This article implements the thermodynamic design space data-mining and multi-objective optimization of two typical supercritical carbon dioxide (SCO₂) Brayton cycles: the recompression Brayton cycle (SCO₂RBC) and the recompression reheating Brayton cycle (SCO₂RRBC). Firstly, a mathematical model with more constraints has been established for the two Brayton cycles. The maximum errors of the mathematical model relative to the references for the SCO₂RBC and SCO₂RRBC are 2.5%, 3.5% respectively. Then, three data-mining techniques (global sensitivity analysis by ANOVA, single factor analysis, coupling analysis by SOM) are successively applied to explore the design space. As a result, four key design parameters have been identified: the maximum and the minimum cycle temperatures, the pressure ratio, and the shunt flow percentage. And they present different non-linear effects on the cycles’ performances (monotone increasing or decreasing, parabolic type with extreme point). It is also found that in order to achieve a global optimum, the maximum cycle temperature should be close to its upper bound, while the minimum cycle temperature tends to approach its lower bound, and a larger pressure ratio of compressor as well as a smaller shunt flow percentage is also required. Therefore, the data-mining methods are heuristic and can provide useful information for quickly searching the global optimums of SCO₂ Brayton Cycles. Finally, a hybrid optimization algorithm is introduced to optimize the Brayton cycles. It shows that the search efficiency of the hybrid algorithm is 3 ∼ 4 times higher than the traditional stochastic algorithms. For the given design space, the cycle efficiency of the SCO₂RRBC is improved by 10 percentage points. The hybrid algorithm coupled with the data-mining techniques are likely to speed up the design process of Brayton cycles, and have the potential to further improve the cycles’ performances.

本文实现了两种典型的超临界二氧化碳（SCO ₂）布雷顿循环：再压缩布雷顿循环（SCO ₂ RBC）和再压缩再加热布雷顿循环（SCO ₂ RRBC）的热力学设计空间数据挖掘和多目标优化。首先，对两个布雷顿循环建立了具有更多约束条件的数学模型。数学模型相对于 SCO ₂ RBC 和 SCO ₂参考的最大误差RRBC分别为2.5%、3.5%。然后，依次应用三种数据挖掘技术（ANOVA 的全局敏感性分析、单因素分析、SOM 的耦合分析）来探索设计空间。因此，确定了四个关键设计参数：最高和最低循环温度、压力比和分流百分比。并且它们对循环的性能表现出不同的非线性影响（单调递增或递减，具有极值点的抛物线型）。还发现，为了达到全局最优，最高循环温度应接近其上限，而最低循环温度则趋于接近其下限，压缩机压比较大，分流器较小。还需要流量百分比。所以，₂布雷顿循环。最后，引入混合优化算法来优化布雷顿循环。结果表明，混合算法的搜索效率比传统的随机算法提高了 3 ～ 4 倍。对于给定的设计空间，SCO ₂ RRBC的循环效率提高了 10 个百分点。混合算法与数据挖掘技术相结合可能会加快布雷顿循环的设计过程，并有可能进一步提高循环的性能。