinch, A, King, C, King, D, King, R, Kinna, D, Kiptily, V, Kirk, A, Kirov, K, Kirschner, A, Kizane, G, Klepper, C, Klix, A, Knight, P, Knipe, S, Knott, S, Kobuchi, T, Kã¶chl, F, Kocsis, G, Kodeli, I, Kogan, L, Kogut, D, Koivuranta, S, Kominis, Y, Kã¶ppen, M, Kos, B, Koskela, T, Koslowski, H, Koubiti, M, Kovari, M, Kowalska StrzÈ©ciwilk, E, Krasilnikov, A, Krasilnikov, V, Krawczyk, N, Kresina, M, Krieger, K, Krivska, A, Kruezi, U, Ksiaå¼ek, I, Kukushkin, A, Kundu, A, Kurki Suonio, T, Kwak, S, Kwiatkowski, R, Kwon, O, Laguardia, L, Lahtinen, A, Laing, A, Lam, N, Lambertz, H, Lane, C, Lang, P, Lanthaler, S, Lapins, J, Lasa, A, Last, J, Å aszyå„ska, E, Lawless, R, Lawson, A, Lawson, K, Lazaros, A, Lazzaro, E, Leddy, J, Lee, S, Lefebvre, X, Leggate, H, Lehmann, J, Lehnen, M, Leichtle, D, Leichuer, P, Leipold, F, Lengar, I, Lennholm, M, Lerche, E, Lescinskis, A, Lesnoj, S, Letellier, E, Leyland, M, Leysen, W, Li, L, Liang, Y, Likonen, J, Linke, J, Linsmeier, C, Lipschultz, B, Liu, G, Liu, Y, Lo Schiavo, V, Loarer, T, Loarte, A, Lobel, R, Lomanowski, B, Lomas, P, Lã¶nnroth, J, Lã³pez, J, LÃ³pez Razola, J, Lorenzini, R, Losada, U, Lovell, J, Loving, A, Lowry, C, Luce, T, Lucock, R, Lukin, A, Luna, C, Lungaroni, M, Lungu, C, Lungu, M, Lunniss, A, Lupelli, I, Lyssoivan, A, Macdonald, N, Macheta, P, Maczewa, K, Magesh, B, Maget, P, Maggi, C, Maier, H, Mailloux, J, Makkonen, T, Makwana, R, Malaquias, A, Malizia, A, Manas, P, Manning, A, Manso, M, Mantica, P, Mantsinen, M, Manzanares, A, Maquet, P, Marandet, Y, Marcenko, N, Marchetto, C, Marchuk, O, Marinelli, M, Marinucci, M, Markoviä , T, Marocco, D, Marot, L, Marren, C, Marshal, R, Martin, A, Martin, Y, MartÃn De Aguilera, A, Martãnez, F, MartÃn SolÃs, J, Martynova, Y, Maruyama, S, Masiello, A, Maslov, M, Matejcik, S, Mattei, M, Matthews, G, Maviglia, F, Mayer, M, Mayoral, M, May Smith, T, Mazon, D, Mazzotta, C, Mcadams, R, Mccarthy, P, Mcclements, K, Mccormack, O, Mccullen, P, Mcdonald, D, Mcintosh, S, Mckean, R, Mckehon, J, Meadows, R, Meakins, A, Medina, F, Medland, M, Medley, S, Meigh, S, Meigs, A, Meisl, G, Meitner, S, Meneses, L, Menmuir, S, Mergia, K, Merrigan, I, Mertens, P, Meshchaninov, S, Messiaen, A, Meyer, H, Mianowski, S, Michling, R, Middleton Gear, D, Miettunen, J, Militello, F, Militello Asp, E, Miloshevsky, G, Mink, F, Minucci, S, Miyoshi, Y, Mlynã¡å™, J, Molina, D, Monakhov, I, Moneti, M, Mooney, R, Moradi, S, Mordijck, S, Moreira, L, Moreno, R, Moro, F, Morris, A, Morris, J, Moser, L, Mosher, S, Moulton, D, Murari, A, Muraro, A, Murphy, S, Asakura, N, Na, Y, Nabais, F, Naish, R, Nakano, T, Nardon, E, Naulin, V, Nave, M, Nedzelski, I, Nemtsev, G, Nespoli, F, Neto, A, Neu, R, Neverov, V, Newman, M, Nicholls, K, Nicolas, T, Nielsen, A, Nielsen, P, Nilsson, E, Nishijima, D, Noble, C, Nocente, M, Nodwell, D, Nordlund, K, Nordman, H, Nouailletas, R, Nunes, I, Oberkofler, M, Odupitan, T, Ogawa, M, O'Gorman, T, Okabayashi, M, Olney, R, Omolayo, O, O'Mullane, M, Ongena, J, Orsitto, F, Orszagh, J, Oswuigwe, B, Otin, R, Owen, A, Paccagnella, R, Pace, N, Pacella, D, Packer, L, Page, A, Pajuste, E, Palazzo, S, Pamela, S, Panja, S, Papp, P, Paprok, R, Parail, V, Park, M, Parra Diaz, F, Parsons, M, Pasqualotto, R, Patel, A, Pathak, S, Paton, D, Patten, H, Pau, A, Pawelec, E, Paz Soldan, C, Peackoc, A, Pearson, I, Pehkonen, S, Peluso, E, Penot, C, Pereira, A, Pereira, R, Pereira Puglia, P, Perez Von Thun, C, Peruzzo, S, Peschanyi, S, Peterka, M, Petersson, P, Petravich, G, Petre, A, Petrella, N, Petrå¾ilka, V, Peysson, Y, Pfefferlã©, D, Philipps, V, Pillon, M, Pintsuk, G, Piovesan, P, Pires Dos Reis, A, Piron, L, Pironti, A, Pisano, F, Pitts, R, Pizzo, F, Plyusnin, V, Pomaro, N, Pompilian, O, Pool, P, Popovichev, S, Porfiri, M, Porosnicu, C, Porton, M, Possnert, G, Potzel, S, Powell, T, Pozzi, J, Prajapati, V, Prakash, R, Prestopino, G, Price, D, Price, M, Price, R, Prior, P, Proudfoot, R, Pucella, G, Puglia, P, Puiatti, M, Pulley, D, Purahoo, K, Pã¼tterich, T, Rachlew, E, Rack, M, Ragona, R, Rainford, M, Rakha, A, Ramogida, G, Ranjan, S, Rapson, C, Rasmussen, J, Rathod, K, Rattã¡, G, Ratynskaia, S, Ravera, G, Rayner, C, Rebai, M, Reece, D, Reed, A, Rã©fy, D, Regan, B, Regaã±a, J, Reich, M, Reid, N, Reimold, F, Reinhart, M, Reinke, M, Reiser, D, Rendell, D, Reux, C, Reyes Cortes, S, Reynolds, S, Riccardo, V, Richardson, N, Riddle, K, Rigamonti, D, Rimini, F, Risner, J, Riva, M, Roach, C, Robins, R, Robinson, S, Robinson, T, Robson, D, Roccella, R, Rodionov, R, Rodrigues, P, Rodriguez, J, Rohde, V, Romanelli, F, Romanelli, M, Romanelli, S, Romazanov, J, Rowe, S, Rubel, M, Rubinacci, G, Rubino, G, Ruchko, L, Ruiz, M, Ruset, C, Rzadkiewicz, J, Saarelma, S, Sabot, R, Safi, E, Sagar, P, Saibene, G, Saint Laurent, F, Salewski, M, Salmi, A, Salmon, R, Salzedas, F, Samaddar, D, Samm, U, Sandiford, D, Santa, P, Santala, M, Santos, B, Santucci, A, Sartori, F, Sartori, R, Sauter, O, Scannell, R, Schlummer, T, Schmid, K, Schmidt, V, Schmuck, S, Schneider, M, Schã¶pf, K, Schwã¶rer, D, Scott, S, Sergienko, G, Sertoli, M, Shabbir, A, Sharapov, S, Shaw, A, Shaw, R, Sheikh, H, Shepherd, A, Shevelev, A, Shumack, A, Sias, G, Sibbald, M, Sieglin, B, Silburn, S, Silva, A, Silva, C, Simmons, P, Simpson, J, Simpson Hutchinson, J, Sinha, A, Sipilã¤, S, Sips, A, Sirã©n, P, Sirinelli, A, Sjã¶strand, H, Skiba, M, Skilton, R, Slabkowska, K, Slade, B, Smith, N, Smith, P, Smith, R, Smith, T, Smithies, M, Snoj, L, Soare, S, Solano, E, Somers, A, Sommariva, C, Sonato, P, Sopplesa, A, Sousa, J, Sozzi, C, Spagnolo, S, Spelzini, T, Spineanu, F, Stables, G, Stamatelatos, I, Stamp, M, Staniec, P, Stankå«nas, G, Stan Sion, C, Stead, M, Stefanikova, E, Stepanov, I, Stephen, A, Stephen, M, Stevens, A, Stevens, B, Strachan, J, Strand, P, Strauss, H, Strã¶m, P, Stubbs, G, Studholme, W, Subba, F, Summers, H, Svensson, J, Åšwiderski, Å, Szabolics, T, Szawlowski, M, Szepesi, G, Suzuki, T, Tã¡l, B, Tala, T, Talbot, A, Talebzadeh, S, Taliercio, C, Tamain, P, Tame, C, Tang, W, Tardocchi, M, Taroni, L, Taylor, D, Taylor, K, Tegnered, D, Telesca, G, Teplova, N, Terranova, D, Testa, D, Tholerus, E, Thomas, J, Thomas, P, Thompson, A, Thompson, C, Thompson, V, Thorne, L, Thornton, A, Thrysã¸e, A, Tigwell, P, Tipton, N, Tiseanu, I, Tojo, H, Tokitani, M, Tolias, P, Tomeå¡, M, Tonner, P, Towndrow, M, Trimble, P, Tripsky, M, Tsalas, M, Tsavalas, P, Tskhakaya Jun, D, Turner, I, Turner, M, Turnyanskiy, M, Tvalashvili, G, Tyrrell, S, Uccello, A, Ul Abidin, Z, Uljanovs, J, Ulyatt, D, Urano, H, Uytdenhouwen, I, Vadgama, A, Valcarcel, D, Valentinuzzi, M, Valisa, M, Vallejos Olivares, P, Valovic, M, Van De Mortel, M, Van Eester, D, Van Renterghem, W, Van Rooij, G, Varje, J, Varoutis, S, Vartanian, S, Vasava, K, Vasilopoulou, T, Vega, J, Verdoolaege, G, Verhoeven, R, Verona, C, Verona Rinati, G, Veshchev, E, Vianello, N, Vicente, J, Viezzer, E, Villari, S, Villone, F, Vincenzi, P, Vinyar, I, Viola, B, Vitins, A, Vizvary, Z, Vlad, M, Voitsekhovitch, I, Vondrã¡ä ek, P, Vora, N, Vu, T, Pires De Sa, W, Wakeling, B, Waldon, C, Walkden, N, Walker, M, Walker, R, Walsh, M, Wang, E, Wang, N, Warder, S, Warren, R, Waterhouse, J, Watkins, N, Watts, C, Wauters, T, Weckmann, A, Weiland, J, Weisen, H, Weiszflog, M, Wellstood, C, West, A, Wheatley, M, Whetham, S, Whitehead, A, Whitehead, B, Widdowson, A, Wiesen, S, Wilkinson, J, Williams, J, Williams, M, Wilson, A, Wilson, D, Wilson, H, Wilson, J, Wischmeier, M, Withenshaw, G, Withycombe, A, Witts, D, Wood, D, Wood, R, Woodley, C, Wray, S, Wright, J, Wu, J, Wukitch, S, Wynn, A, Xu, T, Yadikin, D, Yanling, W, Yao, L, Yavorskij, V, Yoo, M, Young, C, Young, D, Young, I, Young, R, Zacks, J, Zagorski, R, Zaitsev, F, Zanino, R, Zarins, A, Zastrow, K, Zerbini, M, Zhang, W, Zhou, Y, Zilli, E, Zoita, V, Zoletnik, S, Zychor, I, Materials Physics, Department of Physics, European