Large-scale design optimisation of boiling water reactor bundles with neuroevolution

Abstract We combine advances in deep reinforcement learning (RL) with evolutionary computation to perform large-scale optimisation of boiling water reactor (BWR) bundles using CASMO4/SIMULATE3 codes; capturing fine details, radial/axial fuel heterogeneity, and real-world constraints. RL constructs neural networks that learn how to assign fuel and poison enrichment by narrowing the search space into the areas where human/physics knowledge demonstrate merit. Evolution strategies diversify the search in these areas, through obtaining guidance from RL candidates. With very efficient/parallel implementation, our optimisation approach is able to solve a coupled multi-zone BWR bundle optimisation with ~ 40 constraints. The methodology is applied to a GE14-10×10 bundle, showing the ability of neuroevolution to find ~ 100 feasible designs. The optimal bundle has 7 axial zones with non-uniform enrichment radially and axially. The results of this work also demonstrate that our neuroevolution methodology is sufficiently generic to adapt to other assembly and reactor designs with minor adjustments.