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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

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

SSRN

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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

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Optimizing Structural Modification Operation for B+-Tree on Byte-Addressable Devices

Optimizing Structural Modification Operation for B+-Tree on ...

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IEEE International Conference on Computer design: VLSI in Computers and Processors, (ICCD)

作者： Dingze Hong Jinlei Hu Jianxi Chen Dan Feng Jian Liu Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System of MoE School of Computer Science and Technology Huazhong University of Science and Technology The Fifth Electronics Research Institute of MIIT China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Persistent Memory (PM) offers both byte-address ability and non-volatility, making it well-suited for accelerating $\mathrm{B}^{+}$ -Tree indexes. However, existing persistent $\mathrm{B}^{+}$ -Tree indexes face significant performance challenges due to high structural modification operation (SMO) overhead. SMOs often result in costly item migrations and increased tail latency, which severely degrade the overall performance. In this paper, we present SSTree, a high-performance $\mathrm{B}^{+}$ – Tree index specifically optimized to address SMO overhead. SSTree introduces three key innovations: (i) efficient leaf node expansion using a list of subnodes to postpone expensive node splits, (ii) delegated fingerprints to speed up search operations across subnodes, and (iii) proactive subnode compaction that employs out-of-place updates to optimize item organization. Our evaluation demonstrates that SSTree delivers up to $4.38\times$ higher write throughput and up to $62\times$ lower tail latency compared to state-of-the-art persistent $\mathrm{B}^{+}$ -Tree indexes.

关键词： Technological innovation Tail Organizations Fingerprint recognition Throughput Computer crashes Delays Indexes Optimization Faces

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Cooperative Relaying and Jamming design for Secure LPWAN Wireless Communication

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China Communications 2025年第4期22卷 100-116页

作者： Chen Dianxia Ma Xiaoshan Yang Lin Yu Kan Feng Zhiyong Education Tianjin Ren’ai CollegeTianjin 301636China Academy of Military Sciences of PLA Beijing 100091China National Key Laboratory of Science and Technology on Information System Security Systems Engineering InstituteAMSPLABeijing 100091China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China

Physical layer security methods based on joint relay and jammer selection(JRJS)have been widely investigated in the study of secure wireless *** from current works on JRJS schemes,which assumed that the global channel state information(CSI)of the eavesdroppers(Eves)was known beforehand,then the optimal relaying and jamming relays were *** importantly,the time complexity of selecting optimal jamming relay is O(N^(2)),where N is the maximum number of relays/*** this paper,for the scenario where the source wants to exchange the message with the destination,via relaying scheme due to longer communication distance and limited transmission power,in the presence of multiple Eves,with the assumption of Eves'perfect CSI and average CSI,we propose two kinds of JRJS *** particular,the time complexity of finding the optimal jammer can be reduced to O(N).Furthermore,we present a novel JRJS scheme for no CSI of Eves by minimizing the difference between expected signal and interfering signal at the ***,simulations show that the designed methods are more effective than JRJS and other existing strategies in terms of security performance.

关键词： joint relay and jammer selection physical layer security secrecy outage probability

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design of Intentional Eye-blink Signal Acquisition and Control system Based on Pressure Sensor 7

Design of Intentional Eye-blink Signal Acquisition and Contr...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Shao, Xueqi Ning, Haonan Yang, Wenjun Niu, Yafeng Southeast University School of Mechanical Engineering Department of Industrial Design Nanjing China National Key Laboratory of Science and Technology on Aircraft Control Xi'an China

ISBN: (纸本)9781665478571

Eye-blink control means that users blink intentionally to realize human-computer interaction. An intentional eye-blink signal acquisition and control system based on flexible pressure sensor is designed, which aims to control the object through simple eye movements such as blinking and winking. The system uses two pressure sensors to collect the blink signals at the orbicularis oculi muscles of the left and right eyes respectively. The software part realizes the acquisition and recognition of six intentional eye-blink signals, and transmits blink-control instruction signals through Bluetooth, so as to realize the control of the remote-controlled vehicle. The advantage of this system is that it is not affected by illumination and does not need GUI interface for control. The availability of the system is qualified after system test. © 2022 IEEE.

