检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Li, Chengyuan Li, Meifu Qiu, Zhifang Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu Sichuan China

Compared to knowledge-based diagnostic systems, data-based methods tend to perform better in terms of speed and accuracy in diagnosing reactor accidents, and have significant advantages in terms of scalability of models. With the increasing use of high-precision system analysis programs in nuclear engineering, the number of high-fidelity computational data for accident simulation is exploding. Therefore, an algorithm that can achieve both automatic extraction of low-dimensional features from the data and guarantee the validity of the features is needed to improve the performance and confidence of the accident diagnosis system. This study proposes an autoencoder-based autonomous learning framework, namely Padded Auto-Encoder (PAE), which is able to automatically encode accident monitoring data that has been noise-added and with partially missing data into low-dimensional feature vectors via a Vision Transformer-based encoder, and to decode the feature vectors into noise-free and complete reconstructed monitoring data. Thus, the encoder part of the framework is able to automatically infer valid representations from partially missing and noisy monitoring data that reflect the complete and noise-free original data, and the representation vectors can be used for downstream tasks for accident diagnosis or else. In this paper, LOCA of HPR1000 was used as the study object, and the PAE was trained by an unsupervised method using cases with different break locations and sizes as the dataset. The encoder part of the pre-trained PAE was subsequently used as the feature extractor for the monitoring data, and several basic statistical learning algorithms for predicting the break locations and sizes. The results of the study show that the pre-trained diagnostic model with two stages has a better performance in break location and size diagnostic capability with an improvement of 41.62% and 80.86% in the metrics respectively, compared to the diagnostic model with end-to-end model st

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

A Long-term Dependent and Trustworthy Approach to reactor Accident Prognosis based on Temporal Fusion Transformer

arXiv

引用

arXiv 2022年

作者： Li, Chengyuan Qiu, Zhifang Ma, Yugao Li, Meifu Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China

Prognosis of the reactor accident is a crucial way to ensure appropriate strategies are adopted to avoid radioactive releases. However, there is very limited research in the field of nuclear industry. In this paper, we propose a method for accident prognosis based on the Temporal Fusion Transformer (TFT) model with multi-headed self-attention and gating mechanisms. The method utilizes multiple covariates to improve prediction accuracy on the one hand, and quantile regression methods for uncertainty assessment on the other. The method proposed in this paper is applied to the prognosis after loss of coolant accidents (LOCAs) in HPR1000 reactor. Extensive experimental results show that the method surpasses novel deep learning-based prediction methods in terms of prediction accuracy and confidence. Furthermore, the interference experiments with different signal-to-noise ratios and the ablation experiments for static covariates further illustrate that the robustness comes from the ability to extract the features of static and historical covariates. In summary, this work for the first time applies the novel composite deep learning model TFT to the prognosis of key parameters after a reactor accident, and makes a positive contribution to the establishment of a more intelligent and staff-light maintenance method for reactor systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Nuclear industry

来源：评论

学校读者我要写书评

暂无评论

Experimental Study on Flow Resistance and Phase Distribution of Gas-Liquid Flow in Helical Cruciform Fuel Assembly

SSRN

引用

SSRN 2024年

作者： Zhang, Qi Wang, Haoyu Li, Junlong Ren, Quanyao Zhou, Yi Zheng, LeLe Zhang, Yile Li, Dongyang Gu, Hanyang Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610041 China School of Nuclear Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Harbin Engineering University China

The helical cruciform fuel (HCF) is characterized by self-supporting and enhanced transverse mixing, which may replace the cylindrical fuel of the light water reactor (LWR). In this work, the flow resistance and phase distribution of a 4×4 HCF assembly was investigated by experimental method. The experimental system of air-water two-phase flow was established, and the void fraction was gained by wire-mesh sensor (WMS). The dummy HCF elements produced by additive manufacturing technology were assembled with the locating plates. The evolution of the flow regime and the void fraction of the central and peripheral were paid particular attention. The correlations for the two-phase friction multiplier and void fraction were proposed based on the experimental data. © 2024, The Authors. All rights reserved.

关键词： Two phase flow

来源：评论

学校读者我要写书评

暂无评论

A Method for Generating Set of Test Cases for Nuclear Power DCS Combination Based on Contract Model 6th

A Method for Generating Set of Test Cases for Nuclear Power ...

