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Prediction of the Evolution of the Nuclear Reactor Core Parameters Using Artificial Neural Network by Palmi, Krzysztof; Kubinski, Wojciech; Darnowski, Piotr is a scholarly article available to read on EtoBox.

What is Prediction of the Evolution of the Nuclear Reactor Core Parameters Using Artificial Neural Network about?

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

Author

Palmi, Krzysztof; Kubinski, Wojciech; Darnowski, Piotr

Published

2023

Language

EN