Pub Date : 2026-03-01Epub Date: 2025-12-27DOI: 10.1016/j.nucengdes.2025.114726
Sipeng Du, Yunzhao Li, Hangqi Zhang, Ruizhi Shao, Liangzhi Cao
The power distribution of nuclear reactor core is a critical indicator of its operation state. For most reactors, the measured power distribution is generally obtained through the calculated power distribution, the measured and calculated in-core detector response currents. Therefore, the calculation accuracy of the in-core detector response current directly impacts the reliability of the measured power distribution. NECP-Bamboo is a PWR-core physics analysis software developed by the Nuclear Engineering Computational Physics (NECP) Laboratory at Xi'an Jiaotong University in China. Its precise calculation of the Self-Powered Neutron Detector (SPND) response current has been validated based on experimental data. Based on NECP-Bamboo, the paper conducts a quantitative analysis of the impact of the precise calculation of the response current on the verification of the reactor power distribution. The verification results based on the actual measurement data of the AP1000 indicate that the accuracy of NECP-Bamboo in calculating SPND response currents is significantly higher than that of the in-service original dedicated software for AP1000. Using accurately calculated SPND response currents can effectively reduce the error in power distribution, and NECP-Bamboo exhibits a smaller error compared to the in-service original dedicated software.
{"title":"Verification of PWR-Core power distribution based on precisely calculated SPND response currents","authors":"Sipeng Du, Yunzhao Li, Hangqi Zhang, Ruizhi Shao, Liangzhi Cao","doi":"10.1016/j.nucengdes.2025.114726","DOIUrl":"10.1016/j.nucengdes.2025.114726","url":null,"abstract":"<div><div>The power distribution of nuclear reactor core is a critical indicator of its operation state. For most reactors, the measured power distribution is generally obtained through the calculated power distribution, the measured and calculated in-core detector response currents. Therefore, the calculation accuracy of the in-core detector response current directly impacts the reliability of the measured power distribution. NECP-Bamboo is a PWR-core physics analysis software developed by the Nuclear Engineering Computational Physics (NECP) Laboratory at Xi'an Jiaotong University in China. Its precise calculation of the Self-Powered Neutron Detector (SPND) response current has been validated based on experimental data. Based on NECP-Bamboo, the paper conducts a quantitative analysis of the impact of the precise calculation of the response current on the verification of the reactor power distribution. The verification results based on the actual measurement data of the AP1000 indicate that the accuracy of NECP-Bamboo in calculating SPND response currents is significantly higher than that of the in-service original dedicated software for AP1000. Using accurately calculated SPND response currents can effectively reduce the error in power distribution, and NECP-Bamboo exhibits a smaller error compared to the in-service original dedicated software.</div></div>","PeriodicalId":19170,"journal":{"name":"Nuclear Engineering and Design","volume":"448 ","pages":"Article 114726"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-02DOI: 10.1016/j.nucengdes.2025.114728
Chen Hao , Yuchen Wen , Yizhen Wang
HNET is a high-fidelity neutron transport program developed for 3D reactor core simulation. To enhance its high-fidelity neutron transport simulation capability for pebble-bed HTR, a three-dimensional Method of Characteristics (3D-MOC) solver with Linear Source Approximation (LSA) is developed in HNET recently. The present work gives a comprehensive description on this newly developed solver with emphasizes on its calculation scheme, geometric modelling capability, parallel strategy and acceleration techniques. Computational efficiency of this 3D-MOC solver is enhanced by using virtual mesh CMFD method (vcCMFD) and modular track arrange strategy. This pebble-bed HTR 3D-MOC solver in HNET is verified with a pebble-bed HTR whole core model developed on HTR-10 benchmark. Results show that it takes about 189 core-hours to perform whole-core criticality simulation, which demonstrates its feasibility and efficiency for pebble-bed HTR high-fidelity neutron transport simulation.