关键词： Human computer interaction

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Modeling and Program Development of Supercritical CO2 Sub-Channel Based on Modelica Language

SSRN

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SSRN 2023年

作者： Guo, Jia Ming, Yang Zhao, Fulong Fang, Huawei Yin, Jingwei Tian, Ruifeng College of Nuclear Science and Technology Harbin Engineering University Harbin150001 China Heilongjiang Provincial Key Laboratory of Nuclear Power System & Equipment Harbin Engineering University Harbin150001 China Key Laboratory of Nuclear Reactor System Design Technology of China Nuclear Power Research and Design Institute Chengdu610000 China

Small nuclear reactor (SMR) system has many advantages, including low construction cost, short construction period and fast economic benefits. SMR has broad prospects in power supply in remote areas, small power grids, low-temperature heating, nuclear hydrogen production, seawater desalination, nuclear powered ships, etc. In order to study the hydraulics and thermal parameters of supercritical carbon dioxide as coolant in SMR, this paper developed a supercritical carbon dioxide reactor sub-channel analysis program based on Modelica language. Based on the sub-channel analysis method, the reactor coolant flow channel is divided in several control bodies, then the program calculates hydraulics and thermal parameters of each control body. In order to verify the correctness of the model and program, sub-channel program will calculate the axial temperature distribution, radial temperature distribution, coolant flow rate and power level under steady-state conditions. Then compare these calculations with KAIST MMR in Korea. In the transient characteristic analysis, this paper will analyze the reactor power increase, reactivity insertion and coolant flow loss accident, then evaluate the safety performance of the reactor. This paper relies on theoretical modeling and program development method, studying the sub-channel thermal hydraulic characteristics of steady-state and transient conditions, which provides theoretical reference and program support for the study of transient response characteristics and safety characteristics of supercritical carbon dioxide reactor. © 2023, The Authors. All rights reserved.

关键词： Temperature distribution

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Intra Fast Mode Decision Algorithm and Hardware design for AVS2

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

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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)

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Experimental Investigation of Droplet Transient Behavior During Reflood Cooling Process of Single-Rod Channel

SSRN

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SSRN 2023年

作者： Chen, Mingjing Chen, Deqi Liu, Haiong Liu, Hanzhou Zhong, Lei Deng, Jian Key Laboratory of Low-grade Energy Utilization Technologies and Systems Chongqing University Ministry of Education Chongqing400044 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610041 China

The reflood process after a loss of coolant accident (LOCA) in a nuclear reactor involves complex two-phase flow with low pressure and flow rate, as well as boiling phase transition. Accurately predicting the transient behavior of liquid droplets during the dispersed flow stage is crucial for evaluating reactor accidents, including peak cladding temperature (PCT). However, existing prediction models for liquid droplets in dispersed flows are typically based on quasi steady-state annular flow models and do not account for transient behavior, such as the propulsion characteristics of the quench front during strong transient flooding processes, and the formation mechanism of large droplets. In this study, we present the results of a visualization experiment of reflood cooling in a single-rod channel to investigate the development mechanisms of droplet formation and fragmentation. We observe the evolution behavior of the vapor-liquid interface during the reflood process and quantitatively analyze the transient size distribution characteristics of the droplets. Based on these findings, the transient characteristics of the quench front propagation during reflood, Kelvin-Helmholtz (K-H) instability, hot wall ejection mechanisms for droplet formation, and aerodynamic and capillary breakup mechanisms for droplet breakage, as well as entrainment and evaporation of droplets under high-speed steam are considered. A prediction model for droplet transient behavior during single-rod channel reflood cooling is established, and the results are compared with experimental data. © 2023, The Authors. All rights reserved.

关键词： Cooling

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Simulation of Nuclear reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning

Simulation of Nuclear Reactor Accident Scenarios using Physi...

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Electronic Information Engineering and Computer science (EIECS), 2021 International Conference on

作者： Yang Yu Yufei Xie Wenlin Wang Guohua Wu Haitao Lan Enbo Lin Ping An Zibin Sun Haichuan Zhang Yixian Wu National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R 2 : 0.735).

关键词： Radioactive materials Accuracy Monte Carlo methods Transfer learning Neural networks Robustness Data models Fuels Inductors Accidents

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

arXiv

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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

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