Commission, Litaudon, X., Abduallev, S., Abhangi, M., Abreu, P., Afzal, M., Aggarwal, K. M., Ahlgren, T., Ahn, J. H., Aho-Mantila, L., Aiba, N., Airila, M., Albanese, R., Aldred, V., Alegre, D., Alessi, E., Aleynikov, P., Alfier, A., Alkseev, A., Allinson, M., Alper, B., Alves, E., Ambrosino, G., Ambrosino, R., Amicucci, L., Amosov, V., Andersson Sundén, E., Angelone, M., Anghel, M., Angioni, C., Appel, L., Appelbee, C., Arena, P., Ariola, M., Arnichand, H., Arshad, S., Ash, A., Ashikawa, N., Aslanyan, V., Asunta, O., Auriemma, F., Austin, Y., Avotina, L., Axton, M. D., Ayres, C., Bacharis, M., Baciero, A., Baião, D., Bailey, S., Baker, A., Balboa, I., Balden, M., Balshaw, N., Bament, R., Banks, J. W., Baranov, Y. F., Barnard, M. A., Barnes, D., Barnes, M., Barnsley, R., Baron Wiechec, A., Barrera Orte, L., Baruzzo, M., Basiuk, V., Bassan, M., Bastow, R., Batista, A., Batistoni, P., Baughan, R., Bauvir, B., Baylor, L., Bazylev, B., Beal, J., Beaumont, P. S., Beckers, M., Beckett, B., Becoulet, A., Bekris, N., Beldishevski, M., Bell, K., Belli, F., Bellinger, M., Belonohy, É., Ben Ayed, N., Benterman, N. A., Bergsåker, H., Bernardo, J., Bernert, M., Berry, M., Bertalot, L., Besliu, C., Beurskens, M., Bieg, B., Bielecki, J., Biewer, T., Bigi, M., Bílková, P., Binda, F., Bisoffi, A., Bizarro, J. P. S., Björkas, C., Blackburn, J., Blackman, K., Blackman, T. R., Blanchard, P., Blatchford, P., Bobkov, V., Boboc, A., Bodnár, G., Bogar, O., Bolshakova, I., Bolzonella, T., Bonanomi, N., Bonelli, F., Boom, J., Booth, J., Borba, D., Borodin, D., Borodkina, I., Botrugno, A., Bottereau, C., Boulting, P., Bourdelle, C., Bowden, M., Bower, C., Bowman, C., Boyce, T., Boyd, C., Boyer, H. J., Bradshaw, J. M. A., Braic, V., Bravanec, R., Breizman, B., Bremond, S., Brennan, P. D., Breton, S., Brett, A., Brezinsek, S., Bright, M. D. J., Brix, M., Broeckx, W., Brombin, M., Brosławski, A., Brown, D. P. D., Brown, M., Bruno, E., Bucalossi, J., Buch, J., Buchanan, J., Buckley, M. A., Budny, R., Bufferand, H., Bulman, M., Bulmer, N., Bunting, P., Buratti, P., Burckhart, A., Buscarino, A., Busse, A., Butler, N. K., Bykov, I., Byrne, J., Cahyna, P., Calabrò, G., Calvo, I., Camenen, Y., Camp, P., Campling, D. C., Cane, J., Cannas, B., Capel, A. J., Card, P. J., Cardinali, A., Carman, P., Carr, M., Carralero, D., Carraro, L., Carvalho, B. B., Carvalho, I., Carvalho, P., Casson, F. J., Castaldo, C., Catarino, N., Caumont, J., Causa, F., Cavazzana, R., Cave-Ayland, K., Cavinato, M., Cecconello, M., Ceccuzzi, S., Cecil, E., Cenedese, A., Cesario, R., Challis, C. D., Chandler, M., Chandra, D., Chang, C. S., Chankin, A., Chapman, I. T., Chapman, S. C., Chernyshova, M., Chitarin, G., Ciraolo, G., Ciric, D., Citrin, J., Clairet, F., Clark, E., Clark, M., Clarkson, R., Clatworthy, D., Clements, C., Cleverly, M., Coad, J. P., Coates, P. A., Cobalt, A., Coccorese, V., Cocilovo, V., Coda, S., Coelho, R., Coenen, J. W., Coffey, I., Colas, L., Collins, S., Conka, D., Conroy, S., Conway, N., Coombs, D., Cooper, D., Cooper, S. R., Corradino, C., Corre, Y., Corrigan, G., Cortes, S., Coster, D., Couchman, A. S., Cox, M. P., Craciunescu, T., Cramp, S., Craven, R., Crisanti, F., Croci, G., Croft, D., Crombé, K., Crowe, R., Cruz, N., Cseh, G., Cufar, A., Cullen, A., Curuia, M., Czarnecka, A., Dabirikhah, H., Dalgliesh, P., Dalley, S., Dankowski, J., Darrow, D., Davies, O., Davis, W., Day, C., Day, I. E., De Bock, M., de Castro, A., de la Cal, E., de la Luna, E., De Masi, G., de Pablos, J. L., De Temmerman, G., De Tommasi, G., de Vries, P., Deakin, K., Deane, J., Degli Agostini, F., Dejarnac, R., Delabie, E., den Harder, N., Dendy, R. O., Denis, J., Denner, P., Devaux, S., Devynck, P., Di Maio, F., Di Siena, A., Di Troia, C., Dinca, P., D’Inca, R., Ding, B., Dittmar, T., Doerk, H., Doerner, R. P., Donné, T., Dorling, S. E., Dormido-Canto, S., Doswon, S., Douai, D., Doyle, P. T., Drenik, A., Drewelow, P., Drews, P., Duckworth, Ph., Dumont, R., Dumortier, P., Dunai, D., Dunne, M., Ďuran, I., Durodié, F., Dutta, P., Duval, B. P., Dux, R., Dylst, K., Dzysiuk, N., Edappala, P. V., Edmond, J., Edwards, A. M., Edwards, J., Eich, Th., Ekedahl, A., El-Jorf, R., Elsmore, C. G., Enachescu, M., Ericsson, G., Eriksson, F., Eriksson, J., Eriksson, L. G., Esposito, B., Esquembri, S., Esser, H. G., Esteve, D., Evans, B., Evans, G. E., Evison, G., Ewart, G. D., Fagan, D., Faitsch, M., Falie, D., Fanni, A., Fasoli, A., Faustin, J. M., Fawlk, N., Fazendeiro, L., Fedorczak, N., Felton, R. C., Fenton, K., Fernades, A., Fernandes, H., Ferreira, J., Fessey, J. A., Février, O., Ficker, O., Field, A., Fietz, S., Figueiredo, A., Figueiredo, J., Fil, A., Finburg, P., Firdaouss, M., Fischer, U., Fittill, L., Fitzgerald, M., Flammini, D., Flanagan, J., Fleming, C., Flinders, K., Fonnesu, N., Fontdecaba, J. M., Formisano, A., Forsythe, L., Fortuna, L., Fortuna-Zalesna, E., Fortune, M., Foster, S., Franke, T., Franklin, T., Frasca, M., Frassinetti, L., Freisinger, M., Fresa, R., Frigione, D., Fuchs, V., Fuller, D., Futatani, S., Fyvie, J., Gál, K., Galassi, D., Gałązka, K., Galdon-Quiroga, J., Gallagher, J., Gallart, D., Galvão, R., Gao, X., Gao, Y., Garcia, J., Garcia-Carrasco, A., García-Muñoz, M., Gardarein, J. -L., Garzotti, L., Gaudio, P., Gauthier, E., Gear, D. F., Gee, S. J., Geiger, B., Gelfusa, M., Gerasimov, S., Gervasini, G., Gethins, M., Ghani, Z., Ghate, M., Gherendi, M., Giacalone, J. C., Giacomelli, L., Gibson, C. S., Giegerich, T., Gil, C., Gil, L., Gilligan, S., Gin, D., Giovannozzi, E., Girardo, J. B., Giroud, C., Giruzzi, G., Glöggler, S., Godwin, J., Goff, J., Gohil, P., Goloborod’Ko, V., Gomes, R., Gonçalves, B., Goniche, M., Goodliffe, M., Goodyear, A., Gorini, G., Gosk, M., Goulding, R., Goussarov, A., Gowland, R., Graham, B., Graham, M. E., Graves, J. P., Grazier, N., Grazier, P., Green, N. R., Greuner, H., Grierson, B., Griph, F. S., Grisolia, C., Grist, D., Groth, M., Grove, R., Grundy, C. N., Grzonka, J., Guard, D., Guérard, C., Guillemaut, C., Guirlet, R., Gurl, C., Utoh, H. H., Hackett, L. J., Hacquin, S., Hagar, A., Hager, R., Hakola, A., Halitovs, M., Hall, S. J., Hallworth Cook, S. P., Hamlyn-Harris, C., Hammond, K., Harrington, C., Harrison, J., Harting, D., Hasenbeck, F., Hatano, Y., Hatch, D. R., Haupt, T. D. V., Hawes, J., Hawkes, N. C., Hawkins, J., Hawkins, P., Haydon, P. W., Hayter, N., Hazel, S., Heesterman, P. J. L., Heinola, K., Hellesen, C., Hellsten, T., Helou, W., Hemming, O. N., Hender, T. C., Henderson, M., Henderson, S. S., Henriques, R., Hepple, D., Hermon, G., Hertout, P., Hidalgo, C., Highcock, E. G., Hill, M., Hillairet, J., Hillesheim, J., Hillis, D., Hizanidis, K., Hjalmarsson, A., Hobirk, J., Hodille, E., Hogben, C. H. A., Hogeweij, G. M. D., Hollingsworth, A., Hollis, S., Homfray, D. A., Horáček, J., Hornung, G., Horton, A. R., Horton, L. D., Horvath, L., Hotchin, S. P., Hough, M. R., Howarth, P. J., Hubbard, A., Huber, A., Huber, V., Huddleston, T. M., Hughes, M., Huijsmans, G. T. A., Hunter, C. L., Huynh, P., Hynes, A. M., Iglesias, D., Imazawa, N., Imbeaux, F., Imríšek, M., Incelli, M., Innocente, P., Irishkin, M., Ivanova-Stanik, I., Jachmich, S., Jacobsen, A. S., Jacquet, P., Jansons, J., Jardin, A., Järvinen, A., Jaulmes, F., Jednoróg, S., Jenkins, I., Jeong, C., Jepu, I., Joffrin, E., Johnson, R., Johnson, T., Johnston, Jane, Joita, L., Jones, G., Jones, T. T. C., Hoshino, K. K., Kallenbach, A., Kamiya, K., Kaniewski, J., Kantor, A., Kappatou, A., Karhunen, J., Karkinsky, D., Karnowska, I., Kaufman, M., Kaveney, G., Kazakov, Y., Kazantzidis, V., Keeling, D. L., Keenan, T., Keep, J., Kempenaars, M., Kennedy, C., Kenny, D., Kent, J., Kent, O. N., Khilkevich, E., Kim, H. T., Kim, H. S., Kinch, A., King, C., King, D., King, R. F., Kinna, D. J., Kiptily, V., Kirk, A., Kirov, K., Kirschner, A., Kizane, G., Klepper, C., Klix, A., Knight, P., Knipe, S. J., Knott, S., Kobuchi, T., Köchl, F., Kocsis, G., Kodeli, I., Kogan, L., Kogut, D., Koivuranta, S., Kominis, Y., Köppen, M., Kos, B., Koskela, T., Koslowski, H. R., Koubiti, M., Kovari, M., Kowalska-Strzęciwilk, E., Krasilnikov, A., Krasilnikov, V., Krawczyk, N., Kresina, M., Krieger, K., Krivska, A., Kruezi, U., Książek, I., Kukushkin, A., Kundu, A., Kurki-Suonio, T., Kwak, S., Kwiatkowski, R., Kwon, O. J., Laguardia, L., Lahtinen, A., Laing, A., Lam, N., Lambertz, H. T., Lane, C., Lang, P. T., Lanthaler, S., Lapins, J., Lasa, A., Last, J. R., Łaszyńska, E., Lawless, R., Lawson, A., Lawson, K. D., Lazaros, A., Lazzaro, E., Leddy, J., Lee, S., Lefebvre, X., Leggate, H. J., Lehmann, J., Lehnen, M., Leichtle, D., Leichuer, P., Leipold, F., Lengar, I., Lennholm, M., Lerche, E., Lescinskis, A., Lesnoj, S., Letellier, E., Leyland, M., Leysen, W., Li, L., Liang, Y., Likonen, J., Linke, J., Linsmeier, Ch., Lipschultz, B., Liu, G., Liu, Y., Lo