引用

6th International Conference on Computing, Control, and Industrial Engineering, CCIE 2021

作者： Peng, Beicheng Chen, Zhi Liu, Jie Liu, Zhaojun Computer School University of South China Hengyang421001 China National Key Laboratory of Reactor System Design Technology Nuclear Power Institute of China Chengdu610213 China

ISBN: (纸本)9789811939266

Aiming at the need to solve the problem of complex interaction of parameters and difficulty in determining the combination strength in the combination test of the digital I&C system of nuclear power plants, a method for generating a set of variable strength combination test cases based on a contract model is proposed. First, analyze the relationship between input parameters and output parameters from the constructed contract model, determine the actual interaction relationship of the system, and adopt the variable strength combination test case generation method based on the In-Parameter-Order strategy to achieve full coverage of the actual interaction relationship between components, and use contractual constraints to further reduce the scale of test data. Finally, the algorithm is applied to the test of nuclear power plant core temperature measurement system. The results show that the proposed algorithm has certain advantages in test data scale, coverage and error detection ability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Nuclear power plants

来源：评论

学校读者我要写书评

暂无评论

Intra Fast Mode Decision Algorithm and Hardware design for AVS2

Intra Fast Mode Decision Algorithm and Hardware Design for A...

引用

2021 International Conference on Signal Processing and Machine Learning, CONF-SPML 2021

作者： Xie, Hao Liu, Kaiyang Zhao, Yang Zheng, Xiao Qing, Xianguo Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China Peking University School of Software and Microelectronics Beijing China

ISBN: (纸本)9781665417341

AVS2 is a kind of video coding standard proposed by China, it adopts 33 intra prediction modes to improve coding performance, while the computational complexity has increased dramatically. In order to reduce the complexity of AVS2 intra mode decision and make the AVS2 hardware encoder meet real-time requirements, this paper proposes the AVS2 intra fast mode decision algorithm and hardware design. The experimental results show that the intra mode decision algorithm and hardware design proposed in this paper can meet the throughput requirement of 1920x1080@60fps at a clock frequency of 300MHz. By using Xilinx FPGA XC7K325T 900 on the Vivado HLS platform for synthesis, only 10% of the LUT, 5% of FF, 5% of BRAM, and 6% of DSP in FPGA resources are consumed to meet the design requirements. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

来源：评论

学校读者我要写书评

暂无评论

Identification Algorithm Framework and Structural model on Input Pattern of Metamorphic Relations 9

Identification Algorithm Framework and Structural model on I...

引用

9th International Conference on Dependable systems and Their Applications, DSA 2022

作者： Yan, Shiyu Yang, Xiaohua Lu, Zhongjian Li, Meng Gong, Helin Liu, Jie Computer School University of South China Hunan Hengyang China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China Cnnc Key Laboratory on High Trusted Computing Hunan Hengyang China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Metamorphic Relation (MR) plays a crucial role in Metamorphic Testing (MT), which can be regarded as the implication relation from the input pattern to the output pattern. Based on MR's properties, the identification of Metamorphic Relations (MRs) can be from its input pattern and output pattern, respectively. However, the finding of the effective input pattern is very first and important. Especially for complicated mathematical function includes more than two input variables, we systematically analyze the structural model of input pattern in MR, propose the concept of the class of basic input pattern, as well as prove that any one input pattern can be expressed as a combination of basic input patterns in scientific computing programs. Moreover, the structural model of input pattern in MR is established based on previous proof. Finally, we put forward an identification algorithm framework and analysis its complexity. The core contribution of our method is to decompose the input pattern identification problem in multi-dimensional space into a single one-dimensional. For this reason, the examples show that the complexity of the algorithm is reduced significantly. © 2022 IEEE.

关键词： Computational complexity

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning Technique Using Similar Phenomena Training for Fuel Digital Twins of Nuclear systems 13

Transfer Learning Technique Using Similar Phenomena Training...

引用

13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023

作者： Daniell, James Kobayashi, Kazuma Bhowmik, Palash Chakraborty, Souvik Alam, Syed Nuclear Engineering and Radiation Science Missouri University of Science and Technology RollaMO United States Reactor Systems Design and Analysis Idaho National Laboratory Idaho United States Department of Applied Mechanics IIT Delhi New Delhi India

ISBN: (纸本)9780894487910

Transfer Learning (TL) is a procedure used for training Neural Networks (NN) by using pretrained models to provide information to a model with a new output. This is achieved by training one model, freezing the initial layers, and adding an aditional output to them. TL aims to reduce the training data requirement of NNs (valuable in nuclear systems), although most reserached applications focus on image processing or phenomena with more data than nuclear systems. This work aims to use similar or constituent phenomena for TL applications. Specifically, thermal expansion of SiC/SiC cladding is used to inform irradiation swelling at high temperatures. Results show that the trained TL model generalizes predictions better than traditional feedforward models, and achieves higher accuracy. Additionally, TL models were able to extrapolate information outside of the original model’s bounds more easily than feedforward modules. This type of work is expected to be used in nuclear systems due to the low data availability and benefit of generalizing outputs. © 2023 American Nuclear Society, Incorporated.

