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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.

关键词： Electric heating

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Visualizing the dynamic progression of backward erosion piping in a Hele-Shaw cell

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Journal of Zhejiang University-science A(Applied Physics & Engineering) 2022年第11期23卷 945-954页

作者： Gang ZHENG Jing-bo TONG Tian-qi ZHANG Zi-wu WANG Xun LI Ji-qing ZHANG Chun-yu QI Hai-zuo ZHOU Yu DIAO MOE Key Laboratory of Coast Civil Structure Safety Tianjin UniversityTianjin 300072China School of Science Tianjin UniversityTianjin 300072China China Railway Design Corporation Tianjin 300308China National Engineering Laboratory for Digital Construction and Evaluation Technology of Urban Rail Transit Tianjin 300308China

With the utilization of underground space,backward erosion piping(BEP)has been observed in many underground structures(e.g.,shield tunnels)founded on sandy ***,due to invisibility,the geometry of the eroded pipe and its spatial evolution with time during the piping process was still not *** this study,we developed a Hele-Shaw cell to visualize the dynamic progression of *** imaging process technology,we obtained a typical process of BEP(the erosion process can be divided into a piping progression phase and a piping stabilization phase),quantitatively characterized the formation of erosion pipes,and compared the patterns of erosion(e.g.,the erosion area A and the maximum erosion radius R)that spontaneously develop under different fluxes of *** most interesting finding is that the sand grains in a thicker Hele-Shaw model are easier to dislodge,which is possibly due to the granular system in a thicker model having more degrees of freedom,reducing the stability of the sand grains.

关键词： Backward erosion piping(BEP) Dynamic progression Hele-Shaw cell Visualize Imaging processing technology

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Efficient Multivariate Time Series Anomaly Detection Through Transfer Learning for Large-Scale Web Services

Efficient Multivariate Time Series Anomaly Detection Through...

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IEEE International Conference on Web Services (ICWS)

作者： Yongqian Sun Minghan Liang Zeyu Che Dongwen Li Tinghua Zheng Shenglin Zhang Pengtian Zhu Yuzhi Zhang Dan Pei Nankai University Tianjin China Tianjin Key Laboratory of Operating System (TKL-OS) Tianjin China Haihe Laboratory of Information Technology Application Innovation (HL-IT) Tianjin China Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Beijing China

Timely anomaly detection of multivariate time series (MTS) is of vital importance for managing large-scale Web services. However, many deep learning-based MTS anomaly detection models require long-term MTS training data to achieve good performance, which conflicts with frequent pattern changes in Web services entities. Moreover, the training overhead of vast MTS in large-scale Web services is unacceptable. To address these issues, we design OmniTransfer, a model-agnostic framework that combines improved hierarchical agglomerative clustering with an adaptive transfer learning strategy, making many state-of-the-art (SOTA) MTS anomaly detection models efficient and effective. Extensive experiments using real-world data from a large Web content service provider show that OmniTransfer significantly reduces the model initialization time by 59.72% and the training cost by 85.01%, while maintaining high accuracy in detecting anomalies.

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Q-Learning for Finite-Horizon H∞ Tracking Control of Unknown Linear Discrete-Time systems

Q-Learning for Finite-Horizon H∞ Tracking Control of Unknow...

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Chinese Control Conference (CCC)

作者： Mingxiang Liu Qianqian Cai Wei Meng Dandan Li Minyue Fu Zhimin Han School of Automation Guangdong University of Technology Guangzhou China Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Automation Hangzhou Dianzi University Hangzhou China

In this paper, we present a Q-learning algorithm for finite-horizon H∞ tracking control of unknown linear discrete-time systems. Finite horizon control is challenging due to its correspondence with a time-varying Riccati equation. The proposed algorithm can determine the solution of the time-varying Riccati equation with unknown system dynamics. We first establish the time-varying Q-function, then propose a corresponding Q-learning algorithm and prove its convergence. Finally, the proposed algorithm is proved to be effective through numerical examples.

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Anomaly Detection-Oriented High-Frequency Signal Storage for Diesel Engines 4

Anomaly Detection-Oriented High-Frequency Signal Storage for...

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4th IEEE International Conference on Computer and Communication Engineering technology, CCET 2021

作者： Huang, He Xu, Bailing Luo, Hao Zhuang, Haoran Liu, Xuewei Ma, Liyong Naval University of Engineering National Key Laboratory for Vessel Integrated Power System Technology Wuhan China Shanghai Marine Diesel Engine Research Institute Shanghai China School of Information Science and Engineering Harbin Institute of Technology Weihai China

ISBN: (纸本)9781665438902

Marine diesel engine has been the main power of civil ships, small and medium-sized ships and conventional submarines. Anomaly detection on diesel engines is very important for the safety of marine systems. Diesel engine anomaly detection requires access to a large number of high-frequency signals. How to store these signals to support anomaly detection applications is currently rarely studied. This article discusses the design of high-frequency signal storage systems that provide data support for intelligent anomaly detection systems. It mainly focuses the design of database system and file system for high-frequency signal storage requirements of diesel engines. The actual operation shows that the system designed in this paper can support the storage of high-frequency signals and the deployment of anomaly detection applications. © 2021 IEEE.

关键词： Diesel engines

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