In this paper, a transonic compressor cascade was optimized to improve its aerodynamic performance. A new blade parameterization method with 16 control variables was first proposed to fit the shapes of the suction and the pressure side, as well as the leading edge. Then, the Kriging surrogate-model-based genetic algorithm (GA) was used to optimize the performance of the transonic cascade. The optimization algorithm is effective in reducing the total pressure loss while extending the working range of the cascade. The results show that the total pressure loss coefficient could be reduced by 11% at the best airflow angle and the working range could be extended by 6.9% for the optimized cascade in two-dimensional simulations. Similar improvement results could also be obtained in the simulations of their linear cascade cases. Detailed analyses show that the relative maximum thickness positions of the optimized blades move forward by about 10% to the leading edge, and the radii of curvature of the front half of the suction and pressure surfaces increase, compared with the initial blade. This makes the front half of the optimized blades look more closely like a wedge. Consequently, the passage shock strength is reduced and the shock changes from the passage normal shock to oblique shock. The weakened shock strength leads to the disappearance of the flow separation caused by the shock boundary layer interaction on the suction surfaces of the optimized blades, and results in a narrowed wake width at the outlet section.

No abstract

Highly three–dimensional and complex flow structures are closely related to the aerodynamic losses occurring in the transonic axial–flow compressor. The large eddy simulation (LES) approach was adopted to study the aerodynamic performance of the NASA rotor 37 for the cases at the design, the near stall (NS), and the near choke (NC) flow rate. The internal flow vortex topology was analyzed by the Q–criterion method, the omega (Ω) vortex identification method, and the Liutex identification method. It was observed that the Q–criterion method was vulnerable to being influenced by the flow with high–shear deformation rate, especially near the end–wall regions. The Ω method was adopted to recognize the three–dimensional vortex structure with a higher precision than that of the Q–criterion method. Meanwhile, the Liutex vortex identification method showed a good performance in vortex identification, and the corresponding contribution of Liutex components in the vortex topology was analyzed. The results show that the high–vortex fields around the separation line and reattachment line had high vortex components in the x–axis, the tip clearance vortices presented a high–vortex component in the y–axis, and the suction side corner vortex possessed high–vortex components in the y– and z–axes.