关键词： Swelling

来源：评论

学校读者我要写书评

暂无评论

Variable stiffness design optimization of fiber-reinforced composite laminates with regular and irregular holes considering fiber continuity for additive manufacturing

引用

Chinese Journal of Aeronautics 2025年第3期38卷 334-354页

作者： Yi LIU Zunyi DUAN Chunping ZHOU Yuan SI Chenxi GUAN Yi XIONG Bin XU Jun YAN Jihong ZHU School of Mechanics Civil Engineering&ArchitectureNorthwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China Key Lab for Airborne Hi-performance Electro-magnetic Window RISACJinan 130012China School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsInternational Research Center for Computational MechanicsDalian University of TechnologyDalian 116024China State IJR Center of Aerospace Design and Additive Manufacturing MIIT Lab of Metal Additive Manufacturing and Innovative DesignNorthwestern Polytechnical UniversityXi’an 710072China

Fiber-reinforced composites are an ideal material for the lightweight design of aerospace structures. Especially in recent years, with the rapid development of composite additive manufacturing technology, the design optimization of variable stiffness of fiber-reinforced composite laminates has attracted widespread attention from scholars and industry. In these aerospace composite structures, numerous cutout panels and shells serve as access points for maintaining electrical, fuel, and hydraulic systems. The traditional fiber-reinforced composite laminate subtractive drilling manufacturing inevitably faces the problems of interlayer delamination, fiber fracture, and burr of the laminate. Continuous fiber additive manufacturing technology offers the potential for integrated design optimization and manufacturing with high structural performance. Considering the integration of design and manufacturability in continuous fiber additive manufacturing, the paper proposes linear and nonlinear filtering strategies based on the Normal Distribution Fiber Optimization (NDFO) material interpolation scheme to overcome the challenge of discrete fiber optimization results, which are difficult to apply directly to continuous fiber additive manufacturing. With minimizing structural compliance as the objective function, the proposed approach provides a strategy to achieve continuity of discrete fiber paths in the variable stiffness design optimization of composite laminates with regular and irregular holes. In the variable stiffness design optimization model, the number of candidate fiber laying angles in the NDFO material interpolation scheme is considered as design variable. The sensitivity information of structural compliance with respect to the number of candidate fiber laying angles is obtained using the analytical sensitivity analysis method. Based on the proposed variable stiffness design optimization method for complex perforated composite laminates, the numerical examples cons

关键词： Variable stiffness composite laminates Discrete material interpolation scheme Normal distribution fiber optimization Discrete fiber continuous filtering strategy Additive manufacturing of composite laminates

来源：评论

学校读者我要写书评

暂无评论

Residual resampling-based physics-informed neural network for neutron diffusion equations

arXiv

引用

arXiv 2024年

作者： Zhang, Heng He, Yun-Ling Liu, Dong Hang, Qin Yao, He-Min Xiang, Di Chongqing University of Posts and Telecommunications Chongqing400065 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China CNNC Engineering Research Center of Nuclear Energy Software and Digital Reactor Chengdu610213 China

The neutron diffusion equation plays a pivotal role in the analysis of nuclear reactors. Nevertheless, employing the Physics-Informed Neural Network (PINN) method for its solution entails certain limitations. Traditional PINN approaches often utilize fully connected network (FCN) architecture, which is susceptible to overfitting, training instability, and gradient vanishing issues as the network depth increases. These challenges result in accuracy bottlenecks in the solution. In response to these issues, the Residual-based Resample Physics-Informed Neural Network(R2-PINN) is proposed, which proposes an improved PINN architecture that replaces the FCN with a Convolutional Neural Network with a shortcut(S-CNN), incorporating skip connections to facilitate gradient propagation between network layers. Additionally, the incorporation of the Residual Adaptive Resampling (RAR) mechanism dynamically increases sampling points, enhancing the spatial representation capabilities and overall predictive accuracy of the model. The experimental results illustrate that our approach significantly improves the model’s convergence capability, achieving high-precision predictions of physical fields. In comparison to traditional FCN-based PINN methods, R2-PINN effectively overcomes the limitations inherent in current methods, providing more accurate and robust solutions for neutron diffusion equations. Copyright © 2024, The Authors. All rights reserved.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

A Selection Method of Effective Metamorphic Relations 13

A Selection Method of Effective Metamorphic Relations

引用

13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022

作者： Zhang, Jie Hong, Jie Huang, Dafei Li, Meng Yan, Shiyu Gong, Helin School of Computing University of South China Hunan Hengyang China Engineering and Technology Research Center of Software Evaluation and Testing for Intellectual Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

ISBN: (数字)9781665486903

ISBN: (纸本)9781665486903

Metamorphic testing can effectively alleviate the test oracle problem. However, the program under test needs to be executed one more time;it is particularly significant to select the metamorphic relation with the highly fault-detecting ability and improve the test cost-effectiveness. Due to the limitations of existing evaluation methods in engineering applications, a validity evaluation method for metamorphic relation is presented based on single factor and multi-factor mutation scores. It can guide their priority ranking and selection. Experiments show that this method can significantly reduce test costs. © 2022 IEEE.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：