Multi-objective Optimization and Objective Decision Analysis of Supercritical Carbon Dioxide Nuclear Reactor System Based on CRITIC-TOPSIS Method

Supercritical carbon dioxide (S-CO₂) nuclear reactor system, due to its high efficiency and compact design, holds significant potential for applications in miniaturized reactor domains such as maritime vessels and space exploration. This study conducts a multi-dimensional comprehensive performance optimization of the system, covering overall volume, efficiency, and specific work, and proposes a data-driven objective weighting method to reduce the impact of subjective judgment on the optimization results. In this study, we first construct models for the S-CO₂ nuclear reactor recompression direct cycle system and the calculation of various optimization objectives. Subsequently, using the genetic optimization algorithm (NSGA-II), we develop a multi-objective optimization and objective decision analysis framework for the S-CO₂ reactor system based on the Criteria Importance Through Intercriteria Correlation (CRITIC) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). With maximum thermal efficiency, maximum specific work, and minimum volume as optimization objectives, the objective weights of each target are fully derived from sample data. Finally, we conduct a systematic multi-objective performance optimization and decision analysis of the entire system. The results indicate that within the given range of optimization variables, system volume carries a relatively large objective weight. A multi-criteria decision analysis is performed on the optimized Pareto frontier using the derived objective weights, leading to the identification of the optimal multi-objective parameter values under the TOPSIS decision scheme. This study provides a theoretical reference for a comprehensive and in-depth analysis of the S-CO₂ reactor system's performance.