研究轴流压气机优化设计问题,在高负荷跨音速轴流压气机相应的叶型优化设计工作中,针对压气机等熵效率过低,由于转子叶顶通道出现流动分离,导致损失过大.为提高等熵效率,结合人工神经网络与遗传算法对压气机转子的吸力面50％叶展以上叶型进行调整.优化后的新叶型可以有效地改善叶顶流动结构,抑制分离,在总压比基本不变的情况下使压气机峰值效率提升约1.7试验证明,叶型优化设计有显著效果,同时也指出了单一优化方案的局限性.%An optimization design was carried out for a high-loading transonic axial compressor. A flow separation was found around the tip region of rotor blade which mainly enlarged loss end up with an insufficient isentropy efficiency. To increase the isentopy efficiency of the compressor, the design process adjusted the suction side profile of the upper 50% span based on the method consisting of artificial neural net work and genetic algorithm. As the result, the new profile greatly improved the structure of the flow field, suppressed the flow separation, and finally brought en increase of peak-efficiency about 1.7%, while the mass-flow rate in the same back pressure was obviously enlarged. The design shows an outstanding effect and also the limit of single-method optimization.
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