MYRRHA core loading pattern optimization using metaheuristics

The goal of the nuclear reactor in-core fuel management is to analyze and propose new core configurations to be loaded into a reactor core at the beginning of fuel cycles. Each new core loading has to comply with the nuclear facility's operating objectives, limits, and safety requirements. Given a stock of available nuclear fuel, a search among the possible candidates is usually done in order to identify a fuel loading pattern that also optimizes the fuel usage during the operation. This can be achieved using metaheuristic optimization techniques. In this paper, a special computer program that addresses the problem of in-core fuel management optimization of MYRRHA, a new irradiation facility currently under development at SCK•CEN, is briefly described. A simple MYRRHA in-core fuel management optimization problem is solved using two different metaheuristic methods – Genetic Algorithm (GA) and Ant Colony Optimization (ACO). The obtained results are compared and the efficiency of the applied algorithms is discussed afterwards. The MYRRHA in-core fuel management optimization problem discussed in the paper aims at a maximum irradiation performance of the machine expressed in terms of the neutron flux achieved at experimental channels. It is shown that both GA and ACO provide feasible solutions that outperform an intuitively designed loading pattern.status: publishe