Application of Genetic Algorithms in In-Core Fuel Management for Small Modular Reactors (SMRs)

Abstract

This work presents a methodology for in-core fuel management in Small Modular Reactors (SMRs) using Genetic Algorithms (GAs). A Python-based system will be developed to enable the interaction between the GAs and the nodal neutronic calculation code developed at UFABC, which provides the main reactor characteristics at the beginning of the cycle. Initially, a one-dimensional (1D) approach will be adopted, with plans to later perform two-dimensional (2D) simulations applied to a reference SMR. The initial fuel management proposal aims to optimize the power distribution, making it more uniform within the reactor core and thereby enhancing operational safety.