A nuclear reactor based on MIT BEAVRS benchmark was used as a typical power generating Pressurized Water Reactor (PWR). The PARCS v3.2 nodal-diffusion core simulator was used as a full-core reactor physics solver to emulate the operation of a reactor and to generate training, and validation data for the ANN. The ANN was implemented with dedicated Python 3.8 code with Google's TensorFlow 2.0 library. The effort was based to a large extent on the process of appropriate automatic transformation of data generated by PARCS simulator, which was later used in the process of the ANN development. Various methods that allow obtaining better accuracy of the ANN predicted results were studied, such as trying different ANN architectures to find the optimal number of neurons in the hidden layers of the network. Results were later compared with the architectures proposed in the literature. For the selected best architecture predictions were made for different core parameters and their dependence on core loading patterns. In this study, a special focus was put on the prediction of the fuel cycle length for a given core loading pattern, as it can be considered one of the targets for plant economic operation. For instance, the length of a single fuel cycle depending on the initial core loading pattern was predicted with very good accuracy (>99%). This work contributes to the exploration of the usefulness of neural networks in solving nuclear reactor design problems. Thanks to the application of ANN, designers can avoid using an excessive amount of core simulator runs and more rapidly explore the space of possible solutions before performing more detailed design considerations.

翻译：以MIT BEAVRS基准题中的核反应堆作为典型动力压水堆（PWR），采用PARCS v3.2节块扩散堆芯模拟器作为全堆物理求解器，模拟反应堆运行过程并生成人工神经网络（ANN）的训练与验证数据。ANN基于专用Python 3.8代码结合谷歌TensorFlow 2.0库实现。研究重点在于对PARCS模拟器生成数据进行适当的自动转换，该数据随后用于ANN开发流程。本研究探讨了多种提升ANN预测结果精度的方法，包括尝试不同ANN架构以确定网络隐藏层最佳神经元数量，并将结果与文献中提出的架构进行对比。针对选定的最优架构，预测了不同堆芯参数及其对装料模式的依赖性。研究特别关注特定装料模式下的燃料循环长度预测，因其被视为核电站经济运行的关键目标之一。例如，基于初始装料模式预测单次燃料循环长度的精度可达99%以上。本工作探索了神经网络在解决核反应堆设计问题中的实用性。通过应用ANN，设计人员可避免执行大量堆芯模拟器运算，从而在开展更详细的设计论证前更快速地探索可行解空间。