| dc.creator | Casas Molina, Víctor J. | |
| dc.creator | Hernández-Solís, A. | |
| dc.creator | Merino-Rodríguez, Iván | |
| dc.creator | Romojaro Otero, Pablo | |
| dc.date | 2023-03-03T13:22:59Z | |
| dc.date | 2023-03-03T13:22:59Z | |
| dc.date | 2022 | |
| dc.date.accessioned | 2024-05-02T20:30:33Z | |
| dc.date.available | 2024-05-02T20:30:33Z | |
| dc.identifier | http://repositorio.ucm.cl/handle/ucm/4462 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9274704 | |
| dc.description | ANICCA is the nuclear fuel cycle code developed by the Belgian Nuclear Research Centre (SCK CEN). Nuclear Fuel Cycle codes are of special importance for the assessment of the scenarios and the study of nuclear reactor fleet deployment and decommissioning. In said studies, the flow and inventory of Spent Nuclear Fuel (SNF) are of paramount importance, which are calculated through the irradiation module. In this work, a new approach to the irradiation module is presented. The approach is based on two direct neural networks which predict the final isotopic inventory in the SNF by using the initial fuel composition and the discharge burnup as inputs. These neural networks have been trained in Keras by a database produced with SERPENT2 continuous energy Monte Carlo transport code. Said models are dedicated to two of the most common nuclear fuel technologies for pressurized water reactors: UOX and MOX. Results showed a nice agreement between the new and the classical approach. At the same time, a quicker response in simulations was reported, especially for complex scenarios that involve multi-recycled fuel strategies (known as closed cycle). Thanks to the new method the prebuilt libraries needed in the previous module can be avoided, and so are the simplifications brought by the use of these. | |
| dc.language | en | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.source | Proceedings - International Conference of the Chilean Computer Science Society, SCCC, 2022, 1-8 | |
| dc.subject | Nuclear fuels | |
| dc.subject | Computational modeling | |
| dc.subject | Monte Carlo methods | |
| dc.subject | Inductors | |
| dc.subject | Codes | |
| dc.subject | Radiation effects | |
| dc.subject | Physics | |
| dc.title | Deep learning models as an approach to nuclear fuel irradiation processes in pressurized water reactors | |
| dc.type | Article | |