Schiavo, V. P., Loarer, T., Loarte, A., Lobel, R. C., Lomanowski, B., Lomas, P. J., Lönnroth, J., López, J. M., López-Razola, J., Lorenzini, R., Losada, U., Lovell, J. J., Loving, A. B., Lowry, C., Luce, T., Lucock, R. M. A., Lukin, A., Luna, C., Lungaroni, M., Lungu, C. P., Lungu, M., Lunniss, A., Lupelli, I., Lyssoivan, A., Macdonald, N., Macheta, P., Maczewa, K., Magesh, B., Maget, P., Maggi, C., Maier, H., Mailloux, J., Makkonen, T., Makwana, R., Malaquias, A., Malizia, A., Manas, P., Manning, A., Manso, M. E., Mantica, P., Mantsinen, M., Manzanares, A., Maquet, Ph., Marandet, Y., Marcenko, N., Marchetto, C., Marchuk, O., Marinelli, M., Marinucci, M., Markovič, T., Marocco, D., Marot, L., Marren, C. A., Marshal, R., Martin, A., Martin, Y., Martín de Aguilera, A., Martínez, F. J., Martín-Solís, J. R., Martynova, Y., Maruyama, S., Masiello, A., Maslov, M., Matejcik, S., Mattei, M., Matthews, G. F., Maviglia, F., Mayer, M., Mayoral, M. L., May-Smith, T., Mazon, D., Mazzotta, C., Mcadams, R., Mccarthy, P. J., Mcclements, K. G., Mccormack, O., Mccullen, P. A., Mcdonald, D., Mcintosh, S., Mckean, R., Mckehon, J., Meadows, R. C., Meakins, A., Medina, F., Medland, M., Medley, S., Meigh, S., Meigs, A. G., Meisl, G., Meitner, S., Meneses, L., Menmuir, S., Mergia, K., Merrigan, I. R., Mertens, Ph., Meshchaninov, S., Messiaen, A., Meyer, H., Mianowski, S., Michling, R., Middleton-Gear, D., Miettunen, J., Militello, F., Militello-Asp, E., Miloshevsky, G., Mink, F., Minucci, S., Miyoshi, Y., Mlynář, J., Molina, D., Monakhov, I., Moneti, M., Mooney, R., Moradi, S., Mordijck, S., Moreira, L., Moreno, R., Moro, F., Morris, A. W., Morris, J., Moser, L., Mosher, S., Moulton, D., Murari, A., Muraro, A., Murphy, S., Asakura, N. N., Na, Y. S., Nabais, F., Naish, R., Nakano, T., Nardon, E., Naulin, V., Nave, M. F. F., Nedzelski, I., Nemtsev, G., Nespoli, F., Neto, A., Neu, R., Neverov, V. S., Newman, M., Nicholls, K. J., Nicolas, T., Nielsen, A. H., Nielsen, P., Nilsson, E., Nishijima, D., Noble, C., Nocente, M., Nodwell, D., Nordlund, K., Nordman, H., Nouailletas, R., Nunes, I., Oberkofler, M., Odupitan, T., Ogawa, M. T., O’Gorman, T., Okabayashi, M., Olney, R., Omolayo, O., O’Mullane, M., Ongena, J., Orsitto, F., Orszagh, J., Oswuigwe, B. I., Otin, R., Owen, A., Paccagnella, R., Pace, N., Pacella, D., Packer, L. W., Page, A., Pajuste, E., Palazzo, S., Pamela, S., Panja, S., Papp, P., Paprok, R., Parail, V., Park, M., Parra Diaz, F., Parsons, M., Pasqualotto, R., Patel, A., Pathak, S., Paton, D., Patten, H., Pau, A., Pawelec, E., Paz Soldan, C., Peackoc, A., Pearson, I. J., Pehkonen, S. -P., Peluso, E., Penot, C., Pereira, A., Pereira, R., Pereira Puglia, P. P., Perez von Thun, C., Peruzzo, S., Peschanyi, S., Peterka, M., Petersson, P., Petravich, G., Petre, A., Petrella, N., Petržilka, V., Peysson, Y., Pfefferlé, D., Philipps, V., Pillon, M., Pintsuk, G., Piovesan, P., Pires dos Reis, A., Piron, L., Pironti, A., Pisano, F., Pitts, R., Pizzo, F., Plyusnin, V., Pomaro, N., Pompilian, O. G., Pool, P. J., Popovichev, S., Porfiri, M. T., Porosnicu, C., Porton, M., Possnert, G., Potzel, S., Powell, T., Pozzi, J., Prajapati, V., Prakash, R., Prestopino, G., Price, D., Price, M., Price, R., Prior, P., Proudfoot, R., Pucella, G., Puglia, P., Puiatti, M. E., Pulley, D., Purahoo, K., Pütterich, Th., Rachlew, E., Rack, M., Ragona, R., Rainford, M. S. J., Rakha, A., Ramogida, G., Ranjan, S., Rapson, C. J., Rasmussen, J. J., Rathod, K., Rattá, G., Ratynskaia, S., Ravera, G., Rayner, C., Rebai, M., Reece, D., Reed, A., Réfy, D., Regan, B., Regaña, J., Reich, M., Reid, N., Reimold, F., Reinhart, M., Reinke, M., Reiser, D., Rendell, D., Reux, C., Reyes Cortes, S. D. A., Reynolds, S., Riccardo, V., Richardson, N., Riddle, K., Rigamonti, D., Rimini, F. G., Risner, J., Riva, M., Roach, C., Robins, R. J., Robinson, S. A., Robinson, T., Robson, D. W., Roccella, R., Rodionov, R., Rodrigues, P., Rodriguez, J., Rohde, V., Romanelli, F., Romanelli, M., Romanelli, S., Romazanov, J., Rowe, S., Rubel, M., Rubinacci, G., Rubino, G., Ruchko, L., Ruiz, M., Ruset, C., Rzadkiewicz, J., Saarelma, S., Sabot, R., Safi, E., Sagar, P., Saibene, G., Saint-Laurent, F., Salewski, M., Salmi, A., Salmon, R., Salzedas, F., Samaddar, D., Samm, U., Sandiford, D., Santa, P., Santala, M. I. K., Santos, B., Santucci, A., Sartori, F., Sartori, R., Sauter, O., Scannell, R., Schlummer, T., Schmid, K., Schmidt, V., Schmuck, S., Schneider, M., Schöpf, K., Schwörer, D., Scott, S. D., Sergienko, G., Sertoli, M., Shabbir, A., Sharapov, S. E., Shaw, A., Shaw, R., Sheikh, H., Shepherd, A., Shevelev, A., Shumack, A., Sias, G., Sibbald, M., Sieglin, B., Silburn, S., Silva, A., Silva, C., Simmons, P. A., Simpson, J., Simpson-Hutchinson, J., Sinha, A., Sipilä, S. K., Sips, A. C. C., Sirén, P., Sirinelli, A., Sjöstrand, H., Skiba, M., Skilton, R., Slabkowska, K., Slade, B., Smith, N., Smith, P. G., Smith, R., Smith, T. J., Smithies, M., Snoj, L., Soare, S., Solano, E. R., Somers, A., Sommariva, C., Sonato, P., Sopplesa, A., Sousa, J., Sozzi, C., Spagnolo, S., Spelzini, T., Spineanu, F., Stables, G., Stamatelatos, I., Stamp, M. F., Staniec, P., Stankūnas, G., Stan-Sion, C., Stead, M. J., Stefanikova, E., Stepanov, I., Stephen, A. V., Stephen, M., Stevens, A., Stevens, B. D., Strachan, J., Strand, P., Strauss, H. R., Ström, P., Stubbs, G., Studholme, W., Subba, F., Summers, H. P., Svensson, J., Świderski, Ł., Szabolics, T., Szawlowski, M., Szepesi, G., Suzuki, T. T., Tál, B., Tala, T., Talbot, A. R., Talebzadeh, S., Taliercio, C., Tamain, P., Tame, C., Tang, W., Tardocchi, M., Taroni, L., Taylor, D., Taylor, K. A., Tegnered, D., Telesca, G., Teplova, N., Terranova, D., Testa, D., Tholerus, E., Thomas, J., Thomas, J. D., Thomas, P., Thompson, A., Thompson, C. -A., Thompson, V. K., Thorne, L., Thornton, A., Thrysøe, A. S., Tigwell, P. A., Tipton, N., Tiseanu, I., Tojo, H., Tokitani, M., Tolias, P., Tomeš, M., Tonner, P., Towndrow, M., Trimble, P., Tripsky, M., Tsalas, M., Tsavalas, P., Tskhakaya jun, D., Turner, I., Turner, M. M., Turnyanskiy, M., Tvalashvili, G., Tyrrell, S. G. J., Uccello, A., Ul-Abidin, Z., Uljanovs, J., Ulyatt, D., Urano, H., Uytdenhouwen, I., Vadgama, A. P., Valcarcel, D., Valentinuzzi, M., Valisa, M., Vallejos Olivares, P., Valovic, M., Van De Mortel, M., Van Eester, D., Van Renterghem, W., van Rooij, G. J., Varje, J., Varoutis, S., Vartanian, S., Vasava, K., Vasilopoulou, T., Vega, J., Verdoolaege, G., Verhoeven, R., Verona, C., Verona Rinati, G., Veshchev, E., Vianello, N., Vicente, J., Viezzer, E., Villari, S., Villone, F., Vincenzi, P., Vinyar, I., Viola, B., Vitins, A., Vizvary, Z., Vlad, M., Voitsekhovitch, I., Vondráček, P., Vora, N., Vu, T., Pires de