Study on Risk-informed Safety Evaluation and Optimization under Adaptive Sampling in Nuclear Power Plant

Li Linfeng; Xu Anqi; Dong Xiaomeng; Zhang Zhen; Yang Ming; Wen Ting; Liu Yong

doi:10.13832/j.jnpe.2024.S2.0223

Volume 45 Issue S2

Jan. 2025

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Article Navigation > Nuclear Power Engineering > 2024 > 45(S2): 223-230

Li Linfeng, Xu Anqi, Dong Xiaomeng, Zhang Zhen, Yang Ming, Wen Ting, Liu Yong. Study on Risk-informed Safety Evaluation and Optimization under Adaptive Sampling in Nuclear Power Plant[J]. Nuclear Power Engineering, 2024, 45(S2): 223-230. doi: 10.13832/j.jnpe.2024.S2.0223

Citation:

Li Linfeng, Xu Anqi, Dong Xiaomeng, Zhang Zhen, Yang Ming, Wen Ting, Liu Yong. Study on Risk-informed Safety Evaluation and Optimization under Adaptive Sampling in Nuclear Power Plant[J]. Nuclear Power Engineering, 2024, 45(S2): 223-230. doi: 10.13832/j.jnpe.2024.S2.0223

Citation:

Li Linfeng, Xu Anqi, Dong Xiaomeng, Zhang Zhen, Yang Ming, Wen Ting, Liu Yong. Study on Risk-informed Safety Evaluation and Optimization under Adaptive Sampling in Nuclear Power Plant[J]. Nuclear Power Engineering, 2024, 45(S2): 223-230. doi: 10.13832/j.jnpe.2024.S2.0223

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Study on Risk-informed Safety Evaluation and Optimization under Adaptive Sampling in Nuclear Power Plant

doi: 10.13832/j.jnpe.2024.S2.0223

Shenzhen Key Laboratory of Nuclear and Radiation Safety, Shenzhen University, Shenzhen, Guangdong, 518060, China

Received Date: 2024-06-21
Rev Recd Date: 2024-10-10
Publish Date: 2025-01-06

Abstract

Abstract

In order to meet the dual requirements of safety and economy of advanced reactors, the Deterministic Safety Analysis and Probabilistic Safety Analysis are coupled in this study. Based on the Risk-informed System Analysis (RISA), a RISA optimization method under adaptive sampling strategy is proposed to realistically evaluate the safety margin of nuclear power plants. It focuses on solving the problems of massive sampling times and low calculation efficiency under high precision requirements. Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) Algorithm are used to train the surrogate model, which replaces a large number of Best Estimation and Uncertainty Analysis code simulations. The adaptive sampling strategy is used to identify the limit surface and reduce the sampling range and times. Taking the Small Break Loss of Coolant Accident (SBLOCA) as an example, the test results show that, compared with the random sampling RISA results, the peak temperature of fuel cladding predicted by the surrogate model is close, and the calculation time is reduced by more than 50%. Therefore, this method can support the practical engineering application of RISA in the future, and provide realistic and accurate decision support for the risk-informed design, operation and management.
- Nuclear safety analysis,
- Adaptive sampling,
- Surrogate model,
- Risk-informed system analysis (RISA),
- Risk-informed safety margin characterization (RISMC)

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References(18)

References

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