{"title":"High-fidelity neutron transport simulation for pebble-bed HTR in HNET","authors":"Chen Hao , Yuchen Wen , Yizhen Wang","doi":"10.1016/j.nucengdes.2025.114728","DOIUrl":"10.1016/j.nucengdes.2025.114728","url":null,"abstract":"<div><div>HNET is a high-fidelity neutron transport program developed for 3D reactor core simulation. To enhance its high-fidelity neutron transport simulation capability for pebble-bed HTR, a three-dimensional Method of Characteristics (3D-MOC) solver with Linear Source Approximation (LSA) is developed in HNET recently. The present work gives a comprehensive description on this newly developed solver with emphasizes on its calculation scheme, geometric modelling capability, parallel strategy and acceleration techniques. Computational efficiency of this 3D-MOC solver is enhanced by using virtual mesh CMFD method (vcCMFD) and modular track arrange strategy. This pebble-bed HTR 3D-MOC solver in HNET is verified with a pebble-bed HTR whole core model developed on HTR-10 benchmark. Results show that it takes about 189 core-hours to perform whole-core criticality simulation, which demonstrates its feasibility and efficiency for pebble-bed HTR high-fidelity neutron transport simulation.</div></div>","PeriodicalId":19170,"journal":{"name":"Nuclear Engineering and Design","volume":"448 ","pages":"Article 114728"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-15DOI: 10.1016/j.nucengdes.2025.114678
Shang He, Peng Minjun, Xu Yifan, Xia Genglei
In the field of reactor thermal-hydraulics, there is a growing demand for reduced order model (ROM) that offers both high accuracy and fast computation, particularly for applications involving repeated simulations. The widely used linear ROM based on Principal Component Analysis (PCA) often suffers from the Kolmogorov barrier when applied to convection-dominated physical phenomena, requiring a large number of modes to achieve acceptable accuracy. To address this challenge, this study employs kernel PCA (KPCA), a nonlinear extension of the PCA, in combination with sparse grids theory to construct a parameterized, nonintrusive nonlinear ROM of the steady-state velocity magnitude field in a simplified two-dimensional reactor pressure vessel (RPV) lower plenum under varying inlet mass flow rates. A key obstacle in KPCA is the pre-image problem, which refers to the difficulty of reconstructing data from its low-dimensional representation. To overcome this, a distance-constrained pre-image recovery strategy enhanced by anchor points is proposed. The results indicate that, relative to the PCA-based linear ROM, the nonlinear ROM reveals the nonlinear advantage of the KPCA approach in mitigating the Kolmogorov barrier. It can achieve comparable or better accuracy with fewer modes while maintaining an evaluation time less than 0.1 s per query. In addition, the sparse-grid sampling strategy effectively identifies informative snapshots, thereby enhancing the robustness of ROM. The proposed approach offers a more accurate framework for nonlinear model reduction, particularly suitable for scenarios that require rapid acquisition of high-dimensional results, such as real-time simulation and parametric analysis of complex thermal-hydraulic systems.
{"title":"A parametric nonlinear model reduction method for the flow field in the RPV lower plenum using KPCA and sparse grids","authors":"Shang He, Peng Minjun, Xu Yifan, Xia Genglei","doi":"10.1016/j.nucengdes.2025.114678","DOIUrl":"10.1016/j.nucengdes.2025.114678","url":null,"abstract":"<div><div>In the field of reactor thermal-hydraulics, there is a growing demand for reduced order model (ROM) that offers both high accuracy and fast computation, particularly for applications involving repeated simulations. The widely used linear ROM based on Principal Component Analysis (PCA) often suffers from the Kolmogorov barrier when applied to convection-dominated physical phenomena, requiring a large number of modes to achieve acceptable accuracy. To address this challenge, this study employs kernel PCA (KPCA), a nonlinear extension of the PCA, in combination with sparse grids theory to construct a parameterized, nonintrusive nonlinear ROM of the steady-state velocity magnitude field in a simplified two-dimensional reactor pressure vessel (RPV) lower plenum under varying inlet mass flow rates. A key obstacle in KPCA is the pre-image problem, which refers to the difficulty of reconstructing data from its low-dimensional representation. To overcome this, a distance-constrained pre-image recovery strategy enhanced by anchor points is proposed. The results indicate that, relative to the PCA-based linear ROM, the nonlinear ROM reveals the nonlinear advantage of the KPCA approach in mitigating the Kolmogorov barrier. It can achieve comparable or better accuracy with fewer modes while maintaining an evaluation time less than 0.1 s per query. In addition, the sparse-grid sampling strategy effectively identifies informative snapshots, thereby enhancing the robustness of ROM. The proposed approach offers a more accurate framework for nonlinear model reduction, particularly suitable for scenarios that require rapid acquisition of high-dimensional results, such as real-time simulation and parametric analysis of complex thermal-hydraulic systems.</div></div>","PeriodicalId":19170,"journal":{"name":"Nuclear Engineering and Design","volume":"448 ","pages":"Article 114678"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145753662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-30DOI: 10.1016/j.nucengdes.2025.114731
Marcelo C. Santos , Bernardo M. Caixeta , Andressa S. Nicolau , Cláudio M.N.A. Pereira , Roberto Schirru