Sa, W. W., Wakeling, B., Waldon, C. W. F., Walkden, N., Walker, M., Walker, R., Walsh, M., Wang, E., Wang, N., Warder, S., Warren, R. J., Waterhouse, J., Watkins, N. W., Watts, C., Wauters, T., Weckmann, A., Weiland, J., Weisen, H., Weiszflog, M., Wellstood, C., West, A. T., Wheatley, M. R., Whetham, S., Whitehead, A. M., Whitehead, B. D., Widdowson, A. M., Wiesen, S., Wilkinson, J., Williams, J., Williams, M., Wilson, A. R., Wilson, D. J., Wilson, H. R., Wilson, J., Wischmeier, M., Withenshaw, G., Withycombe, A., Witts, D. M., Wood, D., Wood, R., Woodley, C., Wray, S., Wright, J., Wright, J. C., Wu, J., Wukitch, S., Wynn, A., Xu, T., Yadikin, D., Yanling, W., Yao, L., Yavorskij, V., Yoo, M. G., Young, C., Young, D., Young, I. D., Young, R., Zacks, J., Zagorski, R., Zaitsev, F. S., Zanino, R., Zarins, A., Zastrow, K. D., Zerbini, M., Zhang, W., Zhou, Y., Zilli, E., Zoita, V., Zoletnik, S., Zychor, I., Andersson SundÃ©n, E., Baiã¡o, D., Belonohy, Ã. ., Bergsã¥ker, H., Bãlkovã¡, P., Bjã¶rkas, C., Bodnã¡r, G., Broså awski, A., Calabrã², G., Crombã©, K., De Castro, A., De La Cal, E., De La Luna, E., De Pablos, J. L., De Vries, P., Den Harder, N., D'Inca, R., Donnã©, T., Duckworth, P. h., Ä uran, I., Durodiã©, F., Eich, T. h., Fã©vrier, O., Gã¡l, K., Gaå azka, K., Galvã¡o, R., GarcÃa-MuÃ±oz, M., Gardarein, J. -. L., Glã¶ggler, S., Goloborod'Ko, V., Goncalves, B., Guã©rard, C., Horã¡ä ek, J., Imrãå¡ek, M., Jã¤rvinen, A., Jednorã³g, S., Kã¶chl, F., Kã¶ppen, M., Kowalska-StrzÈ©ciwilk, E., Ksiaå¼ek, I., Å aszyå ska, E., Linsmeier, C. h., Lã¶nnroth, J., Lã³pez, J. M., LÃ³pez-Razola, J., Maquet, P. h., Markoviä , T., MartÃn De Aguilera, A., Martãnez, F. J., MartÃn-SolÃs, J. R., Mertens, P. h., Mlynã¡å , J., O'Gorman, T., O'Mullane, M., Pehkonen, S. -. P., Perez Von Thun, C., Petrå¾ilka, V., Pfefferlã©, D., Pires Dos Reis, A., Pã¼tterich, T. h., Rattã¡, G., Rã©fy, D., Regaã±a, J., Schã¶pf, K., Schwã¶rer, D., Sipilã¤, S. K., Sirã©n, P., Sjã¶strand, H., Stankå«nas, G., Strã¶m, P., Å widerski, Å. ., Tã¡l, B., Thompson, C. -. A., Thrysã¸e, A. S., Tomeå¡, M., Tskhakaya Jun, D., Van Rooij, G. J., Vondrã¡ä ek, P., Pires De Sa, W. W., Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Pierre et Marie Curie - Paris 6 (UPMC), Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Hôpital de Rangueil, CHU Toulouse [Toulouse]-CHU Toulouse [Toulouse], Laboratoire de microbiologie et génétique moléculaires (LMGM), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Lorraine (UL)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Energia [Milano] (DENG), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Research Centre Julich (FZJ), Institute for Plasma Research, Instituto Superior Tecnico Lisboa, Queen's University Belfast, University of Helsinki, CEA, Department of Applied Physics, School services, SCI, National Institutes for Quantum and Radiological Science and Technology, VTT, University of Naples Federico II, Universidad Nacional de Educacion a Distancia, CNR, Russian Research Centre Kurchatov Institute, Universita degli Studi di Napoli Parthenope, Ente Per Le Nuove Tecnologie L'energia e l'ambiente, Troitsk Institute for Innovation and Fusion Research, Uppsala University, National Institute for Cryogenics and Isotopic Technology, Max-Planck-Institut fur Plasmaphysik, University of Catania, Fusion for Energy Joint Undertaking, National Institutes of Natural Sciences - National Institute for Fusion Science, Massachusetts Institute of Technology, University of Latvia, Imperial College London, CIEMAT, University of Oxford, EUROfusion Programme Management Unit, Oak Ridge National Laboratory, Karlsruhe Institute of Technology KIT, University of York, Royal Institute of Technology, Maritime University of Szczecin, H. Niewodniczanski Institute of Nuclear Physics of the Polish Academy of Sciences, Czech Academy of Sciences, University of Trento, Ecole Polytechnique Federale de Lausanne (EPFL), Wigner Research Centre for Physics, Comenius University, University of Milan - Bicocca, National Institute for Optoelectronics, Fourth State Research, University of Texas at Austin, Belgian Nuclear Research Center, National Centre for Nuclear Research (NCBJ), Princeton University, CNRS, University of Cagliari, University of Warwick, Soltan Institute for Nuclear Studies, FOM Institute DIFFER, National Institute for Laser, Plasma and Radiation Physics, Ghent University, J. Stefan Institute, Universite de Lorraine, CAS - Institute of Plasma Physics, University of California at San Diego, Koninklijke Militaire School - Ecole Royale Militaire, Horia Hulubei National Institute of Physics and Nuclear Engineering, Chalmers University of Technology, School services, ELEC, Department of Signal Processing and Acoustics, Automaatio- ja systeemitekniik, Universidad Politecnica de Madrid, Second University of Naples, Warsaw University of Technology, Universita della Basilicata, Barcelona Supercomp. Center, Universidad de Sevilla, Centro Brasileiro de Pesquisas Fisicas, Department of Electrical Engineering and Automation, Sähkötekniikan laitos, University of Rome Tor Vergata, RAS - Ioffe Physico Technical Institute, General Atomics, University of Innsbruck, Fusion and Plasma Physics, University of Toyama, University of Strathclyde, National Technical University of Athens, Universita della Tuscia, Technical University of Denmark, Korea Advanced Institute of Science and Technology, Seoul National University, University College Cork, Vienna University of Technology, University of Opole, Daegu University, National Fusion Research Institute, Dublin City University, Universidad Politécnica de Madrid, PELIN LLC, Arizona State University, Universidad Complutense, University of Basel, Universidad Carlos III de Madrid, Consorzio CREATE, Demokritos National Centre for Scientific Research, Purdue University, Universite Libre de Bruxelles, School Services, ARTS, Department of Design, University of California Office of the President, Universidade de Sao Paulo, School Services, BIZ, Department of Information and Service Management, Lithuanian Energy Institute, HRS Fusion, Politecnico di Torino, University of Cassino, University of Electronic Science and Technology of China, Department of Electronics and Nanoengineering, Aalto-yliopisto, Aalto University, and Faculdade de Engenharia
Subjects: Technology, fusion, Física [Ciências exactas e naturais], Tokamak, Nuclear engineering, DIAGNOSTICS, 01 natural sciences, ILW, 010305 fluids & plasmas, law.invention, Ilw, [SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph], Plasma, H-Mode Plasmas, law, ITER, Disruption Prediction, COLLISIONALITY, EDGE LOCALIZED MODES, Diagnostics, Operation, JET, plasma, Nuclear and High Energy Physics, Condensed Matter Physics, Physics, Jet (fluid), JET, plasma, fusion, ITER, Divertor, Settore FIS/01 - Fisica Sperimentale, Fusion, Plasma and Space Physics, DENSITY PEAKING, Carbon Wall, H-MODE PLASMAS, [ SPI.MECA.MEFL ] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph], Density Peaking, Neutron transport, Facing Components, Collisionality, 114 Physical sciences, Física, Física, Nuclear physics, Physical sciences [Natural sciences], Fusion, plasma och rymdfysik, Pedestal, 0103 physical sciences, Nuclear fusion, ddc:530, Neutron, 010306 general physics, Fusion, Physics, Physical sciences, Nuclear and High Energy Physic, Edge Localized Modes, QC717, Física [Àrees temàtiques de la UPC], Reactors de fusió, Física, FACING COMPONENTS, Fusion reactors, Jet, CARBON WALL, DISRUPTION PREDICTION, OPERATION, ddc:600
