低雷诺数轴流压气机叶型气动优化

To investigate the optimal design method of axial flow compressor airfoils under low Reynolds number， the prediction accuracy of a quasi-3D solver MISES for low Reynolds number flow was first verified， and the predicted transition position for an airfoil （the V103 airfoil） is comparable to the experimental data with an error lower than 5%. Then based on a multiple circular arc （MCA） airfoil parameterization， the Strengthened Elitist Genetic Algorithm （SEGA） were used to build a quasi-3D aerodynamic optimization design platform for axial flow compressor airfoil. Taking the total pressure loss coefficient as the optimization objective function， and the outlet flow angle as constraint function， the V103 airfoil was comprehensively optimized for single-point under low Reynolds number and two-point at both high and low Reynolds numbers. The results show that， the laminar separation bubble is suppressed after single-point optimization under low Reynolds number， and the total pressure loss coefficient is reduced by 31.31%， and the performance is significantly improved， while the total pressure loss of the airfoil is reduced by 3.05% and 3.03%， respectively， under high and low Reynolds number after two-point optimization . The results of this paper prove the effectiveness of the research method and the feasibility of the airfoil optimization considering Reynolds number.