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Research on the method of predicting CEFR core thermal hydraulic parameters based on adaptive radial basis function neural network

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成果类型:
期刊论文
作者:
Yi, Jinhao;Ji, Nan;Zhao, Pengcheng;Wu, Hong
通讯作者:
Zhao, P.
作者机构:
[Wu, Hong; Yi, Jinhao; Ji, Nan; Zhao, Pengcheng] Univ South China, Sch Nucl Sci & Technol, Hengyang, Hunan, Peoples R China.
通讯机构:
[Zhao, P.] S
School of Nuclear Science and Technology, Hunan, China
语种:
英文
关键词:
adaptive gradient descent;fast reactor;forecasting;radial basis function neural network arithmetic;thermal hydraulic parameters
期刊:
Frontiers in Energy Research
ISSN:
2296-598X
年:
2022
卷:
10
页码:
961901
基金类别:
This work is supported by the ‘‘Coupled Response Mechanism of Lead-based Fast Reactor Hot Pool and Cold Pool Thermal stratification under Asymmetric Thermal Load Condition and its Influence on Natural Circulation Performance” project of National Natural Science Foundation of China (Grant Nos. 11905101).
机构署名:
本校为第一机构
院系归属:
核科学技术学院
摘要:
Alterations in thermal hydraulic parameters directly affect the safety of reactors. Accurately predicting the trends of key thermal hydraulic parameters under various working conditions can greatly improve reactor safety, thereby effectively preventing the occurrence of nuclear power plant accidents. The thermal hydraulic characteristic parameters in the reactor are affected by many factors, in order to preliminarily study whose forecasting methods and determine the feasibility of neural network forecasting, the China Experimental Fast Reactor (CEFR) is selected as the research target in this ...

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