Abstract: The 2014–2016 JET results are reviewed in the light of their significance for optimising the ITER research plan for the active and non-active operation. More than 60 h of plasma operation with ITER first wall materials successfully took place since its installation in 2011. New multi-machine scaling of the type I-ELM divertor energy flux density to ITER is supported by first principle modelling. ITER relevant disruption experiments and first principle modelling are reported with a set of three disruption mitigation valves mimicking the ITER setup. Insights of the L–H power threshold in Deuterium and Hydrogen are given, stressing the importance of the magnetic configurations and the recent measurements of fine-scale structures in the edge radial electric. Dimensionless scans of the core and pedestal confinement provide new information to elucidate the importance of the first wall material on the fusion performance. H-mode plasmas at ITER triangularity (H = 1 at ßN ~ 1.8 and n/nGW ~ 0.6) have been sustained at 2 MA during 5 s. The ITER neutronics codes have been validated on high performance experiments. Prospects for the coming D–T campaign and 14 MeV neutron calibration strategy are reviewed. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014–2018 under grant agreement No 633053. Peer Reviewed Article signat per 1.173 autors/es: X. Litaudon35, S. Abduallev39, M. Abhangi46, P. Abreu53, M. Afzal7, K.M. Aggarwal29, T. Ahlgren101, J.H. Ahn8, L. Aho-Mantila112, N. Aiba69, M. Airila112, R. Albanese105, V. Aldred7, D. Alegre93, E. Alessi45, P. Aleynikov55, A. Alfier12, A. Alkseev72, M. Allinson7, B. Alper7, E. Alves53, G. Ambrosino105, R. Ambrosino106, L. Amicucci90, V. Amosov88, E. Andersson Sundén22, M. Angelone90, M. Anghel85, C. Angioni62, L. Appel7, C. Appelbee7, P. Arena30, M. Ariola106, H. Arnichand8, S. Arshad41, A. Ash7, N. Ashikawa68, V. Aslanyan64, O. Asunta1, F. Auriemma12, Y. Austin7, L. Avotina103, M.D. Axton7, C. Ayres7, M. Bacharis24, A. Baciero57, D. Baião53, S. Bailey7, A. Baker7, I. Balboa7, M. Balden62, N. Balshaw7, R. Bament7, J.W. Banks7, Y.F. Baranov7, M.A. Barnard7, D. Barnes7, M. Barnes27, R. Barnsley55, A. Baron Wiechec7, L. Barrera Orte34, M. Baruzzo12, V. Basiuk8, M. Bassan55, R. Bastow7, A. Batista53, P. Batistoni90, R. Baughan7, B. Bauvir55, L. Baylor73, B. Bazylev56, J. Beal110, P.S. Beaumont7, M. Beckers39, B. Beckett7, A. Becoulet8, N. Bekris35, M. Beldishevski7, K. Bell7, F. Belli90, M. Bellinger7, É. Belonohy62, N. Ben Ayed7, N.A. Benterman7, H. Bergsåker42, J. Bernardo53, M. Bernert62, M. Berry7, L. Bertalot55, C. Besliu7, M. Beurskens63, B. Bieg61, J. Bielecki47, T. Biewer73, M. Bigi12, P. Bílková50, F. Binda22, A. Bisoffi31, J.P.S. Bizarro53, C. Björkas101, J. Blackburn7, K. Blackman7, T.R. Blackman7, P. Blanchard33, P. Blatchford7, V. Bobkov62, A. Boboc7, G. Bodnár113, O. Bogar18, I. Bolshakova60, T. Bolzonella12, N. Bonanomi97, F. Bonelli56, J. Boom62, J. Booth7, D. Borba35,53, D. Borodin39, I. Borodkina39, A. Botrugno90, C. Bottereau8, P. Boulting7, C. Bourdelle8, M. Bowden7, C. Bower7, C. Bowman110, T. Boyce7, C. Boyd7, H.J. Boyer7, J.M.A. Bradshaw7, V. Braic87, R. Bravanec40, B. Breizman107, S. Bremond8, P.D. Brennan7, S. Breton8, A. Brett7, S. Brezinsek39, M.D.J. Bright7, M. Brix7, W. Broeckx78, M. Brombin12, A. Brosławski65, D.P.D. Brown7, M. Brown7, E. Bruno55, J. Bucalossi8, J. Buch46, J. Buchanan7, M.A. Buckley7, R. Budny76, H. Bufferand8, M. Bulman7, N. Bulmer7, P. Bunting7, P. Buratti90, A. Burckhart62, A. Buscarino30, A. Busse7, N.K. Butler7, I. Bykov42, J. Byrne7, P. Cahyna50, G. Calabrò90, I. Calvo57, Y. Camenen4, P. Camp7, D.C. Campling7, J. Cane7, B. Cannas17, A.J. Capel7, P.J. Card7, A. Cardinali90, P. Carman7, M. Carr7, D. Carralero62, L. Carraro12, B.B. Carvalho53, I. Carvalho53, P. Carvalho53, F.J. Casson7, C. Castaldo90, N. Catarino53, J. Caumont7, F. Causa90, R. Cavazzana12, K. Cave-Ayland7, M. Cavinato12, M. Cecconello22, S. Ceccuzzi90, E. Cecil76, A. Cenedese12, R. Cesario90, C.D. Challis7, M. Chandler7, D. Chandra46, C.S. Chang76, A. Chankin62, I.T. Chapman7, S.C. Chapman28, M. Chernyshova49, G. Chitarin12, G. Ciraolo8, D. Ciric7, J. Citrin38, F. Clairet8, E. Clark7, M. Clark7, R. Clarkson7, D. Clatworthy7, C. Clements7, M. Cleverly7, J.P. Coad7, P.A. Coates7, A. Cobalt7, V. Coccorese105, V. Cocilovo90, S. Coda33, R. Coelho53, J.W. Coenen39, I. Coffey29, L. Colas8, S. Collins7, D. Conka103, S. Conroy22, N. Conway7, D. Coombs7, D. Cooper7, S.R. Cooper7, C. Corradino30, Y. Corre8, G. Corrigan7, S. Cortes53, D. Coster62, A.S. Couchman7, M.P. Cox7, T. Craciunescu86, S. Cramp7, R. Craven7, F. Crisanti90, G. Croci97, D. Croft7, K. Crombé15, R. Crowe7, N. Cruz53, G. Cseh113, A. Cufar81, A. Cullen7, M. Curuia85, A. Czarnecka49, H. Dabirikhah7, P. Dalgliesh7, S. Dalley7, J. Dankowski47, D. Darrow76, O. Davies7, W. Davis55,76, C. Day56, I.E. Day7, M. De Bock55, A. de Castro57, E. de la Cal57, E. de la Luna57, G. De Masi12, J. L. de Pablos57, G. De Temmerman55, G. De Tommasi105, P. de Vries55, K. Deakin7, J. Deane7, F. Degli Agostini12, R. Dejarnac50, E. Delabie73, N. den Harder38, R.O. Dendy7, J. Denis8, P. Denner39, S. Devaux62,104, P. Devynck8, F. Di Maio55, A. Di Siena62, C. Di Troia90, P. Dinca86, R. D’Inca62, B. Ding51, T. Dittmar39, H. Doerk62, R.P. Doerner9, T. Donné34, S.E. Dorling7, S. Dormido-Canto93, S. Doswon7, D. Douai8, P.T. Doyle7, A. Drenik62,81, P. Drewelow63, P. Drews39, Ph. Duckworth55, R. Dumont8, P. Dumortier58, D. Dunai113, M. Dunne62, I. Ďuran50, F. Durodié58, P. Dutta46, B. P. Duval33, R. Dux62, K. Dylst78, N. Dzysiuk22, P.V. Edappala46, J. Edmond7, A.M. Edwards7, J. Edwards7, Th. Eich62, A. Ekedahl8, R. El-Jorf7, C.G. Elsmore7, M. Enachescu84, G. Ericsson22, F. Eriksson16, J. Eriksson22, L.G. Eriksson36, B. Esposito90, S. Esquembri94, H.G. Esser39, D. Esteve8, B. Evans7, G.E. Evans7, G. Evison7, G.D. Ewart7, D. Fagan7, M. Faitsch62, D. Falie86, A. Fanni17, A. Fasoli33, J. M. Faustin33, N. Fawlk7, L. Fazendeiro53, N. Fedorczak8, R.C. Felton7, K. Fenton7, A. Fernades53, H. Fernandes53, J. Ferreira53, J.A. Fessey7, O. Février8, O. Ficker50, A. Field7, S. Fietz62, A. Figueiredo53, J. Figueiredo53,35, A. Fil8, P. Finburg7, M. Firdaouss8, U. Fischer56, L. Fittill7, M. Fitzgerald7, D. Flammini90, J. Flanagan7, C. Fleming7, K. Flinders7, N. Fonnesu90, J. M. Fontdecaba57, A. Formisano79, L. Forsythe7, L. Fortuna30, E. Fortuna-Zalesna19, M. Fortune7, S. Foster7, T. Franke34, T. Franklin7, M. Frasca30, L. Frassinetti42, M. Freisinger39, R. Fresa98, D. Frigione90, V. Fuchs50, D. Fuller35, S. Futatani6, J. Fyvie7, K. Gál34,62, D. Galassi2, K. Gałązka49, J. Galdon-Quiroga92, J. Gallagher7, D. Gallart6, R. Galvão10, X. Gao51, Y. Gao39, J. Garcia8, A. Garcia-Carrasco42, M. García-Muñoz92, J.-L. Gardarein3, L. Garzotti7, P. Gaudio95, E. Gauthier8, D.F. Gear7, S.J. Gee7, B. Geiger62, M. Gelfusa95, S. Gerasimov7, G. Gervasini45, M. Gethins7, Z. Ghani7, M. Ghate46, M. Gherendi86, J.C. Giacalone8, L. Giacomelli45, C.S. Gibson7, T. Giegerich56, C. Gil8, L. Gil53, S. Gilligan7, D. Gin54, E. Giovannozzi90, J.B. Girardo8, C. Giroud7, G. Giruzzi8, S. Glöggler62, J. Godwin7, J. Goff7, P. Gohil43, V. Goloborod’ko102, R. Gomes53, B. Gonçalves53, M. Goniche8, M. Goodliffe7, A. Goodyear7, G. Gorini97, M. Gosk65, R. Goulding76, A. Goussarov78, R. Gowland7, B. Graham7, M.E. Graham7, J. P. Graves33, N. Grazier7, P. Grazier7, N.R. Green7, H. Greuner62, B. Grierson76, F.S. Griph7, C. Grisolia8, D. Grist7, M. Groth1, R. Grove73, C.N. Grundy7, J. Grzonka19, D. Guard7, C. Guérard34, C. Guillemaut8,53, R. Guirlet8, C. Gurl7, H.H. Utoh69, L.J. Hackett7, S. Hacquin8,35, A. Hagar7, R. Hager76, A. Hakola112, M. Halitovs103, S.J. Hall7, S.P. Hallworth Cook7, C. Hamlyn-Harris7, K. Hammond7, C. Harrington7, J. Harrison7, D. Harting7, F. Hasenbeck39, Y. Hatano108, D.R. Hatch107, T.D.V. Haupt7, J. Hawes7, N.C. Hawkes7, J. Hawkins7, P. Hawkins7, P.W. Haydon7, N. Hayter7, S. Hazel7, P.J.L. Heesterman7, K. Heinola101, C. Hellesen22, T. Hellsten42, W. Helou8, O.N. Hemming7, T.C. Hender7, M. Henderson55, S.S. Henderson21, R. Henriques53, D. Hepple7, G. Hermon7, P. Hertout8, C. Hidalgo57, E.G. Highcock27, M. Hill7, J. Hillairet8, J. Hillesheim7, D. Hillis73, K. Hizanidis70, A. Hjalmarsson22, J. Hobirk62, E. Hodille8, C.H.A. Hogben7, G.M.D. Hogeweij38, A. Hollingsworth7, S. Hollis7, D.A. Homfray7, J. Horáček50, G. Hornung15, A.R. Horton7, L.D. Horton36, L. Horvath110, S.P. Hotchin7, M.R. Hough7, P.J. Howarth7, A. Hubbard64, A. Huber39, V. Huber39, T.M. Huddleston7, M. Hughes7, G.T.A. Huijsmans55, C.L. Hunter7, P. Huynh8, A.M. Hynes7, D. Iglesias7, N. Imazawa69, F. Imbeaux8, M. Imríšek50, M. Incelli109, P. Innocente12, M. Irishkin8, I. Ivanova-Stanik49, S. Jachmich58,35, A.S. Jacobsen83, P. Jacquet7, J. Jansons103, A. Jardin8, A. Järvinen1, F. Jaulmes38, S. Jednoróg49, I. Jenkins7, C. Jeong20, I. Jepu86, E. Joffrin8, R. Johnson7, T. Johnson42, Jane