An Intelligent Optimization Method for Preliminary Design of Lead-Bismuth Reactor Core Based on Kriging Surrogate Model

Abstract

To meet the numerous application demands of lead-bismuth reactors, different design optimization tasks need to be conducted on these reactors based on the existing reactor core solutions. However, the design optimization of lead-bismuth reactors is a challenging task because it is a complex, multi-dimensional, and nonlinear constrained problem. To resolve these issues and improve the efficiency of design optimization, a new method, called the KSM-OLHS-SEUMRE method, based on the Kriging surrogate model (KSM), orthogonal Latin hypercube sampling (OLHS), and space exploration and unimodal region elimination (SEUMRE) algorithm is proposed in this study. Based on this method, a design optimization program of lead-bismuth reactors (DOPPLER-K) is developed, which realizes functions like sample point generation, optimization analysis, pre-post processing of reactor calculation, coupling of the Reactor Monte Carlo (RMC) calculation code and the Steady-state Thermal-hydraulic Analysis Code (STAC). Further, taking lead-bismuth reactors SPALLER-4 and URANUS as prototypes, the proposed intelligent optimization method for preliminary design of lead-bismuth reactor core is verified. The results show that this method can rapidly and accurately find the target scheme satisfying the optimization conditions, and it is three orders of magnitude faster than pure Monte Carlo calculation. Compared with the initial core scheme of URANUS, the optimization rates of fuel loading, total core mass, active zone volume, and total core volume are reduced by 10.8, 11.5, 18.1, and 17.1%, respectively. These results validate the feasibility and efficacy of the proposed method for design optimization of lead-bismuth reactor core.