| Issue |
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
|
|
|---|---|---|
| Article Number | 17010 | |
| Number of page(s) | 9 | |
| Section | Artificial Intelligence & Digital in Nuclear Applications - Quantum Computing | |
| DOI | https://doi.org/10.1051/epjconf/202430217010 | |
| Published online | 15 October 2024 | |
https://doi.org/10.1051/epjconf/202430217010
PWR Core Loading Pattern Design Assisted by Artificial Intelligence
1 Depto. de Informática de Sistemas y Computadores, Universitat Politècnica de València, Camí de Vera s/n, 46022, Spain
2 Research Institute for Industrial, Radiophysical and Environmental Safety (ISIRYM), Universitat Politècnica de València, Camí de Vera s/n, 46022, Spain
* Corresponding author: pabpalfo@gap.upv.es
Published online: 15 October 2024
Core design is the a priori study of the behavior of a reactor core throughout a cycle between two fuel reloads. There is a growing interest in using Artificial Intelligence (AI) tools to accelerate this type of calculation. Coupled thermal-hydraulic/neutronic calculations allow access to many variables of special interest to develop a digital twin (metamodel), which can be used, for instance, for pattern optimization, since it presents restrictions from the point of economics, safety and licensing. This work presents a neural network (NN) trained to obtain a metamodel, which will be used to determine different optimal configurations of the fuel elements based on certain criteria.
© The Authors, published by EDP Sciences, 2024
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