Johnston7, L. Joita7, G. Jones7, T.T.C. Jones7, K.K. Hoshino69, A. Kallenbach62, K. Kamiya69, J. Kaniewski7, A. Kantor7, A. Kappatou62, J. Karhunen1, D. Karkinsky7, I. Karnowska7, M. Kaufman73, G. Kaveney7, Y. Kazakov58, V. Kazantzidis70, D.L. Keeling7, T. Keenan7, J. Keep7, M. Kempenaars7, C. Kennedy7, D. Kenny7, J. Kent7, O.N. Kent7, E. Khilkevich54, H.T. Kim35, H.S. Kim80, A. Kinch7, C. king7, D. King7, R.F. King7, D.J. Kinna7, V. Kiptily7, A. Kirk7, K. Kirov7, A. Kirschner39, G. Kizane103, C. Klepper73, A. Klix56, P. Knight7, S.J. Knipe7, S. Knott96, T. Kobuchi69, F. Köchl111, G. Kocsis113, I. Kodeli81, L. Kogan7, D. Kogut8, S. Koivuranta112, Y. Kominis70, M. Köppen39, B. Kos81, T. Koskela1, H.R. Koslowski39, M. Koubiti4, M. Kovari7, E. Kowalska-Strzęciwilk49, A. Krasilnikov88, V. Krasilnikov88, N. Krawczyk49, M. Kresina8, K. Krieger62, A. Krivska58, U. Kruezi7, I. Książek48, A. Kukushkin72, A. Kundu46, T. Kurki-Suonio1, S. Kwak20, R. Kwiatkowski65, O.J. Kwon13, L. Laguardia45, A. Lahtinen101, A. Laing7, N. Lam7, H.T. Lambertz39, C. Lane7, P.T. Lang62, S. Lanthaler33, J. Lapins103, A. Lasa101, J.R. Last7, E. Łaszyńska49, R. Lawless7, A. Lawson7, K.D. Lawson7, A. Lazaros70, E. Lazzaro45, J. Leddy110, S. Lee66, X. Lefebvre7, H.J. Leggate32, J. Lehmann7, M. Lehnen55, D. Leichtle41, P. Leichuer7, F. Leipold55,83, I. Lengar81, M. Lennholm36, E. Lerche58, A. Lescinskis103, S. Lesnoj7, E. Letellier7, M. Leyland110, W. Leysen78, L. Li39, Y. Liang39, J. Likonen112, J. Linke39, Ch. Linsmeier39, B. Lipschultz110, G. Liu55, Y. Liu51, V.P. Lo Schiavo105, T. Loarer8, A. Loarte55, R.C. Lobel7, B. Lomanowski1, P.J. Lomas7, J. Lönnroth1,35, J. M. López94, J. López-Razola57, R. Lorenzini12, U. Losada57, J.J. Lovell7, A.B. Loving7, C. Lowry36, T. Luce43, R.M.A. Lucock7, A. Lukin74, C. Luna5, M. Lungaroni95, C.P. Lungu86, M. Lungu86, A. Lunniss110, I. Lupelli7, A. Lyssoivan58, N. Macdonald7, P. Macheta7, K. Maczewa7, B. Magesh46, P. Maget8, C. Maggi7, H. Maier62, J. Mailloux7, T. Makkonen1, R. Makwana46, A. Malaquias53, A. Malizia95, P. Manas4, A. Manning7, M.E. Manso53, P. Mantica45, M. Mantsinen6, A. Manzanares91, Ph. Maquet55, Y. Marandet4, N. Marcenko88, C. Marchetto45, O. Marchuk39, M. Marinelli95, M. Marinucci90, T. Markovič50, D. Marocco90, L. Marot26, C.A. Marren7, R. Marshal7, A. Martin7, Y. Martin33, A. Martín de Aguilera57, F.J. Martínez93, J. R. Martín-Solís14, Y. Martynova39, S. Maruyama55, A. Masiello12, M. Maslov7, S. Matejcik18, M. Mattei79, G.F. Matthews7, F. Maviglia11, M. Mayer62, M.L. Mayoral34, T. May-Smith7, D. Mazon8, C. Mazzotta90, R. McAdams7, P.J. McCarthy96, K.G. McClements7, O. McCormack12, P.A. McCullen7, D. McDonald34, S. McIntosh7, R. McKean7, J. McKehon7, R.C. Meadows7, A. Meakins7, F. Medina57, M. Medland7, S. Medley7, S. Meigh7, A.G. Meigs7, G. Meisl62, S. Meitner73, L. Meneses53, S. Menmuir7,42, K. Mergia71, I.R. Merrigan7, Ph. Mertens39, S. Meshchaninov88, A. Messiaen58, H. Meyer7, S. Mianowski65, R. Michling55, D. Middleton-Gear7, J. Miettunen1, F. Militello7, E. Militello-Asp7, G. Miloshevsky77, F. Mink62, S. Minucci105, Y. Miyoshi69, J. Mlynář50, D. Molina8, I. Monakhov7, M. Moneti109, R. Mooney7, S. Moradi37, S. Mordijck43, L. Moreira7, R. Moreno57, F. Moro90, A.W. Morris7, J. Morris7, L. Moser26, S. Mosher73, D. Moulton7,1, A. Murari12,35, A. Muraro45, S. Murphy7, N.N. Asakura69, Y.S. Na80, F. Nabais53, R. Naish7, T. Nakano69, E. Nardon8, V. Naulin83, M.F.F. Nave53, I. Nedzelski53, G. Nemtsev88, F. Nespoli33, A. Neto41, R. Neu62, V.S. Neverov72, M. Newman7, K.J. Nicholls7, T. Nicolas33, A.H. Nielsen83, P. Nielsen12, E. Nilsson8, D. Nishijima99, C. Noble7, M. Nocente97, D. Nodwell7, K. Nordlund101, H. Nordman16, R. Nouailletas8, I. Nunes53, M. Oberkofler62, T. Odupitan7, M.T. Ogawa69, T. O’Gorman7, M. Okabayashi76, R. Olney7, O. Omolayo7, M. O’Mullane21, J. Ongena58, F. Orsitto11, J. Orszagh18, B.I. Oswuigwe7, R. Otin7, A. Owen7, R. Paccagnella12, N. Pace7, D. Pacella90, L.W. Packer7, A. Page7, E. Pajuste103, S. Palazzo30, S. Pamela7, S. Panja46, P. Papp18, R. Paprok50, V. Parail7, M. Park66, F. Parra Diaz27, M. Parsons73, R. Pasqualotto12, A. Patel7, S. Pathak46, D. Paton7, H. Patten33, A. Pau17, E. Pawelec48, C. Paz Soldan43, A. Peackoc36, I.J. Pearson7, S.-P. Pehkonen112, E. Peluso95, C. Penot55, A. Pereira57, R. Pereira53, P.P. Pereira Puglia7, C. Perez von Thun35,39, S. Peruzzo12, S. Peschanyi56, M. Peterka50, P. Petersson42, G. Petravich113, A. Petre84, N. Petrella7, V. Petržilka50, Y. Peysson8, D. Pfefferlé33, V. Philipps39, M. Pillon90, G. Pintsuk39, P. Piovesan12, A. Pires dos Reis52, L. Piron7, A. Pironti105, F. Pisano17, R. Pitts55, F. Pizzo79, V. Plyusnin53, N. Pomaro12, O.G. Pompilian86, P.J. Pool7, S. Popovichev7, M.T. Porfiri90, C. Porosnicu86, M. Porton7, G. Possnert22, S. Potzel62, T. Powell7, J. Pozzi7, V. Prajapati46, R. Prakash46, G. Prestopino95, D. Price7, M. Price7, R. Price7, P. Prior7, R. Proudfoot7, G. Pucella90, P. Puglia52, M.E. Puiatti12, D. Pulley7, K. Purahoo7, Th. Pütterich62, E. Rachlew25, M. Rack39, R. Ragona58, M.S.J. Rainford7, A. Rakha6, G. Ramogida90, S. Ranjan46, C.J. Rapson62, J.J. Rasmussen83, K. Rathod46, G. Rattá57, S. Ratynskaia82, G. Ravera90, C. Rayner7, M. Rebai97, D. Reece7, A. Reed7, D. Réfy113, B. Regan7, J. Regaña34, M. Reich62, N. Reid7, F. Reimold39, M. Reinhart34, M. Reinke110,73, D. Reiser39, D. Rendell7, C. Reux8, S.D.A. Reyes Cortes53, S. Reynolds7, V. Riccardo7, N. Richardson7, K. Riddle7, D. Rigamonti97, F.G. Rimini7, J. Risner73, M. Riva90, C. Roach7, R.J. Robins7, S.A. Robinson7, T. Robinson7, D.W. Robson7, R. Roccella55, R. Rodionov88, P. Rodrigues53, J. Rodriguez7, V. Rohde62, F. Romanelli90, M. Romanelli7, S. Romanelli7, J. Romazanov39, S. Rowe7, M. Rubel42, G. Rubinacci105, G. Rubino12, L. Ruchko52, M. Ruiz94, C. Ruset86, J. Rzadkiewicz65, S. Saarelma7, R. Sabot8, E. Safi101, P. Sagar7, G. Saibene41, F. Saint-Laurent8, M. Salewski83, A. Salmi112, R. Salmon7, F. Salzedas53, D. Samaddar7, U. Samm39, D. Sandiford7, P. Santa46, M.I.K. Santala1, B. Santos53, A. Santucci90, F. Sartori41, R. Sartori41, O. Sauter33, R. Scannell7, T. Schlummer39, K. Schmid62, V. Schmidt12, S. Schmuck7, M. Schneider8, K. Schöpf102, D. Schwörer32, S.D. Scott76, G. Sergienko39, M. Sertoli62, A. Shabbir15, S.E. Sharapov7, A. Shaw7, R. Shaw7, H. Sheikh7, A. Shepherd7, A. Shevelev54, A. Shumack38, G. Sias17, M. Sibbald7, B. Sieglin62, S. Silburn7, A. Silva53, C. Silva53, P.A. Simmons7, J. Simpson7, J. Simpson-Hutchinson7, A. Sinha46, S.K. Sipilä1, A.C.C. Sips36, P. Sirén112, A. Sirinelli55, H. Sjöstrand22, M. Skiba22, R. Skilton7, K. Slabkowska49, B. Slade7, N. Smith7, P.G. Smith7, R. Smith7, T.J. Smith7, M. Smithies110, L. Snoj81, S. Soare85, E. R. Solano35,57, A. Somers32, C. Sommariva8, P. Sonato12, A. Sopplesa12, J. Sousa53, C. Sozzi45, S. Spagnolo12, T. Spelzini7, F. Spineanu86, G. Stables7, I. Stamatelatos71, M.F. Stamp7, P. Staniec7, G. Stankūnas59, C. Stan-Sion84, M.J. Stead7, E. Stefanikova42, I. Stepanov58, A.V. Stephen7, M. Stephen46, A. Stevens7, B.D. Stevens7, J. Strachan76, P. Strand16, H.R. Strauss44, P. Ström42, G. Stubbs7, W. Studholme7, F. Subba75, H.P. Summers21, J. Svensson63, Ł. Świderski65, T. Szabolics113, M. Szawlowski49, G. Szepesi7, T.T. Suzuki69, B. Tál113, T. Tala112, A.R. Talbot7, S. Talebzadeh95, C. Taliercio12, P. Tamain8, C. Tame7, W. Tang76, M. Tardocchi45, L. Taroni12, D. Taylor7, K.A. Taylor7, D. Tegnered16, G. Telesca15, N. Teplova54, D. Terranova12, D. Testa33, E. Tholerus42, J. Thomas7, J.D. Thomas7, P. Thomas55, A. Thompson7, C.