Research Reactor Core Loading Optimization: Enabling Machine Learning Applications by Employing Surrogate Models

Recent advancements in machine learning (ML) algorithms and applications have made it possible for ML models to solve complex problems, such as reactor core loading optimization, which represents a multiobjective optimization problem with a high degree of freedom. This study aims to provide a proof of concept for an ML-based core loading optimization scheme aimed at research reactors. As a case study we selected the High Flux Reactor in Petten, the Netherlands. Two optimization algorithms are used in this study: genetic algorithm (GA) and reinforcement learning (RL). The goal is to increase the thermal neutron flux at specific locations in the reactor core while adhering to established safety constraints. The optimization schemes also utilized neural network–based surrogate models to substitute for the computationally intensive core calculations. The surrogate models are used to predict core parameters (such as the neutron flux, control rod position, and heat flux) for any given loading pattern. Our results show that ML-based core loading optimization has the potential to become a viable alternative to the traditional core optimization methods. Both the GA and RL methods were able to generate core loading patterns where the neutron flux was similar in most target locations to the results obtained with the traditional method.