Shock loss is the primary source of total pressure loss of transonic axial compressors. Reducing the shock by redesigning the geometry of rotor is of great interest for turbomachinery designers. However, the complex flow field involving shock waves, shock-boundary interaction, intense secondary flows, etc., in the compressor makes the design of rotor difficult. The conventional method of design and optimization is computationally intensive and time-costly. This study introduces an inverse design method to design rotor blades corresponding to prescribed isentropic Mach number distributions with no modification of flow-governing equations. An artificial neural network is trained to predict the isentropic Mach number distributions of any deformed blades. Then, with the pattern search optimization, the blade corresponding to the prescribed isentropic Mach number distributions can be achieved. When the aerodynamic parameter database is calculated and the neural network is obtained, this method can design large numbers of blades of changed isentropic Mach number distributions immediately, without modifying the computational fluid dynamics (CFD) flow solver. The design process is fully automatic and efficient. In this study, NASA Rotor 37 is redesigned and optimized as test cases. Some analysis on the influence of blade shape on aerodynamic characteristics of the rotor is represented in this study.

冲击损失是跨音速轴流压缩机总压力损失的主要来源。通过重新设计转子的几何形状来减少冲击是涡轮机械设计人员的极大兴趣。然而，压缩机中涉及激波、激波边界相互作用、强烈二次流等的复杂流场使得转子的设计变得困难。传统的设计和优化方法计算量大且耗时。本研究引入了一种逆向设计方法来设计与规定的等熵马赫数分布相对应的转子叶片，而无需修改流动控制方程。训练人工神经网络来预测任何变形叶片的等熵马赫数分布。然后，通过模式搜索优化，可以实现对应于规定等熵马赫数分布的叶片。当计算空气动力学参数数据库并获得神经网络时，该方法可以立即设计出大量等熵马赫数分布变化的叶片，而无需修改计算流体动力学（CFD）流动求解器。设计过程是全自动且高效的。在这项研究中，NASA Rotor 37 作为测试用例进行了重新设计和优化。本研究对叶片形状对转子气动特性的影响进行了一些分析。无需修改计算流体动力学 (CFD) 流动求解器。设计过程是全自动且高效的。在这项研究中，NASA Rotor 37 作为测试用例进行了重新设计和优化。本研究对叶片形状对转子气动特性的影响进行了一些分析。无需修改计算流体动力学 (CFD) 流动求解器。设计过程是全自动且高效的。在这项研究中，NASA Rotor 37 作为测试用例进行了重新设计和优化。本研究对叶片形状对转子气动特性的影响进行了一些分析。