针对空间锂冷堆氦氙闭式布雷顿循环系统的热力过程仿真及系统性能参数优化,开展基于换热器设计下的系统多回路、多部件耦合的系统性能分析建模及质量估计建模研究,构建了系统热力学模型与质量模型。在此基础上,通过分析不同工况下循环关键参数与系统性能指标间的作用关系,明确了不同参数对系统性能的影响方向及后续优化目标。以高效率、大比功、轻量化为目的,对系统展开基于NSGA-II遗传算法的多目标优化分析,得到了约束条件下的帕累托最优解集。通过采用拥挤距离法、TOPSIS熵权法两种决策算法,确定了不同决策算法下多目标优化后系统的最优解,最终系统效率较设计点相对提高31.12%,输出比功提高75.06%,质量减少了2.02%。
Focusing on the thermodynamic process simulation and performance parameter optimization of a space lithium-cooled reactor Helium-Xenon (HeXe) closed Brayton cycle system, this study investigates system performance analysis and mass estimation modeling. The research accounts for multi-loop and multi-component coupling based on heat exchanger design, leading to the construction of the system's thermodynamic and mass models.On this basis, the relationships between key cycle parameters and system performance indicators under various operating conditions were analyzed. This analysis clarified the influence of different parameters on system performance and defined subsequent optimization goals.Aiming for high efficiency, high specific work, and lightweight design, a multi-objective optimization analysis was conducted using the NSGA-II genetic algorithm, yielding a Pareto optimal solution set under defined constraints. Two decision-making algorithms—the crowding distance method and the entropy-weighted TOPSIS method—were employed to determine the optimal solutions. Ultimately, compared to the initial design point, the system efficiency improved by 31.12%, the output specific work increased by 75.06%, and the system mass was reduced by 2.02%.
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