In Nuclear Power Plants (NPPs), most monitoring and diagnostic systems operate based on the principle of Detection and Response (D&R), in which operator actions are triggered only after an anomaly is detected. While effective for real-time monitoring, this approach lacks predictive capability, which is critical for anticipating the evolution of accidents and enhancing operational safety. To address this limitation, this study investigates the use of Deep Learning models for multi-horizon forecasting the temporal behavior of key state variables during normal operation and postulated accident scenarios in nuclear reactors. Two datasets were employed: the LABIHS dataset, composed of simulated time series from a Pressurized Water Reactor (PWR) under a Loss-of-Coolant Accident (LOCA), and the SICA dataset, which contains real operational data from the Angra 1 nuclear power plant. The methodology included data preprocessing and data augmentation using instrumentation noise. Four deep learning architectures were evaluated: Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), Time-series Dense Encoder (TiDE), and Neural Hierarchical Interpolation for Time Series (N-HiTS). These models were trained using a sliding window approach and evaluated across multiple forecasting horizons. Comparative results showed that TCN outperformed LSTM among the classical models, while TiDE and N-HiTS achieved the best overall accuracy and stability across all forecasting horizons. With average MAE values of 1.01 ± 2.39 (LABIHS) and 1.45 ± 1.33 (SICA), these findings confirm the effectiveness of modern Deep Learning architectures for predictive monitoring in nuclear power plant operations.
{"title":"Multistep forecasting of state variables in nuclear power plants using deep learning","authors":"Marcelo C. Santos , Bernardo M. Caixeta , Andressa S. Nicolau , Cláudio M.N.A. Pereira , Roberto Schirru","doi":"10.1016/j.nucengdes.2025.114731","DOIUrl":"10.1016/j.nucengdes.2025.114731","url":null,"abstract":"<div><div>In Nuclear Power Plants (NPPs), most monitoring and diagnostic systems operate based on the principle of Detection and Response (D&R), in which operator actions are triggered only after an anomaly is detected. While effective for real-time monitoring, this approach lacks predictive capability, which is critical for anticipating the evolution of accidents and enhancing operational safety. To address this limitation, this study investigates the use of Deep Learning models for multi-horizon forecasting the temporal behavior of key state variables during normal operation and postulated accident scenarios in nuclear reactors. Two datasets were employed: the LABIHS dataset, composed of simulated time series from a Pressurized Water Reactor (PWR) under a Loss-of-Coolant Accident (LOCA), and the SICA dataset, which contains real operational data from the Angra 1 nuclear power plant. The methodology included data preprocessing and data augmentation using instrumentation noise. Four deep learning architectures were evaluated: Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), Time-series Dense Encoder (TiDE), and Neural Hierarchical Interpolation for Time Series (N-HiTS). These models were trained using a sliding window approach and evaluated across multiple forecasting horizons. Comparative results showed that TCN outperformed LSTM among the classical models, while TiDE and N-HiTS achieved the best overall accuracy and stability across all forecasting horizons. With average MAE values of 1.01 ± 2.39 (LABIHS) and 1.45 ± 1.33 (SICA), these findings confirm the effectiveness of modern Deep Learning architectures for predictive monitoring in nuclear power plant operations.</div></div>","PeriodicalId":19170,"journal":{"name":"Nuclear Engineering and Design","volume":"448 ","pages":"Article 114731"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding how gas axially redistributes within fragmented fuel pellets is crucial for predicting the behavior of Light Water Reactor (LWR) fuel rods during accidental scenarios. Specifically, the time scale of this phenomenon plays a fundamental role in determining the progression and hazard of a Loss Of Coolant Accident (LOCA), especially when high burn-up fuel in a severe state of fragmentation is involved. This study presents a Computational Fluid Dynamics (CFD) model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) to predict the time-scale of plenum depressurization in fragmented Light-Water Reactor (LWR) fuel rods. The model examines the effects of incorporating non-linearities in the friction term by comparing the results with experimental data. These data were collected from an experiment that employed surrogate fuel rods containing pellets subjected to mechanical and/or thermal loadings. The objective of the experiement was to reproduce various severity of fuel cracking and to investigate the influence of fuel fragmentation on the dynamics of axial gas redistribution. The results of this study indicate that under certain flow regime conditions – determined by the value of an equivalent Reynolds number – accounting for the non-linear friction term in Navier–Stokes equations guarantees better predictions for the time-scale of plenum depressurization. Also, the model enabled the simulation of the plenum pressure decay by assigning distinct permeability values to each pellet instead of a single uniform value. Multiple simulations were run across all possible combinations of pellets’ positions, having each pellet assigned with values of permeability extracted from the experimental data. This allowed to quantify the impact of the considering various non-uniform distributions of permeability on the dynamics of axial gas redistribution. The present work findings enhance the understanding of axial gas transport, and provide valuable insights for the integration of a model for predicting the axial gas redistribution during a LOCA scenario into the BISON fuel performance code.
