Development of a Core Management Tool for the MYRRHA Irradiation Research Facility

This dissertation develops a core management tool called RELOAD-M capable of optimizing reactor-core fuel loadings for MYRRHA, the future fast-spectrum research facility currently under development at SCK CEN, Belgium. Such a tool is needed for designing highly efficient loading patterns that reflect various performance objectives of the multipurpose machine. RELOAD-M can solve the single-cycle loading pattern optimization problem, using different metaheuristic optimization methods and reactor analysis codes.Two iterative population-based metaheuristics are implemented to solve the loading pattern optimization problem: Genetic Algorithm (GA) (with or without elitism) and Ant Colony Optimization (ACO). Both methods are applied to a simple core-reload problem with a known global optimum and the optimization results are compared. It is found that the elitist GA gives the most consistent results and performs best. MYRRHA reactor-core models are described and used for the neutronics evaluation of different loading patterns by reactor analysis codes tailored to fast-spectrum systems. A simple thermal-hydraulics module is implemented for the calculation of the maximum fuel-cladding temperature. All employed models give results that are sufficiently accurate and fast enough for optimization purposes. A MYRRHA loading pattern optimization problem is solved that aims at maximizing the facility’s irradiation performance expressed in terms of the fast-neutron fluence achieved in reactor experimental channels. Three types of constraints are included in the problem: limited number of available fuel assemblies, maximum allowed fuel-cladding temperature, and end-of-cycle criticality condition. It is concluded that both the GA and ACO algorithms provide feasible solutions that outperform intuitively designed loading patterns. However, the resulting improvement is only marginal.