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This paper presents the steady-state analysis results of the OECD/NEA and U.S. NRC PWR MOX/UO2 (MOX: Mixed Oxide) Core Transient Benchmark with the modern MCNP6 Monte Carlo code based on the ENDF/B-VII.1 evaluated nuclear data library. The purpose was to verify an MCNP6 model proposed for calculations of a heterogeneous MOX/UO2 fuelled PWR core, which has different neutronic characteristics from the popular homogeneous ones loaded with the UO2 fuel due to its partial loading of the MOX fuel. The effective neutron multiplication factors, assembly power distributions, and control rod worths calculated using MCNP6 showed a reasonable agreement within 390 pcm, 6%, and 175 pcm, respectively, with the available benchmark data. The discrepancies between the MCNP6 results and the benchmark data were also discussed. Consequently, these results obtained with MCNP6 and ENDF/B-VII.1 can be considered as a new full-core heterogeneous transport solution to supplement for the available benchmark solutions at the steady-state conditions.

In this study, the SCALE/TRITON code (based on deterministic method) and the Serpent 2 code (based on Monte Carlo method) were utilized to prepare the group constants of the pressurized water reactor (PWR) mixed-oxide (MOX) fuel assemblies for transient analyses of PWR MOX fueled cores in normal operation and control rod ejection accident condition with 3D reactor kinetics codes. The PWR MOX fuel assemblies were modeled with TRITON and Serpent, and their infinite neutron multiplication factors (k-inf) versus burnup and respective two-group neutron cross sections were calculated and compared against the available benchmark data obtained with the HELIOS code. The comparative results generally show a good agreement between TRITON and Serpent with HELIOS within 643 pcm for the k-inf values and within 5% for the two-group neutron cross sections. Therefore, it indicates that the TRITON and Serpent models developed herein for the PWR MOX fuel assemblies can be applied to group constant generation to be further used in transient analyses of PWR MOX fueled cores.

This paper presents the assessment of the ex-core detectors in the Prototype Gen-IV Sodium-cooled Fast Reactor (PGSFR) in response to the movement of single primary control assembly, to evaluate the detectors’ locations and to determine a proper number of detectors. The detectors are located below the containment vessel and distributed in different ranges from the center and different azimuthal angles. Another position considered is in the radial direction of the core, at the sodium coolant gap between the outermost assembly and core shroud. To calculate the detection rate, the contribution of each fuel region to the detector and the core power distribution are required. Initially, the aforementioned contribution is generated by performing a continuous-energy forward fixed-source Monte Carlo calculation using MCNPX. The reactor kinetics code FREK simulates the power change due to an inadvertent withdrawal of a primary control assembly up to 10% from its critical position followed by full insertion of secondary control assemblies. The response from each detector location was evaluated, and consequently, the detectors located below the containment vessel, which can “see” all the fuel assemblies, responded better to the core power increase with a minor error of less than ∼0.6%. In addition, the impact of locating the detector below the containment vessel at different locations from the center or at different azimuthal angles was insignificant but several detectors should be distributed at even azimuthal angles for effective core monitoring and redundancy purposes.

The ADS (accelerator driven system) is recognized as a promising system to annihilate the radioactivity of nuclear waste with its inherent safety feature and waste transmutation potential. Thus, conceptual designs of ADS are widely carrying out. In order to verify the accuracy of an innovative ADS core modeling by using simulation codes, the reactivity calculations of CERMET fueled ADS were conducted using two Monte Carlo codes, Serpent and MCNP6 with ENDF/B-VII.0 library. The comparison shows a good agreement between two codes including the eigenvalue (less than 50 pcm) and fuel temperature feedback (discrepancy is within the standard deviation). It implies that the ADS was modelled successfully and can be used for further investigation. Keywords: CERMET fueled ADS, Serpent code, MCNP6, reactivity calculation.

Cross-sectional homogenization for full-core calculations of small and complex reactor configurations, such as research reactors, has been recently recognized as an interesting and challenging topic. This paper presents the development of a PARCS/Serpent model for the neutronics analysis of a research reactor type TRIGA Mark-II loaded with Russian VVR-M2 fuel (known as the Dalat Nuclear Research Reactor or DNRR). The full-scale DNRR model and a supercell model for a shim/safety rod and its surrounding fuel bundles with the Monte Carlo code Serpent 2 were proposed to generate homogenized few-group cross sections for full-core diffusion calculations with PARCS. The full-scale DNRR model with Serpent 2 was also utilized as a reference to verify the PARCS/Serpent calculations. Comparison of the effective neutron multiplication factors, radial and axial core power distributions, and control rod worths showed a generally good agreement between PARCS and Serpent 2. In addition, the discrepancies between the PARCS and Serpent 2 results are also discussed. Consequently, the results indicate the applicability of the PARCS/Serpent model for further steady state and transient analyses of the DNRR.