{"title":"Porous Flow Modeling of Axial Gas Redistribution in Fragmented LWR Fuel Rods using MOOSE","authors":"Chiara Genoni , Kyle A. Gamble , Davide Pizzocri , Fabiola Cappia , Tommaso Bergomi , Chase Christen , Seongtae Kwon","doi":"10.1016/j.nucengdes.2025.114677","DOIUrl":"10.1016/j.nucengdes.2025.114677","url":null,"abstract":"<div><div>Understanding how gas axially redistributes within fragmented fuel pellets is crucial for predicting the behavior of Light Water Reactor (LWR) fuel rods during accidental scenarios. Specifically, the time scale of this phenomenon plays a fundamental role in determining the progression and hazard of a Loss Of Coolant Accident (LOCA), especially when high burn-up fuel in a severe state of fragmentation is involved. This study presents a Computational Fluid Dynamics (CFD) model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) to predict the time-scale of plenum depressurization in fragmented Light-Water Reactor (LWR) fuel rods. The model examines the effects of incorporating non-linearities in the friction term by comparing the results with experimental data. These data were collected from an experiment that employed surrogate fuel rods containing pellets subjected to mechanical and/or thermal loadings. The objective of the experiement was to reproduce various severity of fuel cracking and to investigate the influence of fuel fragmentation on the dynamics of axial gas redistribution. The results of this study indicate that under certain flow regime conditions – determined by the value of an equivalent Reynolds number – accounting for the non-linear friction term in Navier–Stokes equations guarantees better predictions for the time-scale of plenum depressurization. Also, the model enabled the simulation of the plenum pressure decay by assigning distinct permeability values to each pellet instead of a single uniform value. Multiple simulations were run across all possible combinations of pellets’ positions, having each pellet assigned with values of permeability extracted from the experimental data. This allowed to quantify the impact of the considering various non-uniform distributions of permeability on the dynamics of axial gas redistribution. The present work findings enhance the understanding of axial gas transport, and provide valuable insights for the integration of a model for predicting the axial gas redistribution during a LOCA scenario into the BISON fuel performance code.</div></div>","PeriodicalId":19170,"journal":{"name":"Nuclear Engineering and Design","volume":"448 ","pages":"Article 114677"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-08DOI: 10.1016/j.nucengdes.2025.114714
Hernán Ariel Castro , Raul Ariel Rodriguez , Hugo Luis Bianchi
Treatment and conditioning of spent ion exchange resins (IERs) from nuclear facilities is a complex process. The direct immobilization of these materials with a hydraulic binder is usually a first option. However, even the operational procedure of immobilization with cement is not complicated, the volume of final solidified waste form increased significantly and its long-term integrity presents certain limitations.
A strategy internationally considered is to apply a prior treatment step to the spent IERs, mainly thermal treatments, in order to reduce the waste volume and stabilize the product.
In the last few years, our research group has developed a novel technology based on low-temperature thermal treatment of the IERs with steam followed by a High Performance Plasma Treatment (HPPT) of the generated off-gas. The process is capable of achieving high volume reduction factors and a non-reactive solid product.
