Abstract

Safety and lightweight optimization is significant in shielding design of compact nuclear reactors. The optimal solutions obtained by the existing intelligent optimization algorithm of shielding design is not ideal when there are redundant optimization objectives. In this paper, we propose a novel multi-objective shielding optimization method which couples NSGA-Ⅱ, deep neural network (DNN) and principal component analysis (PCA) for optimization by eliminating redundant objectives. The efficacy of the method is demonstrated by solving up to a plate shielding model and Savannah marine nuclear power reactor shielding model.

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