Performance evaluation of a surrogate model to predict effective dose under hypothesized severe accidents of pressurized water reactor based on neural network

IF 2.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy Pub Date : 2025-07-01 DOI:10.1016/j.anucene.2025.111701

Chang Hyun Song , Wonjun Choi , Sung Joong Kim

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引用次数: 0

Abstract

Numerous studies utilizing severe accident analysis codes have been conducted to evaluate the effectiveness of various mitigation strategy combinations. However, due to various sensitivity factors such as the execution time and characteristics of system performance, an exhaustive analysis of all scenarios is infeasible. Interestingly, recent studies have shown that application of the machine learning technique is beneficial for reducing the calculation cost and this study developed a deep neural network-based surrogate model to assess the effectiveness of a severe accident management strategy. The effectiveness of the strategy was evaluated based on the effective dose over a 72 hrs, which is an ultimate standard for measuring the ability to mitigate a severe accident. Additionally, the model’s performance was further improved by incorporating input parameters that can capture the progression of accident, such as the timing of major events and accident classification based on event branches using a source term grouping method.

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基于神经网络的压水堆严重事故有效剂量预测代理模型性能评价

利用严重事故分析代码进行了大量研究，以评估各种缓解战略组合的有效性。然而，由于执行时间和系统性能特点等各种敏感因素，不可能对所有场景进行详尽的分析。有趣的是，最近的研究表明，机器学习技术的应用有利于降低计算成本，本研究开发了一个基于深度神经网络的代理模型来评估严重事故管理策略的有效性。该战略的有效性是根据72小时内的有效剂量来评估的，这是衡量减轻严重事故能力的最终标准。此外，通过引入能够捕捉事故进展的输入参数，例如主要事件的时间和使用源项分组方法基于事件分支的事故分类，进一步提高了模型的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annals of Nuclear Energy 工程技术-核科学技术

CiteScore

4.30

自引率

21.10%

发文量

632

审稿时长

7.3 months

期刊介绍： Annals of Nuclear Energy provides an international medium for the communication of original research, ideas and developments in all areas of the field of nuclear energy science and technology. Its scope embraces nuclear fuel reserves, fuel cycles and cost, materials, processing, system and component technology (fission only), design and optimization, direct conversion of nuclear energy sources, environmental control, reactor physics, heat transfer and fluid dynamics, structural analysis, fuel management, future developments, nuclear fuel and safety, nuclear aerosol, neutron physics, computer technology (both software and hardware), risk assessment, radioactive waste disposal and reactor thermal hydraulics. Papers submitted to Annals need to demonstrate a clear link to nuclear power generation/nuclear engineering. Papers which deal with pure nuclear physics, pure health physics, imaging, or attenuation and shielding properties of concretes and various geological materials are not within the scope of the journal. Also, papers that deal with policy or economics are not within the scope of the journal.