In the present work, the quality of the solid product obtained in a test bench scale of the process is studied, emphasizing the product compatibility with cement. The solid product embedding with ordinary Portland cement (OPC), without any chemical additives or supplementary materials, was examined. The waste incorporation rate was up to roughly 90% (in volume). The waste form obtained was homogenous and presented compressive strength values around 18 MPa. No evidence of deterioration was observed after 90 days of water immersion.
{"title":"Thermal treatment and high performance plasma treatment applied to spent ion exchange resins: study of solid product embedding with ordinary Portland cement","authors":"Hernán Ariel Castro , Raul Ariel Rodriguez , Hugo Luis Bianchi","doi":"10.1016/j.nucengdes.2025.114714","DOIUrl":"10.1016/j.nucengdes.2025.114714","url":null,"abstract":"<div><div>Treatment and conditioning of spent ion exchange resins (IERs) from nuclear facilities is a complex process. The direct immobilization of these materials with a hydraulic binder is usually a first option. However, even the operational procedure of immobilization with cement is not complicated, the volume of final solidified waste form increased significantly and its long-term integrity presents certain limitations.</div><div>A strategy internationally considered is to apply a prior treatment step to the spent IERs, mainly thermal treatments, in order to reduce the waste volume and stabilize the product.</div><div>In the last few years, our research group has developed a novel technology based on low-temperature thermal treatment of the IERs with steam followed by a High Performance Plasma Treatment (HPPT) of the generated off-gas. The process is capable of achieving high volume reduction factors and a non-reactive solid product.</div><div>In the present work, the quality of the solid product obtained in a test bench scale of the process is studied, emphasizing the product compatibility with cement. The solid product embedding with ordinary Portland cement (OPC), without any chemical additives or supplementary materials, was examined. The waste incorporation rate was up to roughly 90% (in volume). The waste form obtained was homogenous and presented compressive strength values around 18 MPa. No evidence of deterioration was observed after 90 days of water immersion.</div></div>","PeriodicalId":19170,"journal":{"name":"Nuclear Engineering and Design","volume":"448 ","pages":"Article 114714"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-09DOI: 10.1016/j.nucengdes.2026.114752
Alan Matias Avelar , Jian Su , Claudia Giovedi , Fabio de Camargo , Joseph T. Klamo , Oleg Yakimenko
The licensing of new nuclear reactors after the Fukushima accident presents significant challenges due to the complexity of nuclear systems and the stringent regulatory requirements involved. Model-based systems engineering (MBSE) has emerged as a useful approach for managing the development of such complex systems, while Best Estimate Plus Uncertainty (BEPU) methodologies have proven valuable within regulatory frameworks for safety evaluation. However, digital models and databases that are needed to provide evidence that the system meets the specified requirements are usually isolated in discipline-specific data repositories. To address this challenge, this article proposes a model breakdown structure (MBS) methodology, using a set of interconnectable models to seamlessly integrate MBSE, computer-aided engineering (CAE) models, and BEPU analysis. The Brazilian Multipurpose Reactor (RMB) served as the system of interest to exemplify the effectiveness of the proposed methodology. A requirement specification was linked to a finite element analysis (FEA) that estimates the peak cladding temperature in a slow loss of flow accident scenario. Additionally, key design factors are identified using design of experiments (DOE) and analysis of variance (ANOVA). Lastly, Wilks' theorem and Monte Carlo simulations are applied for uncertainty quantification. The results indicate that the 95/95 upper tolerance limit of the peak cladding temperature remains below the onset of nucleate boiling. Furthermore, the utilization of Wilks' theorem can reduce computational cost for uncertainty quantification, and the effect of sampling methods is negligible in Monte Carlo simulations with large sample sizes. This approach can enhance the verification and validation (V&V) of regulatory requirements in the licensing process of new reactors.
