Criticality Maximization in Nuclear Reactors: A Fuel Saving Approach through Genetic Algorithms

Abstract

Maximizing criticality in nuclear reactors is related to the optimization of a fundamental parameter in the design of a reactor fuel element, representing a significant challenge in nuclear engineering. This study proposes an innovative methodology using genetic algorithms to optimize the size of nuclear fuel cells, with the aim of maximizing criticality. Using MCNP5 simulation software, we performed a series of simulations with variations in the size of the fuel cells. The data obtained was subjected to fifth-degree polynomial adjustments and optimized by genetic algorithms, identifying an optimal cell size that maximizes criticality without significantly increasing fuel consumption. This study not only contributes to the literature by integrating computational intelligence techniques into nuclear engineering, but also provides a replicable methodology for future research in nuclear reactor design.