Multiobjective Optimization Shielding Design for Compact Accelerator-Driven Neutron Sources by Application of NSGA-II and MCNP

Abstract

To find the optimal shielding design for compact accelerator-driven neutron sources (CANS) using multi-objective optimization, we developed a new method called NSGA-MC. NSGA-MC employs the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize shielding parameters based on calculations made by the Monte Carlo N-Particle Transport Code (MCNP). A layered shielding configuration with two materials of borated polyethylene (BPE) and lead (Pb) in the order of BPE/Pb/BPE/Pb for RIKEN Accelerator-driven Compact Neutron Source (RANS) was examined using this method, and two objectives were optimized simultaneously: equivalent dose rate and shielding structure weight. As a result, a trade-off relationship between the objectives was finally obtained in the form of a Pareto front. The optimization results revealed significant improvements compared with the current RANS shielding configurations in terms of both dose and weight. The results indicate that a reduction in shielding weight of about 60% can be obtained by adopting the optimized shielding structure design, without sacrificing shielding performance. The performance of the method was discussed by showing advantages of NSGA-MC over a so-called weight sum method.