{"title":"Integrating best estimate plus uncertainty analysis into model-based systems engineering","authors":"Alan Matias Avelar , Jian Su , Claudia Giovedi , Fabio de Camargo , Joseph T. Klamo , Oleg Yakimenko","doi":"10.1016/j.nucengdes.2026.114752","DOIUrl":"10.1016/j.nucengdes.2026.114752","url":null,"abstract":"<div><div>The licensing of new nuclear reactors after the Fukushima accident presents significant challenges due to the complexity of nuclear systems and the stringent regulatory requirements involved. Model-based systems engineering (MBSE) has emerged as a useful approach for managing the development of such complex systems, while Best Estimate Plus Uncertainty (BEPU) methodologies have proven valuable within regulatory frameworks for safety evaluation. However, digital models and databases that are needed to provide evidence that the system meets the specified requirements are usually isolated in discipline-specific data repositories. To address this challenge, this article proposes a model breakdown structure (MBS) methodology, using a set of interconnectable models to seamlessly integrate MBSE, computer-aided engineering (CAE) models, and BEPU analysis. The Brazilian Multipurpose Reactor (RMB) served as the system of interest to exemplify the effectiveness of the proposed methodology. A requirement specification was linked to a finite element analysis (FEA) that estimates the peak cladding temperature in a slow loss of flow accident scenario. Additionally, key design factors are identified using design of experiments (DOE) and analysis of variance (ANOVA). Lastly, Wilks' theorem and Monte Carlo simulations are applied for uncertainty quantification. The results indicate that the 95/95 upper tolerance limit of the peak cladding temperature remains below the onset of nucleate boiling. Furthermore, the utilization of Wilks' theorem can reduce computational cost for uncertainty quantification, and the effect of sampling methods is negligible in Monte Carlo simulations with large sample sizes. This approach can enhance the verification and validation (V&V) of regulatory requirements in the licensing process of new reactors.</div></div>","PeriodicalId":19170,"journal":{"name":"Nuclear Engineering and Design","volume":"448 ","pages":"Article 114752"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-22DOI: 10.1016/j.nucengdes.2025.114694
Jun Hong , Tao Wang , Baoyin Zhu , Dongpeng Li , Haitao Dong , Dungui Zuo , Junlan Huang , Zheng Man , Gongye Zhang
Structural components subjected to simple shear at elevated temperatures are particularly vulnerable to creep deformation and failure, motivating reliable constitutive models and accurate life-prediction tools. This study extends the classical Kachanov-Rabotnov (K-R) model—originally developed for uniaxial tension or small shear deformations—into the large-shear regime, where the traditional formulation becomes less accurate. By introducing equivalent stress and strain measures tailored to finite shear, a modified K-R model is developed that accurately captures creep behavior under large-shear deformation. To demonstrate the applicability of the model, lap-jointed components fabricated with low-melting-point filler metals were selected as case studies, which is used to maintain the safety of the reactor containment vessel. Tensile and creep tests were conducted to fit the model parameters, which were subsequently incorporated into finite element simulations for comparative analysis. Validation against experimental and numerical results shows that the current modified model better replicates creep strain data, achieving closer agreement than the classical K-R model. The proposed model offers a practical and robust tool for creep-life assessment of large-shear structures, with significant implications for applications in nuclear passive-safety systems and brazed assemblies in thermal power and fire-protection equipment.
{"title":"Enhancing creep life prediction under large-shear deformation based on a modified Kachanov–Rabotnov model","authors":"Jun Hong , Tao Wang , Baoyin Zhu , Dongpeng Li , Haitao Dong , Dungui Zuo , Junlan Huang , Zheng Man , Gongye Zhang","doi":"10.1016/j.nucengdes.2025.114694","DOIUrl":"10.1016/j.nucengdes.2025.114694","url":null,"abstract":"<div><div>Structural components subjected to simple shear at elevated temperatures are particularly vulnerable to creep deformation and failure, motivating reliable constitutive models and accurate life-prediction tools. This study extends the classical Kachanov-Rabotnov (K-R) model—originally developed for uniaxial tension or small shear deformations—into the large-shear regime, where the traditional formulation becomes less accurate. By introducing equivalent stress and strain measures tailored to finite shear, a modified K-R model is developed that accurately captures creep