Prediction of the evolution of the nuclear reactor core parameters using artificial neural network

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

Annals of Nuclear Energy Pub Date : 2024-09-07 DOI:10.1016/j.anucene.2024.110891

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引用次数: 0

Abstract

The main aim of the research was to design, implement and investigate an Artificial Neural Network (ANN) to predict the behavior of selected parameters of a nuclear reactor core. The studied core was a typical power-generating Pressurized Water Reactor (PWR). The PARCS v3.2 nodal-diffusion core simulator was used to generate training and validation data. The ANN was implemented using Python 3.8 code with Google’s TensorFlow 2.0 library. The effort was based to a large extent on the process of automatic transformation of generated data, which was later used in the process of the ANN development. Various ANN architectures were studied to obtain better accuracy of prediction. In this study, a special focus was put on the prediction of the fuel cycle length for a given core loading pattern. In addition, a conversion of the input data was applied, allowing for very good accuracy of the cycle length prediction ( $> 99$ %).

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利用人工神经网络预测核反应堆堆芯参数的变化

这项研究的主要目的是设计、实施和研究人工神经网络（ANN），以预测核反应堆堆芯选定参数的行为。所研究的堆芯是一个典型的发电压水堆（PWR）。PARCS v3.2 节点扩散堆芯模拟器用于生成训练和验证数据。使用 Python 3.8 代码和谷歌的 TensorFlow 2.0 库实现了 ANN。这项工作在很大程度上是基于生成数据的自动转换过程，这些数据随后被用于 ANN 的开发过程。为了获得更高的预测准确性，对各种 ANN 架构进行了研究。在这项研究中，重点是预测给定堆芯装载模式下的燃料循环长度。此外，还对输入数据进行了转换，从而获得了非常高的循环长度预测精度（99%）。

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.