应用人工神经网络预测小型模块化压水堆 k-eff 和峰值系数的研究

Tran Chung Le, Thi Dung Nguyen, Viet Phu Tran
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摘要

使用人工神经网络(ANN)方法的机器学习(ML)正被应用于基于大数据集学习的核反应堆所需参数的预测。ML 模型的计算速度通常更快,而准确度则与物理模拟器相当。在这项工作中,建立并训练了一个多层感知器网络,用于预测小型模块化压水堆(PWR)的 k-eff 和调峰因子。结果与使用反应堆物理代码系统(即 SRAC2006)获得的结果进行了比较。比较结果表明,ML 模型具有良好的一致性和更高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A study on the application of artificial neural network to predict k-eff and peaking factor of a small modular PWR
Machine learning (ML) using artificial neural network (ANN) methods is being applied to predict required parameters for nuclear reactors based on learning from big data sets. The ML models usually give faster calculation speed while the accuracy is good agreement with physical simulators. In this work, a multi-layer perceptron network was built and trained to predict k-eff and peaking factor of a small modular pressurized water reactor (PWR). The results are compared with those obtained by using a reactor physics code system, i.e. SRAC2006. The comparison shows good agreement accuracy and higher performance of the ML models.
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