behavior under large-shear deformation. To demonstrate the applicability of the model, lap-jointed components fabricated with low-melting-point filler metals were selected as case studies, which is used to maintain the safety of the reactor containment vessel. Tensile and creep tests were conducted to fit the model parameters, which were subsequently incorporated into finite element simulations for comparative analysis. Validation against experimental and numerical results shows that the current modified model better replicates creep strain data, achieving closer agreement than the classical K-R model. The proposed model offers a practical and robust tool for creep-life assessment of large-shear structures, with significant implications for applications in nuclear passive-safety systems and brazed assemblies in thermal power and fire-protection equipment.</div></div>","PeriodicalId":19170,"journal":{"name":"Nuclear Engineering and Design","volume":"448 ","pages":"Article 114694"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-19DOI: 10.1016/j.nucengdes.2025.114681
Vincent Pascal , Christophe Venard , Johann Martinet , Elena Martin-Lopez , Martin Mascaron , Laura Matteo , Barbara Forno , Bertrand Morel , Jérome Serp , Marie-Sophie Chenaud
Within the framework of studies on the fuel cycle and transuranic actinide management (Pu, Am, Cm), the CEA and ORANO launched an R&D program on fast molten salt reactors (MSR) in 2020. The aim was to assess their opportunities with respect to fuel cycle management and to offer insights into their technical feasibility. ARAMIS-P is an abbreviation of ‘Advanced Reactor for Actinides Management in Salt’ with P for plutonium; this project focused on the preliminary design of a small unit for plutonium conversion integrated into a spent fuel reprocessing facility. The goal was to avoid nuclear cycle issues like spent MOX fuel reprocessing, by investigating the possibility of providing a flexible plutonium conversion service (from high-grade plutonium to ex-MOX quality). The reactor power was fixed at 300 MWth, which is in the power range of advanced modular reactors (AMR). From the perspective of a reprocessing unit, the reactor footprint, the fuel salt hold-up, and the need to develop new chemical processes should be limited. A chloride-based fuel salt was selected due to its compliance with known spent fuel processes. A specific design with a high core power density and a 6-month batch feed-up strategy was chosen to limit the overall amount of fuel salt. The design process was also driven by the desire to make maximum use of proven technologies when available, as well as to implement a maintenance-by-design approach. This report presents the design methodology developed to produce a preliminary reactor sketch, to illustrate its burn-up performance, and finally to give insights into component design.
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Pub Date : 2026-03-01Epub Date: 2025-12-17DOI: 10.1016/j.nucengdes.2025.114690
Chang Hyun Song , Mohammad Amer Allaf , Koroush Shirvan
Small Modular Reactors (SMRs) are increasingly recognized as promising technologies, offering advanced passive safety features, and flexible deployment at smaller financing risk. Among these, the NuScale Power Module (NPM) is notable as the first SMR certified by the U.S. Nuclear Regulatory Commission. In order to further improve the economics of such concept, this study investigates the feasibility of integrating a forced circulation core concept into the NPM framework to enhance thermal performance. The proposed design incorporates improved coolant circulation and a compact steam generator, which increases primary coolant inventory and enhances decay heat removal capacity. These features are expected to provide significant power uprate margin and provide greater resilience during accident conditions. Safety analyses were performed with the MELCOR code for limiting transients, focusing on containment integrity and core safety margins. Results show that containment improvements, combined with the compact steam generator, can support higher power operation without compromising safety limits compared to the latest NPM power output. The findings provide a technical basis for future uprating strategies of >40 % aimed at improving the economic viability of the NPM and broadening the deployment potential of SMRs in diverse energy markets.
小型模块化反应堆（smr）越来越被认为是一种有前途的技术，它提供了先进的被动安全特性，并且在较小的融资风险下灵活部署。其中，NuScale Power Module （NPM）是第一个获得美国核管理委员会认证的SMR。为了进一步提高这一概念的经济性，本研究探讨了将强制循环核心概念整合到NPM框架中以提高热性能的可行性。拟议的设计包括改进的冷却剂循环和一个紧凑的蒸汽发生器，这增加了一次冷却剂库存，提高了衰变热去除能力。这些特性有望提供显著的功率升级余量，并在事故条件下提供更大的恢复能力。使用MELCOR规范进行了安全分析，以限制瞬变，重点是安全壳完整性和堆芯安全裕度。结果表明，与最新的NPM功率输出相比，密封改进与紧凑型蒸汽发生器相结合，可以支持更高功率的运行，而不会影响安全限制。研究结果为未来40%的升级策略提供了技术基础，旨在提高NPM的经济可行性，扩大小型反应堆在不同能源市场的部署潜力。
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