Optimizing the size of a printed circuit heat exchanger by multi-objective genetic algorithm

The printed circuit heat exchanger (PCHE) has been proven to be one of the best choices for the supercritical CO₂ Brayton cycle because of its ability to withstand high temperature and pressure with compact size. In this study, size optimization is carried out to improve the overall thermal-hydraulic performance of a PCHE. The temperature rise and the pressure drop are selected as the optimization objectives. The multi-objective evolutionary algorithm (MOEA) is adopted to conduct the optimization process due to the presence of two conflicting objectives. The non-dominated sorting genetic algorithms II (NSGA-II) coupled with a surrogate model is used to optimize the size of the PCHE. The surrogate model depicts the relations between design variables and objective parameters. The optimal solutions are finally obtained, and the results indicate that the influence of cross flow section on heat transfer will be greater with the increase of the heat transfer coefficient. The pressure drop can be reduced by nearly 26% compared with the reference case if the temperature rises of the optimal solution and the reference case are the same. And the temperature rise can be increased by 0.4% if the pressure drops of the optimal solution and the reference case remain the same. The distribution characteristics of design variables corresponding to Pareto-optimal solutions are revealed as well.