Thermodynamic design space data-mining and multi-objective optimization of SCO2 Brayton cycles

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 similar to 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.