机器学习辅助关联预测 VVER-1000 燃料中的裂变气体分数和氢浓度

IF 1.9 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy Pub Date : 2024-11-21 DOI:10.1016/j.anucene.2024.111073

Yalcin Ilteris Kaan , Khashayar Sadeghi , Seyed Hadi Ghazaie , Ekaterina Sokolova , Victor Modestov , Vitaly Sergeev , Puzhen Gao

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引用次数: 0

摘要

本研究旨在利用基因表达编程作为进化机器学习方法，开发用于预测燃料循环过程中裂变气体分数和氢气浓度的相关性。使用著名的 FRAPCON 代码生成稳态条件下的直接数据集。通过两步敏感性分析，确定对相关性发展最有影响的参数。使用 Wilks 统计方法生成 59 种情景，均匀分布输入参数的不确定性，从而得出 95 % 的置信度。氙、氪和氦的均方误差为 0，而氢的均方误差为 59.36，因为分数值在 0 到 1 之间，浓度范围在 5 PPM 到 200 PPM 之间。R2 值超过 0.97，表明相关精度很高。相关性达到的高精确度表明，根据 Wilk 方法选择 59 个样本数据集足以获得超过 95 % 的精确度。

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Machine learning-assisted correlations for prediction of fission gas fractions and hydrogen concentration in VVER-1000 fuel

This study aims to develop correlations for predicting the fission gas fractions and hydrogen gas concentration during a fuel cycle, using gene expression programming as an evolutionary machine learning approach. The well-known FRAPCON code is used for generating a straightforward dataset under steady-state conditions. The two-step sensitivity analysis is carried out to identify the most influential parameters for correlation development. Wilks’ statistical method is used to generate 59 scenarios to distribute input parameter uncertainties evenly, which leads to a confidence level of 95 %. The mean squared error for xenon, krypton, and helium is 0, while hydrogen exhibited a value of 59.36 since fraction values are between 0 and 1 and concentration ranged from 5 PPM to 200 PPM.

R^{2}

values exceeded 0.97, indicating strong correlation accuracy. The high accuracy achieved from the correlations demonstrates that selecting a 59-sample dataset based on Wilk’s method is sufficient to obtain accuracy exceeding 95 %.

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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.