-A. Thompson7, V.K. Thompson7, L. Thorne7, A. Thornton7, A.S. Thrysøe83, P.A. Tigwell7, N. Tipton7, I. Tiseanu86, H. Tojo69, M. Tokitani67, P. Tolias82, M. Tomeš50, P. Tonner7, M. Towndrow7, P. Trimble7, M. Tripsky58, M. Tsalas38, P. Tsavalas71, D. Tskhakaya jun102, I. Turner7, M.M. Turner32, M. Turnyanskiy34, G. Tvalashvili7, S.G.J. Tyrrell7, A. Uccello45, Z. Ul-Abidin7, J. Uljanovs1, D. Ulyatt7, H. Urano69, I. Uytdenhouwen78, A.P. Vadgama7, D. Valcarcel7, M. Valentinuzzi8, M. Valisa12, P. Vallejos Olivares42, M. Valovic7, M. Van De Mortel7, D. Van Eester58, W. Van Renterghem78, G.J. van Rooij38, J. Varje1, S. Varoutis56, S. Vartanian8, K. Vasava46, T. Vasilopoulou71, J. Vega57, G. Verdoolaege58, R. Verhoeven7, C. Verona95, G. Verona Rinati95, E. Veshchev55, N. Vianello45, J. Vicente53, E. Viezzer62,92, S. Villari90, F. Villone100, P. Vincenzi12, I. Vinyar74, B. Viola90, A. Vitins103, Z. Vizvary7, M. Vlad86, I. Voitsekhovitch34, P. Vondráček50, N. Vora7, T. Vu8, W.W. Pires de Sa52, B. Wakeling7, C.W.F. Waldon7, N. Walkden7, M. Walker7, R. Walker7, M. Walsh55, E. Wang39, N. Wang39, S. Warder7, R.J. Warren7, J. Waterhouse7, N.W. Watkins28, C. Watts55, T. Wauters58, A. Weckmann42, J. Weiland23, H. Weisen33, M. Weiszflog22, C. Wellstood7, A.T. West7, M.R. Wheatley7, S. Whetham7, A.M. Whitehead7, B.D. Whitehead7, A.M. Widdowson7, S. Wiesen39, J. Wilkinson7, J. Williams7, M. Williams7, A.R. Wilson7, D.J. Wilson7, H.R. Wilson110, J. Wilson7, M. Wischmeier62, G. Withenshaw7, A. Withycombe7, D.M. Witts7, D. Wood7, R. Wood7, C. Woodley7, S. Wray7, J. Wright7, J.C. Wright64, J. Wu89, S. Wukitch64, A. Wynn110, T. Xu7, D. Yadikin16, W. Yanling39, L. Yao89, V. Yavorskij102, M.G. Yoo80, C. Young7, D. Young7, I.D. Young7, R. Young7, J. Zacks7, R. Zagorski49, F.S. Zaitsev18, R. Zanino75, A. Zarins103, K.D. Zastrow7, M. Zerbini90, W. Zhang62, Y. Zhou42, E. Zilli12, V. Zoita86, S. Zoletnik113, I. Zychor65 and JET Contributorsa // EUROfusion Consortium JET, Culham Science Centre, Abingdon, OX14 3DB, United Kingdom / 1 Aalto University, PO Box 14100, FIN-00076 Aalto, Finland / 2 Aix Marseille Université, CNRS, Centrale Marseille, M2P2 UMR 7340, 13451, Marseille, France / 3 Aix-Marseille Université, CNRS, IUSTI UMR 7343, 13013 Marseille, France / 4 Aix-Marseille Université, CNRS, PIIM, UMR 7345, 13013 Marseille, France / 5 Arizona State University, Tempe, AZ, United States of America / 6 Barcelona Supercomputing Center, Barcelona, Spain / 7 CCFE, Culham Science Centre, Abingdon, Oxon, OX14 3DB, United Kingdom / 8 CEA, IRFM, F-13108 Saint Paul Lez Durance, France / 9 Center for Energy Research, University of California at San Diego, La Jolla, CA 92093, United States of America / 10 Centro Brasileiro de Pesquisas Fisicas, Rua Xavier Sigaud, 160, Rio de Janeiro CEP 22290-180, Brazil / 11 Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 12 Consorzio RFX, corso Stati Uniti 4, 35127 Padova, Italy / 13 Daegu University, Jillyang, Gyeongsan, Gyeongbuk 712-174, Republic of Korea / 14 Departamento de Física, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain / 15 Department of Applied Physics UG (Ghent University) St-Pietersnieuwstraat 41 B-9000 Ghent, Belgium / 16 Department of Earth and Space Sciences, Chalmers University of Technology, SE-41296 Gothenburg, Sweden / 17 Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi 09123, Cagliari, Italy / 18 Department of Experimental Physics, Faculty of Mathematics, Physics and Informatics Comenius University Mlynska dolina F2, 84248 Bratislava, Slovakia / 19 Department of Materials Science, Warsaw University of Technology, PL-01-152 Warsaw, Poland / 20 Department of Nuclear and Quantum Engineering, KAIST, Daejeon 34141, Korea / 21 Department of Physics and Applied Physics, University of Strathclyde, Glasgow, G4 ONG, United Kingdom / 22 Department of Physics and Astronomy, Uppsala University, SE-75120 Uppsala, Sweden / 23 Department of Physics, Chalmers University of Technology, SE-41296 Gothenburg, Sweden / 24 Department of Physics, Imperial College London, London, SW7 2AZ, United Kingdom / 25 Department of Physics, SCI, KTH, SE-10691 Stockholm, Sweden / 26 Department of Physics, University of Basel, Basel, Switzerland / 27 Department of Physics, University of Oxford, Oxford, OX1 2JD, United Kingdom / 28 Department of Physics, University of Warwick, Coventry, CV4 7AL, United Kingdom / 29 Department of Pure and Applied Physics, Queens University, Belfast, BT7 1NN, United Kingdom / 30 Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, 95125 Catania, Italy / 31 Dipartimento di Ingegneria Industriale, University of Trento, Trento, Italy / 32 Dublin City University (DCU), Dublin, Ireland / 33 Ecole Polytechnique Fédérale de Lausanne (EPFL), Swiss Plasma Center (SPC), CH-1015 Lausanne, Switzerland / 34 EUROfusion Programme Management Unit, Boltzmannstr. 2, 85748 Garching, Germany / 35 EUROfusion Programme Management Unit, Culham Science Centre, Culham, OX14 3DB, United Kingdom / 36 European Commission, B-1049 Brussels, Belgium / 37 Fluid and Plasma Dynamics, ULB—Campus Plaine—CP 231 Boulevard du Triomphe, 1050 Bruxelles, Belgium / 38 FOM Institute DIFFER, Eindhoven, Netherlands / 39 Forschungszentrum Jülich GmbH, Institut für Energie- und Klimaforschung—Plasmaphysik, 52425 Jülich, Germany / 40 Fourth State Research, 503 Lockhart Dr, Austin, TX, United States of America / 41 Fusion for Energy Joint Undertaking, Josep Pl. 2, Torres Diagonal Litoral B3, 08019, Barcelona, Spain / 42 Fusion Plasma Physics, EES, KTH, SE-10044 Stockholm, Sweden / 43 General Atomics, PO Box 85608, San Diego, CA 92186-5608, United States of America / 44 HRS Fusion, West Orange, NJ, United States of America / 45 IFP-CNR, via R. Cozzi 53, 20125 Milano, Italy / 46 Institute for Plasma Research, Bhat, Gandhinagar-382 428, Gujarat State, India / 47 Institute of Nuclear Physics, Radzikowskiego 152, 31-342 Kraków, Poland / 48 Institute of Physics, Opole University, Oleska 48, 45-052 Opole, Poland / 49 Institute of Plasma Physics and Laser Microfusion, Hery 23, 01-497 Warsaw, Poland / 50 Institute of Plasma Physics AS CR, Za Slovankou 1782/3, 182 00 Praha 8, Czechia / 51 Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, People’s Republic of China / 52 Instituto de Física, Universidade de São Paulo, Rua do Matão Travessa R Nr.187 CEP 05508-090 Cidade Universitária, São Paulo, Brasil / 53 Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal / 54 Ioffe Physico-Technical Institute, 26 Politekhnicheskaya, St Petersburg 194021, Russian Federation / 55 ITER Organization, Route de Vinon, CS 90 046, 13067 Saint Paul Lez Durance, France / 56 Karlsruhe Institute of Technology, PO Box 3640, D-76021 Karlsruhe, Germany / 57 Laboratorio Nacional de Fusión, CIEMAT, Madrid, Spain / 58 Laboratory for Plasma Physics Koninklijke Militaire School—Ecole Royale Militaire, Renaissancelaan 30 Avenue de la Renaissance B-1000, Brussels, Belgium / 59 Lithuanian energy institute, Breslaujos g. 3, LT-44403, Kaunas, Lithuania / 60 Magnetic Sensor Laboratory, Lviv Polytechnic National University, Lviv, Ukraine / 61 Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland / 62 Max-Planck-Institut für Plasmaphysik, D-85748 Garching, Germany / 63 Max-Planck-Institut für Plasmaphysik, Teilinsitut Greifswald, D-17491 Greifswald, Germany / 64 MIT Plasma Science and Fusion Centre, Cambridge, MA 02139, United States of America / 65 National Centre for Nuclear Research (NCBJ), 05-400 Otwock-Świerk, Poland / 66 National Fusion Research Institute (NFRI), 169-148 Gwahak-ro, Yuseong-gu, Daejeon 305-806, Republic of Korea / 67 National Institute for Fusion Science, Oroshi, Toki, Gifu 509-5292, Japan / 68 National Institute for Fusion Science, Toki, 509-5292, Japan / 69 National Institutes for Quantum and