Abstract
To improve the performance of electrically assisted turbochargers (EATs), the influences of the hub profile and the casing profile on EAT performance were numerically studied by controlling the upper and lower endwall profiles. An artificial neural network and a genetic algorithm were used to optimize the endwall profile, considering the total pressure ratio and the isentropic efficiency at the peak efficiency point. Different performances of the prototype EAT and the optimized EAT under variable clearance sizes were discussed. The endwall profile affects an EAT by making the main flow structure in the endwall area decelerate and then accelerate due to the expansion and contraction of the meridional surface, which weakens the secondary leakage flow of the prototype EAT and changes the momentum ratio of the clearance leakage flow and the separation flow in the suction surface corner area. Because the tip region flow has a more significant influence on EAT performance, the optimal casing scheme has a better effect than the hub scheme. The optimization design can increase the isentropic efficiency of the maximum efficiency point by 1.5%, the total pressure ratio by 0.67%, the mass flow rate by 1.2%, and the general margin by 6.4%.