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Verification of the 2D/1D coupling 3D transport code TIGER with C5G7 benchmarks

International Journal of Advanced Nuclear reactor design and technology 2019年 1卷 43-50页

作者： Wu, Wenbin Yu, Yingrui Li, Qing Yao, Dong Luo, Qi Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China

TIGER is a 2D/1D coupling 3D transport code developed by Nuclear Power Institute of China based on large-scale parallel computation. It employs 2D domain-decomposed parallel Matrix MOC in the radial direction and 1D finite difference diffusion in the axial direction. 2D domain-decomposed parallel Matrix MOC is accelerated with multi-group and multi-domain coupled PGMRES algorithm. Generally, 2D planar problem are solved independently and parallel degree is natural in the axial direction. Combination of the parallel degrees of both radial and axial directions is adopted in TIGER to achieve the large-scale parallel computation. In this paper, a few variations of C5G7 benchmarks were created based on the published benchmarks. Reference results of these variations were provided by the Monte Carlo code RMC. To demonstrate the accuracy and efficiency of TIGER, C5G7 benchmarks and these relevant variations were calculated by TIGER, and the comparisons were made with the published results and Monte Carlo results. © 2019 Xi'an Jiaotong University

关键词： Matrix algebra

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Network Learning from Best-Response Dynamics in LQ Games

Network Learning from Best-Response Dynamics in LQ Games

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American Control Conference (ACC)

作者： Yijun Chen Kemi Ding Guodong Shi Australian Centre for Field Robotics the University of Sydney Sydney Australia School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China

In this paper, we focus on network structure inference problem for linear-quadratic (LQ) games from best-response dynamics. An adversary is considered to have no knowledge of the game network structure but have the ability to observe all players’ best-response actions and manipulate some players’ actions. This work presents a comprehensive framework for network learning from best-response dynamics in LQ games. First of all, we establish theoretic results that characterize network structure identifiability and provide numerical examples to demonstrate the usefulness of our theoretic results. Next, in the face of the inherent stability and sparsity constraints for the game network structure, we propose an information-theoretic stable and sparse system identification algorithm for learning the network structure. Finally, the effectiveness of the proposed learning algorithm is tested. The connection between network structure inference problem and classical system identification theory is covered by our work, which advances the literature.

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COUPLED MULTIPHYSICS MULTISCALE TRANSIENT SIMULATIONS OF THE Mk1-FHR reactor USING FINITE VOLUME CAPABILITIES OF THE MOOSE FRAMEWORK

COUPLED MULTIPHYSICS MULTISCALE TRANSIENT SIMULATIONS OF THE...

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2021 International Conference on Mathematics and Computational Methods Applied to Nuclear science and Engineering, M and C 2021

作者： Giudicelli, Guillaume Lindsay, Alexander Balestra, Paolo Carlsen, Robert Ortensi, Javier Gaston, Derek DeHart, Mark Abou-Jaoude, Abdalla Novak, April J. Computational Frameworks Idaho National Laboratory Idaho FallsID834015 United States Thermal Fluids System Methods and Analysis Idaho National Laboratory Idaho FallsID834015 United States Reactor Physics Method and Analysis Idaho National Laboratory Idaho FallsID834015 United States Advanced Reactor Technology and Design Idaho National Laboratory Idaho FallsID834015 United States Argonne National Laboratory 9700 S. Cass Avenue IL60439-4854 United States

ISBN: (纸本)9781713886310

Multiphysics simulations are key to reactor safety analysis, especially for novel reactor designs lacking experimental data. Simulation tools within the MOOSE framework, such as the Griffin reactor physics application and the coarse mesh computational fluid dynamics solver Pronghorn, can be coupled to perform such multiphysics transient simulations. We feature those capabilities in tightly-coupled multiphysics transient simulations of a simplified Mk1 PB-FHR reactor. A porous media model based on the incompressible Navier Stokes equation was implemented using the finite volume method for this work. This leverages the newly implemented finite volume discretization feature in MOOSE. Copyright © 2021 AMERICAN NUCLEAR SOCIETY, INCORPORATED, LA GRANGE PARK, ILLINOIS *** rights reserved.

