Structure optimization of turbocharger air-to-air intercooler for an air-cooled engine based on porous-media-model

Abstract

Enhancing the flow and heat-transfer performances of the turbocharger intercooler can improve the overall performance of vehicle engines. Given the immense quantity of grids involved, numerically simulating the full-scale model of intercoolers becomes infeasible. Currently, most of the existing simulations and optimizations of intercoolers are based on the local fin model. A few studies try to employ the porous-media-model to investigate full-scale intercoolers, but they usually focus on water-to-air intercoolers. The applicability of the porous-media-model for full-scale air-to-air intercoolers simulation and optimization still needs further verification. Hence, it is urgent to explore efficient numerical simulation and optimization method for the full-scale air-to-air intercooler. Based on the porous-media-model, this study proposes an efficient numerical simulation method of the full-scale air-to-air intercooler. And it is validated by the air-cooled diesel engine bench test. The mean relative errors of the hot-side pressure-drop and the outlet temperature are 4.28% and 1.52% respectively. Then, on the basis of the efficient numerical simulation method, the optimal-Latin-hypercube sampling method, the general regression neural network (GRNN) surrogate model, and the multi-objective genetic algorithm (GA) are further combined to construct an optimization method of the full-scale intercooler. The optimization method is able to decrease the hot-side pressure-drop of the intercooler by 6.83% and maintain the heat-transfer performance. Finally, the flow mechanisms of performances improvement of the optimized intercooler are analyzed.