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122 results on '"Gu, Xiwen"'

1. A Digital-Twin Assisted Simulation for the Electromechanical System of Aircraft

Author

Shan, Dan, Gu, Xiwen, Huang, Qinni, Lan, Ying, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Fu, Song, editor

Published
2024
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2. An Adaptive System Identification Framework for Early Detection of Themoacoustic Oscillation in Gas Turbine Combustor

Author

Sun, Jiahao, Gu, Xiwen, Yang, Shixi, Gu, Tingwei, Zang, Peng, IFToMM, Series Editor, Ceccarelli, Marco, Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Ball, Andrew D., editor, Ouyang, Huajiang, editor, Sinha, Jyoti K., editor, and Wang, Zuolu, editor

Published
2024
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3. A Digital-Twin Assisted Performance Prediction Model for Industrial Gas Turbines

Author

Huang, Qinni, Gu, Xiwen, Shao, Jianwei, Yang, Shixi, IFToMM, Series Editor, Ceccarelli, Marco, Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Ball, Andrew D., editor, Ouyang, Huajiang, editor, Sinha, Jyoti K., editor, and Wang, Zuolu, editor

Published
2024
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12. Dynamic Behavior Analysis of a Rotor-Bearing-Squeeze Film Damper Coupling System with Bearing Outer Race Localized Defect

Author

Zhou, Chen, He, Jun, Yang, Shixi, Sui, Yongfeng, Xiong, Xin, Gu, Xiwen, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Rizzo, Piervincenzo, editor, and Milazzo, Alberto, editor

Published
2023
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22. A hybrid modeling method of fatigue crack growth for gas turbine blades under combined high and low cycle fatigue loadings.

Author

Yang, Shixi, Huang, Qinni, Gu, Xiwen, Lan, Jibing, and Sui, Yongfeng

Abstract

Fatigue crack is one of the major faults of the gas turbine blades. Previous modeling studies on fatigue crack growth of blades mainly focus on a single numerical or analytical approach considering different sizes of cracks under various fatigue loadings. However, the actual crack propagation process is time-varying. The iteration and update of the model are necessary to evaluate the fatigue life of blades precisely. This paper proposes a hybrid modeling method to study blade crack growth under combined high and low cycle fatigue (CCF) loadings. The method emphasizes the combination and interaction between the finite element (FE) numerical simulation and the analytical calculation based on the fracture mechanics model. The current crack propagation length of the blade FE model is calculated from the crack growth rate obtained by the stress intensity factor (SIF) range according to Paris law. Then, a new SIF range is resolved from the FE model with the updated crack length. The proposed method is verified by the first-stage compressor blade from an in-service gas turbine. Results show that the crack growth is faster using the proposed hybrid modeling method than the traditional method. The update of the SIF range under CCF loadings cannot be ignored when predicting the fatigue life of the blade. Also, a sensitivity analysis is carried out. Suggestions are given on how to set the crack extension length at each stage during modeling, especially when the blade approaches failure. [ABSTRACT FROM AUTHOR]

Published
2024
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23. IDH1 mutation inhibits differentiation of astrocytes and glioma cells with low oxoglutarate dehydrogenase expression by disturbing α-ketoglutarate-related metabolism and epigenetic modification

Author

Zhao, Yuanlin, primary, Yang, Ying, additional, Yang, Risheng, additional, Sun, Chao, additional, Gao, Xing, additional, Gu, Xiwen, additional, Yuan, Yuan, additional, Nie, Yating, additional, Xu, Shenhui, additional, Han, Ruili, additional, Zhang, Lijun, additional, Li, Jing, additional, Hu, Peizhen, additional, Wang, Yingmei, additional, Chen, Huangtao, additional, Cao, Xiangmei, additional, Wu, Jing, additional, Wang, Zhe, additional, Gu, Yu, additional, and Ye, Jing, additional

Published
2024
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25. Mutational profiling of mitochondrial DNA reveals an epithelial ovarian cancer‐specific evolutionary pattern contributing to high oxidative metabolism

Author

Xie, Fanfan, primary, Guo, Wenjie, additional, Wang, Xingguo, additional, Zhou, Kaixiang, additional, Guo, Shanshan, additional, Liu, Yang, additional, Sun, Tianlei, additional, Li, Shengjing, additional, Xu, Zhiyang, additional, Yuan, Qing, additional, Zhang, Huanqin, additional, Gu, Xiwen, additional, Xing, Jinliang, additional, and Liu, Shujuan, additional

Published
2024
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31. Model-informed Charging Policymaking: How does modeling evidence influence EV charging infrastructure policymaking in the UK and the Netherlands?