关键词： finite volume Griffin MOOSE MSR multiphysics PB-FHR porous media flow Pronghorn

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Field-Harmonic-Oriented design of Vernier Permanent Magnet Machine Towards High Torque Density and Low Torque Ripple

Field-Harmonic-Oriented Design of Vernier Permanent Magnet M...

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International Conference on Electrical Machines and systems (ICEMS)

作者： Li Fang Dawei Li Yuanzhi Zhang Jiewen Wu School of Electrical and Electronics Engineering Huazhong University of Science and Technology Wuhan China School of Electricla Engineering and Automation (Hubei Key Laboratory of Power Equipment and System Security for Integrated Energy) Wuhan University Wuhan China College of Design and Engineering National University of Singapore Singapore

Vernier permanent magnet machine (VPMM) has been a promising candidate for low-speed large-torque applications owing to high torque density based on multiple-magnetic field-harmonics working principle However, rich field harmonics also cause large torque ripple, meanwhile the regular reducing methods will also undermine torque density. In this paper, the field-harmonic-oriented design approach is proposed for the VPMM towards high torque density and low torque ripple. Based on the permeance unit model, the mapping relationship between torque output and field harmonics is built. Explicit design regulations are established for the airgap structure so as to modulate the optimal field harmonics composition. Via the proposed method, a surface-mounted VPMM is purposely designed and the electromagnetic performance is verified by FEA. Finally, a prototype of the proposed machine has been manufactured and tested. Both analytical and experimental results validate the feasibility of the proposed design method.

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Research on probabilistic safety assessment (PSA) method of safety class distributed control system (DCS) in nuclear power plant based on fuzzy Markov process

Research on probabilistic safety assessment (PSA) method of ...

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2020 International Conference on Nuclear Engineering, ICONE 2020, collocated with the ASME 2020 Power Conference

作者： Tian, Xufeng Hao, Yan Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China HuaFu avenue 299# section 3 Chengdu city610213 China

ISBN: (纸本)9780791883778

Safety class DCS(Distributed Control system) in nuclear power plant is characterized by multi-state interaction, complex function and unknown fault mechanism, which limits the use of tradition PSA(Probabilistic Safety Assessment) method such as event tree and fault tree. An improved dynamic probabilistic security simulation method is proposed based on fuzzy Markov process to quantitative evaluation the risk of Safety class DCS in nuclear power plant, which resolved the failure uncertainty problem without in-depth understanding of failure mechanism. Firstly, the fuzzy fault tree reliability evaluation model is established based on fuzzy theory and FTA(Fault Tree Analysis), which describes the probability and degree of failure with fuzzy number. Then, the fuzzy fault tree is transformed into fuzzy Markov chain, this model is described mathematically, and the reliability indexes are simulated and analyzed with Monte Carlo simulation method, the change of system safety performance in long-term operation is obtained. Finally, the scientificity and effectiveness is verified by the example of redundant analog output module of safety class DCS in nuclear plant. Copyright © 2020 ASME

关键词： Nuclear power plants

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Joint Entity and Relation Extraction Based on Prompt Learning and Multi-channel Heterogeneous Graph Enhancement