Radiological Science and Technology, Naka, Ibaraki 311-0193, Japan / 70 National Technical University of Athens, Iroon Politechniou 9, 157 73 Zografou, Athens, Greece / 71 NCSR ‘Demokritos’, 153 10, Agia Paraskevi Attikis, Greece / 72 NRC Kurchatov Institute, 1 Kurchatov Square, Moscow 123182, Russian Federation / 73 Oak Ridge National Laboratory, Oak Ridge, TN 37831-6169, United States of America / 74 PELIN LLC, 27a, Gzhatskaya Ulitsa, Saint Petersburg, 195220, Russian Federation / 75 Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy / 76 Princeton Plasma Physics Laboratory, James Forrestal Campus, Princeton, NJ 08543, United States of America / 77 Purdue University, 610 Purdue Mall, West Lafayette, IN 47907, United States of America / 78 SCK-CEN, Nuclear Research Centre, 2400 Mol, Belgium / 79 Second University of Napoli, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 80 Seoul National University, Shilim-Dong, Gwanak-Gu, Republic of Korea / 81 Slovenian Fusion Association (SFA), Jozef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia / 82 Space and Plasma Physics, EES, KTH SE-100 44 Stockholm, Sweden / 83 Technical University of Denmark, Department of Physics, Bldg 309, DK-2800 Kgs Lyngby, Denmark / 84 The ‘Horia Hulubei’ National Institute for Physics and Nuclear Engineering, Magurele-Bucharest, Romania / 85 The National Institute for Cryogenics and Isotopic Technology, Ramnicu Valcea, Romania / 86 The National Institute for Laser, Plasma and Radiation Physics, Magurele-Bucharest, Romania / 87 The National Institute for Optoelectronics, Magurele-Bucharest, Romania / 88 Troitsk Insitute of Innovating and Thermonuclear Research (TRINITI), Troitsk 142190, Moscow Region, Russian Federation / 89 University of Electronic Science and Technology of China, Chengdu, People’s Republic of China / 90 Unità Tecnica Fusione, ENEA C. R. Frascati, via E. Fermi 45, 00044 Frascati (Roma), Italy / 91 Universidad Complutense de Madrid, Madrid, Spain / 92 Universidad de Sevilla, Sevilla, Spain / 93 Universidad Nacional de Educación a Distancia, Madrid, Spain / 94 Universidad Politécnica de Madrid, Grupo I2A2, Madrid, Spain / 95 Università di Roma Tor Vergata, Via del Politecnico 1, Roma, Italy / 96 University College Cork (UCC), Ireland / 97 University Milano-Bicocca, piazza della Scienza 3, 20126 Milano, Italy / 98 University of Basilicata, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 99 University of California, 1111 Franklin St., Oakland, CA 94607, United States of America / 100 University of Cassino, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 101 University of Helsinki, PO Box 43, FI-00014 University of Helsinki, Finland / 102 University of Innsbruck, Fusion@Österreichische Akademie der Wissenschaften (ÖAW), Innsbruck, Austria / 103 University of Latvia, 19 Raina Blvd., Riga, LV 1586, Latvia / 104 University of Lorraine, CNRS, UMR7198, YIJL, Nancy, France / 105 University of Napoli ‘Federico II’, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 106 University of Napoli Parthenope, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 107 University of Texas at Austin, Institute for Fusion Studies, Austin, TX 78712, United States of America / 108 University of Toyama, Toyama, 930-8555, Japan / 109 University of Tuscia, DEIM, Via del Paradiso 47, 01100 Viterbo, Italy / 110 University of York, Heslington, York YO10 5DD, United Kingdom / 111 Vienna University of Technology, Fusion@Österreichische Akademie der Wissenschaften (ÖAW), Austria / 112 VTT Technical Research Centre of Finland, PO Box 1000, FIN-02044 VTT, Finland / 113 Wigner Research Centre for Physics, PO Box 49, H-1525 Budapest, Hungary
Published: 2017
Full Text: View/download PDF

49. A first analysis of JET plasma profile based indicators for disruption prediction and avoidance

Author: Pau Alessandro, Fanni Alessandra, Murari Andrea, Cannas Barbara, Rimini Fernanda, Pisano Gianluca, Sias Giuliana, JET Contributors, Baruzzo Matteo, Tsalas Maximos, Sparapani Paolo, and de Vries Peter
Subjects: disruption prediction, JET, nuclear fusion
Abstract: Disruptive events still pose a serious problem for the protection of in-vessel components of large size tokamak devices, representing therefore a key aspect to be considered for the design of next step fusion devices such as ITER and DEMO. If an efficient mitigation is strongly required to avoid damage and preserve the structural integrity of the machine, efficient avoidance schemes are needed to possibly bring the plasma back to a safe operating condition. In this framework, disruption prediction plays a key role and in the last few years a substantial effort has been devoted to developing more sophisticated prediction systems and improving their performance both in terms of success rate and warning time. Many of the presently developed disruption predictors mainly rely on MHD markers related to still rotating modes and, especially, to locked modes, which are basically the final precursor of most of the disruptions. Nevertheless, in many cases the detection warning time is still unsatisfactory with respect to avoidance requirements, and a significant step forward needs to be taken. This work deals with the development of "plasma profile based indicators" for disruption prediction and avoidance in JET, where parameterized peaking factors have been implemented for electron temperature, density and plasma radiation profiles. The basic interplay of the time evolution of different profiles will be described in relation to the phenomenology characterizing specific disruption types together with the relevant time scales. Furthermore, a statistical analysis aiming to describe differences and boundaries between the safe and the disruptive space as well as among specific types of disruptions will be presented, discussing the implications in terms of disruption prediction and avoidance. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission and the ITER Organization.
Published: 2017

50. Review of disruption predictors in nuclear fusion: Classical, from scratch and anomaly detection approaches

Author: Mariano Ruiz, Eduardo Barrera, Raul Moreno, A. Pereira, Jesús Vega, Sebastián Dormido-Canto, G.A. Rattá, A. Murari, and S. Esquembri
Subjects: Disruption prediction, Engineering, business.industry, Process (computing), Novelty, Anomaly detection, Machine learning, computer.software_genre, 01 natural sciences, 010305 fluids & plasmas, Constant false alarm rate, Data flow diagram, ALARM, Scratch, Anticipation (artificial intelligence), 0103 physical sciences, Nuclear fusion, Artificial intelligence, 010306 general physics, business, computer, Simulation, computer.programming_language
Abstract: Disruption predictors are implemented as data-driven models obtained from machine learning techniques. These data-driven models are deduced from a training process with thousands of discharges (both disruptive and non-disruptive). ITER or DEMO, the next step devices cannot afford to wait for hundreds of disruptions to start predicting. A novelty approach for disruption prediction is to avoid the use of past discharges for learning purposes. The objective is to learn in every discharge how a safe plasma evolution is and to trigger an alarm when anomalies in the data flow appear. Of course, these anomalies have to be signatures of the phenomenon's precursors. By applying these ideas in JET to a dataset of more than 1700 non-disruptive shots and more than 550 that ended in a disruption, the success rate is about 90% and the false alarm rate is slightly above 7%. On average, the alarm is triggered with an anticipation time above 200 ms.
Published: 2016
Full Text: View/download PDF

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Database

Publisher

88 results on '"Disruption Prediction"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources