Low-Reynolds-number effect is an important factor affecting the performance of compressors and other main components of high-altitude long-endurance unmanned aerial vehicles. To improve the flow condition and reduce flow loss under the condition of high altitude and low Reynolds number (Re), this paper proposes an optimization process based on a surrogate model, which combines the class-shape transformation method (CST), the Latin hypercube sampling (LHS) method, the light gradient-boosting machine algorithm (LightGBM), and a genetic algorithm (GA) to optimize a high-subsonic compressor profile. The surrogate model is verified to be accurate and can be used in the optimization process. The accuracy of the GA is higher than the other algorithms under common test functions. The optimization results are verified by numerical simulation, and the flow differences before and after optimization are compared, especially the flow within the boundary layer. By changing the blade shape, the optimization process adjusts the loading distribution to delay the transition of the optimized blade on the suction surface, which changes the turbulent reattachment in the laminar separation bubble (LSB) into laminar reattachment. Therefore, the mixing loss induced by the turbulent reattachment of the LSB and the wake loss of turbulent separation at the trailing edge are significantly reduced, and the performance of the compressor profile is finally improved. In addition, turbulent separation of the optimized profile is delayed reducing the range of the wake region on the suction surface. By this optimization process, the reduction of total pressure loss coefficient at Re of 2.5×105, 3.5×105, and 4.5×105 are 16.32%, 20.76%, and 22.16%, respectively.
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