Joint Entity and Relation Extraction Based on Prompt Learnin...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Haiqing Lv Xiaohui Han Peipei Wang Wenbo Zuo Jing Guo Lijuan Xu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China Information Research Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Joint extraction of entity and relation is crucial in information extraction, aiming to extract all relation triples from unstructured text. However, current joint extraction methods face two main issues. Firstly, they rarely consider the semantic information of entity and relation labels, leading to models that fail to fully understand and utilize the rich semantics in these labels, thereby limiting their performance. Secondly, although table-filling methods are widely used, they focus only on the start or end positions and ignore deep interactions between tables, relying solely on word-level information. To address these issues, we propose the P-MHE framework based on prompt learning and multi-channel heterogeneous graph enhancement. First, we use prompt templates to construct semantic nodes for entity and relation type labels, initializing them along with words as nodes in a heterogeneous graph. We iteratively fuse these semantic nodes through a message-passing mechanism to obtain node representations suitable for entity and relation extraction tasks. Secondly, we design a multi-channel heterogeneous graph to model node relationships from different perspectives, enhancing feature interactions among different types of nodes. Finally, we aggregate the semantic node information of entity and relation type labels after iteration, constructing separate decoding tables for each entity and relation type to better adapt to their respective characteristics. We evaluated our model on four public datasets. Experimental results show that P-MHE outperforms existing models on multiple public datasets. Extensive additional experiments further validate the effectiveness of our model.

关键词： Training Adaptation models Limiting Fuses Semantics Natural languages Information retrieval Feature extraction Data mining Iterative decoding

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The design and Implementation of Global Navigation Satellite system Remote Docking Test Platform 11th

The Design and Implementation of Global Navigation Satellite...

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11th EAI International Conference on Wireless and Satellite systems, WiSATS 2020

作者： Wei, Wang Qiang, Chai Weiguang, Gao Jun, Lu Shihai, Shao Yu, Bai Jingyi, Niu Wenjing, Feng Shaoqian, Li Beijing Institute of Tracking and Communication Technology Beijing100094 China GNSS System Engineering Center China Academy of Aerospace Electronics Technology Beijing100094 China Shanghai Engineering Center for Microsatellites Shanghai201203 China National Key Laboratory of Science and Technology On Communications University of Electronic Science and Technology of China Chengdu611731 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

ISBN: (纸本)9783030690717

The global navigation satellite system (GNSS) includes satellites of multiple types, ground facilities with multiple functions, and various user terminals. The docking test of multiple systems in ground is an important part for managing the design, construction, and deployment of the satellite navigation system. To achieve efficient parallel docking of multiple systems, this paper designs a platform for remote docking test. Specifically, the architecture, operating process, and functional performance of the docking test platform are designed according to the task and characteristics of the docking test for satellite navigation. Besides, some key techniques are also analyzed including the management of command and dispatch for multi-node system as well as the interconnection of remote fiber. This paper implements the effective interconnection between the simulation verification system and real engineering system, and realizes command control and timing scheduling with high-precision. The designed platform has the capability of full coverage test for signal and information projects, and the capability of remote docking test for multi-systems that work in parallel. By applying our platform, the docking efficiency can be improved, the docking cost can be reduced, and the implementation of high-density networking for satellite can be promoted. © 2021, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Global positioning system

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Artificial Intelligence in reactor Physics: Current Status and Future Prospects

arXiv

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arXiv 2025年

作者： Zhang, Ruizhi Zhu, Shengfeng Wang, Kan She, Ding Argaud, Jean-Philippe Bouriquet, Bertrand Li, Qing Gong, Helin School of Mathematical Sciences Shanghai Key Laboratory of Pure Mathematics and Mathematical Practice East China Normal University Shanghai200241 China Department of Engineering Physics Tsinghua University Beijing100084 China Électricité de France R&D 7 boulevard Gaspard Monge Palaiseau91120 France Électricité de France DQI 2 rue Ampère Saint-Denis93206 France Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610041 China Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai200240 China

reactor physics is the study of neutron properties, focusing on using models to examine the interactions between neutrons and materials in nuclear reactors. Artificial intelligence (AI) has made significant contributions to reactor physics, e.g., in operational simulations, safety design, real-time monitoring, core management and maintenance. This paper presents a comprehensive review of AI approaches in reactor physics, especially considering the category of Machine Learning (ML), with the aim of describing the application scenarios, frontier topics, unsolved challenges and future research directions. From equation solving and state parameter prediction to nuclear industry applications, this paper provides a step-by-step overview of ML methods applied to steady-state, transient and combustion problems. Most literature works achieve industry-demanded models by enhancing the efficiency of deterministic methods or correcting uncertainty methods, which leads to successful applications. However, research on ML methods in reactor physics is somewhat fragmented, and the ability to generalize models needs to be strengthened. Progress is still possible, especially in addressing theoretical challenges and enhancing industrial applications such as building surrogate models and digital twins. Copyright © 2025, The Authors. All rights reserved.

