A new design optimization method is proposed for the problem of high-precision aerodynamic design of multistage axial compressors. The method mainly contains three aspects: full-blade surface parametrization can significantly reduce the number of control variables per blade row and increase the degrees of freedom of the leading edge blade angle compared with the traditional semiblade parametric method; secondly, the artificial bee colony algorithm improved initialization and food source exploration and exploitation mechanism to enhance the global optimization ability and convergence speed, and a distributed optimization system is built on the supercomputing platform based on this method; finally, a phased optimization strategy based on the “synchronous change in multirow blades” is proposed, and expert experience is introduced to achieve a better balance between exploration and exploitation. The optimization method is applied to the AL-31F four-stage low-pressure compressor. As a result, the adiabatic efficiency is improved by 0.67% and the surge margin is improved by 3.1% under the premise that the total pressure ratio and mass flow rate satisfy the constraints, which verifies the effectiveness and engineering practicality of the proposed optimization method in the field of multistage axial flow compressor aerodynamic optimization.

中文翻译：

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基于全叶片表面参数化和分相策略的多级轴流式压缩机设计优化

针对多级轴流压缩机高精度气动设计问题，提出了一种新的设计优化方法。该方法主要包括三个方面：与传统的半叶片参数化方法相比，全叶片表面参数化可以显着减少每列控制变量的数量，增加前缘叶片角度的自由度；其次，人工蜂群算法改进了初始化和食物源探索开发机制，增强了全局优化能力和收敛速度，并基于该方法在超级计算平台上构建了分布式优化系统；最后，提出了一种基于“多排叶片同步变化”的分阶段优化策略，并引入专家经验，在探索和开发之间取得更好的平衡。该优化方法应用于AL-31F四级低压压缩机。结果表明，在总压比和质量流量满足约束条件下，绝热效率提高0.67%，喘振裕度提高3.1%，验证了所提优化方法的有效性和工程实用性。多级轴流压气机空气动力学优化领域。