Multiobjective optimization of porous medium for efficient transpiration cooling of hypersonic vehicles using genetic algorithm

Transpiration cooling has been proven an effective method for reducing heat flux on the surfaces of high-speed vehicles. This study investigates the effects of porous medium properties, employing a black-box optimization method to determine the optimal length, thickness, and porosity for a porous medium in a transpiration cooling system on a flat plate under hypersonic laminar flow. The objectives include optimizing thermal effectiveness, coolant consumption, and the weight and cost of the porous material. A multiobjective genetic optimization algorithm is directly integrated with a Computational Fluid Dynamics (CFD) solver, and 1562 CFD simulations were conducted to identify the optimal configuration. The results demonstrate that the length and porosity of the porous medium more significantly impact thermal effectiveness than the thickness. Furthermore, the optimization identified a configuration for the porous medium that, when compared to the original case, shows reductions in length, thickness, and porosity of 3.5%, 11%, and 29%, respectively. Additionally, there were average improvements in thermal effectiveness and coolant consumption of 4.56% and 3.9%, respectively, while the weight and cost of the porous material increased by 3.73% and 3.65%, respectively.