关键词： Nuclear industry

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Significant Wave Height Prediction Based on VMD-SA-MLP-BP

Significant Wave Height Prediction Based on VMD-SA-MLP-BP

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Chinese Automation Congress (CAC)

作者： Yining Wu Runfeng Zhang Fang Liu Zhenzhong Liu Jianguo Wu Haonan Dong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Yichang Research Institute of Testing Technology Yichang China Tianjin Hanhailanfan Marine Technology Co. Ltd. Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

Waves seriously impact port construction, worldwide route planning, military activities, and wave power generation. To improve the accuracy of significant wave height prediction, we proposed a novel prediction method, a multi-layer perceptron combined with a backpropagation adjustment (MLP-BP) prediction model that combines mutation mode decomposition (VMD) and a simulated annealing optimization algorithm (SA). Firstly, we use the variable modulus method to decompose the significant wave height sequence data and transform the wave height sequence into multiple different sub-modes (IMF) to reduce the complexity and non-stationarity of the data. Secondly, the simulated annealing algorithm (SA) is used to optimize the weight and bias of the MLP-BP neural network to find the optimal parameter configuration and improve the performance and generalization ability of the prediction model. Finally, the decomposed sub-modal components of each significant wave height sequence are inserted into the MLP-BP neural network and the predicted values of each element are summed to obtain the final significant wave height prediction. The prediction results of the SSA-MLP-BP, PSO-MLP-BP, SA-MLP-BP, and VMD-SA-MLP-BP were compared and demonstrated that the VMD-SA-MLP-BP model performed best. The MAE, MAPE, MSE, RMSE, and R2 of the prediction evaluation indexes were 0.036 m, 11.7%, 0.004 m 2 , 0.067 m, and 0.983, respectively, which performed well in predicting significant wave height.

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Thermal-mechanical coupling behavior analysis of solid-state constrained component in advanced nuclear energy system

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Energy 2025年 330卷

作者： Xuan Yang Quan Li Jing Zhang Yingwei Wu Yanan He Chenxi Li Mingjun Wang Kailun Guo Chenglong Wang G.H. Su Wenxi Tian Suizheng Qiu State Key Laboratory of Multiphase Flow in Power Engineering School of Nuclear Science and Technology Xi'an Jiaotong University Xi'an 710049 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu 610213 China

With the advantage of phase change heat transfer, heat pipes have the potential to replace flowing coolants for removing fission heat from nuclear reactors. However, the solid-state constrained heat transfer configuration poses mutual constraints between mechanical properties and heat transfer, and their long-term thermal-mechanical coupling behavior requires further investigation. Currently, research on long-term behavior is limited to either individual/local system components or a single physical field. In this paper, an analysis method for coupled thermal-mechanical behavior is proposed and verified, comprehensively considering thermal-mechanical properties, interactions between components, and the fission gas release. This method is employed to analyze the operating characteristics of a solid-state constrained component. The results indicate that prolonged operation leads to complete contact between structural components, generating high contact pressure that enhances heat transfer but increases creep. The release of gaseous fission products, accumulated over operating time, results in a synchronous increase in both gap and external contact pressures, reaching 5.2 MPa and 4.8 MPa, respectively. This process reduces the gas gap conductance, leading to elevated system peak temperatures and a reduction in temperature safety margins by 43 K. After heat pipe failure, continued operation significantly increases the local creep strain, up to 3.9 times that under normal conditions. The gap size and fuel gap pressure should be optimized to enhance gap heat transfer and reduce component creep. Excessive fission gas release should be avoided in fuel configuration. Reducing the system power following a single heat pipe failure can mitigate component creep and extend the operational lifespan.

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