The optimal choice of the few-group structure for full-core transient analyses is still an open issue in reactor physics, especially for fast systems (as the lead fast reactor). One possible approach to select the group boundaries is represented by heuristic search algorithms, such as evolutionary ones. In this paper, a genetic algorithm coupled with the SIMMER code is employed to determine optimized six-group boundaries for the analysis of the ALFRED reactor. The Serpent Monte Carlo code is adopted to produce both the fine-group cross section library and the fine-group flux, used as a figure of merit to drive the genetic optimisation. The results show that the algorithm is indeed able to find satisfactory solutions that comply with the set objectives and can be reasonably interpreted in light of the underlying physics of the considered core.

Genetic algorithm-based optimisation of the few-group structure for lead fast reactor analysis / Massone, MATTIA VINCENZO EDOARDO; Abrate, Nicolo'; Nallo, GIUSEPPE FRANCESCO; Valerio, Domenico; Dulla, Sandra; Ravetto, Piero. - ELETTRONICO. - (2022), pp. 1388-1397. ((Intervento presentato al convegno PHYSOR 2022 International Conference tenutosi a Pittsburgh, PA, U.S.A. nel May 15-20, 2022.

Genetic algorithm-based optimisation of the few-group structure for lead fast reactor analysis

Mattia Massone;Nicolo Abrate;Giuseppe Francesco Nallo;Domenico Valerio;Sandra Dulla;Piero Ravetto
2022

Abstract

The optimal choice of the few-group structure for full-core transient analyses is still an open issue in reactor physics, especially for fast systems (as the lead fast reactor). One possible approach to select the group boundaries is represented by heuristic search algorithms, such as evolutionary ones. In this paper, a genetic algorithm coupled with the SIMMER code is employed to determine optimized six-group boundaries for the analysis of the ALFRED reactor. The Serpent Monte Carlo code is adopted to produce both the fine-group cross section library and the fine-group flux, used as a figure of merit to drive the genetic optimisation. The results show that the algorithm is indeed able to find satisfactory solutions that comply with the set objectives and can be reasonably interpreted in light of the underlying physics of the considered core.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2970481