This paper aims to apply and compare the performance of the three machine learning algorithms&ndash, support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)&ndash, to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future.

The declining water level in Lake Urmia has become a significant issue for Iranian policy and decision makers. This lake has been experiencing an abrupt decrease in water level and is at real risk of becoming a complete saline land. Because of its position, assessment of changes in the Lake Urmia is essential. This study aims to evaluate changes in the water level of Lake Urmia using the space-borne remote sensing and GIS techniques. Therefore, multispectral Landsat 7 ETM+ images for the years 2000, 2010, and 2017 were acquired. In addition, precipitation and temperature data for 31 years between 1986 and 2017 were collected for further analysis. Results indicate that the increased temperature (by 19%), decreased rainfall of about 62%, and excessive damming in the Urmia Basin along with mismanagement of water resources are the key factors in the declining water level of Lake Urmia. Furthermore, the current research predicts the potential environmental crisis as the result of the lake shrinking and suggests a few possible alternatives. The insights provided by this study can be beneficial for environmentalists and related organizations working on this and similar topics. Validerad;2020;Nivå 2;2020-06-26 (alebob)

Groundwater is an important natural resource in arid and semi-arid environments, where discharge from karst springs is utilized as the principal water supply for human use. The occurrence of karst springs over large areas is often poorly documented, and interpolation strategies are often utilized to map the distribution and discharge potential of springs. This study develops a novel method to delineate karst spring zones on the basis of various hydrogeological factors. A case study of the Bojnourd Region, Iran, where spring discharge measurements are available for 359 sites, is used to demonstrate application of the new approach. Spatial mapping is achieved using ensemble modelling, which is based on certainty factors (CF) and logistic regression (LR). Maps of the CF and LR components of groundwater potential were generated individually, and then, combined to prepare an ensemble map of the study area. The accuracy (A) of the ensemble map was then assessed using area under the receiver operating characteristic curve. Results of this analysis show that LR (A = 78%) outperformed CF (A = 67%) in terms of the comparison between model predictions and known occurrences of karst springs (i.e., calibration data). However, combining the CF and LR results through ensemble modelling produced superior accuracy (A = 85%) in terms of spring potential mapping. By combining CF and LR statistical models through ensemble modelling, weaknesses in CF and LR methods are offset, and therefore, we recommend this ensemble approach for similar karst mapping projects. The methodology developed here offers an efficient method for assessing spring discharge and karst spring potentials over regional scales. Validerad;2020;Nivå 2;2020-03-31 (johcin)

Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the ecosystem of the environment, while its desiccation has a negative impact on the surrounded ecosystem. Despite this, this lake provides an interesting recreation setting in terms of ecotourism. The prediction and forecasting of the water level of the lake through simple but practical methods can provide a reliable tool for future lake water resource management. In the present study, we predict the daily water level of Zrebar Lake in Iran through well-known decision tree-based algorithms, including the M5 pruned (M5P), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). We used five different water input combinations to find the most effective one. For our modeling, we chose 70% of the dataset for training (from 2011 to 2015) and 30% for model evaluation (from 2015 to 2017). We evaluated the models&rsquo, performances using different quantitative (root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), percent bias (PBIAS) and ratio of the root mean square error to the standard deviation of measured data (RSR)) and visual frameworks (Taylor diagram and box plot). Our results showed that water level with a one-day lag time had the highest effect on the result and, by increasing the lag time, its effect on the result was decreased. This result indicated that all the developed models had a good prediction capability, but the M5P model outperformed the others, followed by RF and RT equally and then REPT. Our results showed that these algorithms can predict water level accurately only with a one-day lag time in water level as an input and they are cost-effective tools for future predictions.

The most important ageing effect on the reactor pressure vessel (RPV) is radiation embrittlement, which is mainly caused by fast neutrons during operation lifetime of nuclear reactors. The aim of this study was to investigate the DPA (displacement per atom) rate, an important parameter describing radiation damage to the RPV, and identify the position of the maximum DPA rate in the RPV of the VVER-1000/V320 reactor using the Monte Carlo code MCNP5. To reduce statistical errors in the MCNP5 simulation, the weight window technique was applied to non-repeated structures outside the reactor core. The results showed the distribution of the DPA rate in the RPV and the maximum DPA rate was found to be at the first millimeters of the RPV. Consequently, these calculations could be useful for assessment of radiation damage to the RPV of VVER reactors.