PWR Core Loading Pattern Design Assisted by Artificial Intelligence
[EN] 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...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/224371 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/224371 |
| Access Level: | acceso abierto |
| Palavra-chave: | AI PWR Core Loading 07.- Asegurar el acceso a energías asequibles, fiables, sostenibles y modernas para todos 08.- Fomentar el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo, y el trabajo decente para todos |
| Resumo: | [EN] 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. |
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