Author

GU, XIWEN (author) and GU, XIWEN (author)

Abstract

The establishment of a well-developed charging infrastructure is imperative for the broader adoption of Electric Vehicle (EV) and necessitates the formulation of an effective charging infrastructure policy. To navigate the intricacies involved in the policymaking process, the incorporation of EV charging models can be advantageous. Existing research indicates that models have a significant impact on facilitating policymaking in the broader energy sector. Nevertheless, it remains unclear whether computer-based models exert a similar influence on the EV charging policies. Previous studies lack comprehensive insights into the practical application of models in EV charging policy processes and the resultant policy modifications due to the unique attributes of both EV charging models and policies. Furthermore, there exists a lack of systematic understanding regarding the utilization of charging infrastructure models. Given these gaps in knowledge, this research aims to investigate the following question: How does modeling evidence influence EV charging infrastructure policymaking in the UK and the Netherlands? This study finds that EV charging models have exerted substantial influence across various stages of local policy cycles, significantly shaping decision-making processes. Such impact has pre- dominantly concentrated on the practical and operational aspects of the models, primarily concerning the optimal number and spatial distribution of charging points. However, there remains a noticeable lack of attention to strategic considerations pertaining to broader energy transition and green transport initiatives. This oversight is particularly evident in the insufficient exploration of how EV charging in- frastructure can be effectively integrated into a more extensive and long-term blueprint. This research highlights the need for a strategic-level approach to comprehend the interplay between EV charging networks and the larger energy transition agenda, encompassing t, Management of Technology (MoT)

Published
2023

33. Mutational signature of mtDNA confers mechanistic insight into oxidative metabolism remodeling in colorectal cancer

Author

Guo, Wenjie, primary, Liu, Yang, additional, Ji, Xiaoying, additional, Guo, Shanshan, additional, Xie, Fanfan, additional, Chen, Yanxing, additional, Zhou, Kaixiang, additional, Zhang, Huanqin, additional, Peng, Fan, additional, Wu, Dan, additional, Wang, Zhenni, additional, Guo, Xu, additional, zhao, Qi, additional, Gu, Xiwen, additional, and Xing, Jinliang, additional

Published
2023
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37. mitoSomatic: a tool for accurate identification of mitochondrial DNA somatic mutations without paired controls.

Author

Guo, Wenjie, Liu, Yang, Su, Liping, Guo, Shanshan, Xie, Fanfan, Ji, Xiaoying, Zhou, Kaixiang, Guo, Xu, Gu, Xiwen, and Xing, Jinliang

Abstract

Mitochondrial DNA (mtDNA) somatic mutations play important roles in the initiation and progression of cancer. Although next‐generation sequencing (NGS) of paired tumor and control samples has become a common practice to identify tumor‐specific mtDNA mutations, the unique nature of mtDNA and NGS‐associated sequencing bias could cause false‐positive/‐negative somatic mutation calling. Additionally, there are clinical scenarios where matched control tissues are unavailable for comparison. Therefore, a novel approach for accurately identifying somatic mtDNA variants is greatly needed, particularly in the absence of matched controls. In this study, the ground truth mtDNA variants orthogonally validated by triple‐paired tumor, adjacent nontumor, and blood samples were used to develop mitoSomatic, a random forest‐based machine learning tool. We demonstrated that mitoSomatic achieved area under the curve (AUC) values over 0.99 for identifying somatic mtDNA variants without paired control in three tumor types. In addition, mitoSomatic was also applicable in nontumor tissues such as adjacent nontumor and blood samples, suggesting the flexibility of mitoSomatic's classification capability. Furthermore, analysis of triple‐paired samples identified a small group of variants with uncertain somatic/germline origin, whereas application of mitoSomatic significantly facilitated the prediction of their possible source. Finally, a control‐free evaluation of the public pan‐cancer NGS dataset with mitoSomatic revealed a substantial number of variants that were probably misclassified by conventional tumor‐control comparison, further emphasizing the usefulness of mitoSomatic in application. Taken together, our study demonstrates that mitoSomatic is valuable for accurately identifying somatic mtDNA variants in mtDNA NGS data without paired controls, applicable for both tumor and nontumor tissues. [ABSTRACT FROM AUTHOR]

Published
2023
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39. Next-Generation Sequencing–Based Analysis of Urine Cell-Free mtDNA Reveals Aberrant Fragmentation and Mutation Profile in Cancer Patients

Author

Zhou, Kaixiang, primary, Liu, Yang, additional, Yuan, Qing, additional, Lai, Dong, additional, Guo, Shanshan, additional, Wang, Zhenni, additional, Su, Liping, additional, Zhang, Huanqin, additional, Wang, Xiangxu, additional, Guo, Wenjie, additional, Ji, Xiaoying, additional, Gu, Xiwen, additional, Huang, Qichao, additional, Guo, Xu, additional, and Xing, Jinliang, additional

Published
2022
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40. Multiple‐level copy number variations in cell‐free DNA for prognostic prediction of HCC with radical treatments

Author

Wang, Yang, primary, Zhou, Kaixiang, additional, Wang, Xiangxu, additional, Liu, Yang, additional, Guo, Dongnan, additional, Bian, Zhenyuan, additional, Su, Liping, additional, Liu, Kun, additional, Gu, Xiwen, additional, Guo, Xu, additional, Wang, Lin, additional, Zhang, Hongmei, additional, Tao, Kaishan, additional, and Xing, Jinliang, additional

Published
2021
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