哈尔滨工程大学学报2026，Vol.47Issue(7)：1436-1444,9.DOI:10.11990/jheu.202602021
基于非支配排序遗传算法的系统分析程序优化技术
Research on system analysis code optimization technology based on NSGA-Ⅱ
摘要
Abstract
System analysis codes are widely used in the safety analysis of nuclear reactor systems.However,the model-ing process of reactor systems largely depends on the trial-and-error experience of technical personnel,which is time-consuming and highly empirical.To enhance the efficiency and accuracy of modeling in system analysis codes,the non-dominated sorting genetic algorithm Ⅱ is applied to optimize the system analysis process.Using the ORNL-THTF ex-perimental facility as the modeling object,sensitivity analysis was conducted with a sampling-based analysis tool to iden-tify key parameters and their ranges.An automated optimization program was established,with the objective function de-fined as the relative error between simulation results and experimental data.The ORNL-THTF model was optimized for both steady-state and transient conditions.The optimized simulation results were then compared with experimental data for validation analysis.The results indicate that,under steady-state conditions,the relative error after optimization was reduced to within 1%,while under transient conditions,the errors decreased by 9.76%and 4.56%,and the simulated trends became more consistent with the experimental data.The proposed method demonstrates advantages in achieving effective optimization and a fully automated optimization process.关键词
遗传算法/非支配排序遗传算法/模型校准/多目标优化/系统分析程序/敏感性分析/核反应堆安全分析/人工智能Key words
genetic algorithm/non-dominated sorting genetic algorithm(nsga-Ⅱ)/model calibration/multi-objective optimization/system analysis program/sensitivity analysis/nuclear reactor safety analysis/artificial intelligence分类
能源科技引用本文复制引用
姜铭雨,黄擎宇,章静,王明军,巫英伟,田文喜,苏光辉,秋穗正..基于非支配排序遗传算法的系统分析程序优化技术[J].哈尔滨工程大学学报,2026,47(7):1436-1444,9.基金项目
国家自然科学基金面上项目(12175173) （12175173）
中核集团青年英才项目. （）
