压气机叶片扭曲规律的多目标三维气动优化

Multi-objective 3D Aerodynamic Optimization of Twisted Stacking of Compressor Blades

为了提高轴流压气机的等熵效率和总压比,采用基于人工神经网络及遗传算法的叶轮机械叶片三维优化设计方法,开发了一种高性能的动叶片。优化目标是在流量不减小的情况下,尽可能的提高转子叶片的总压比和等熵效率。优化仿真结果显示,优化后所获得的扭曲叶片可以有效地改善叶根处的流动分离,流动分离区明显后移,损失显著降低,在整个工作范围,等熵效率提高了1.27%-7.08%,流量和总压比也都得到了大幅度的提高。结果表明,对亚音叶片进行扭曲规律优化效果很明显,优化方法是获得高性能转子叶片的有效途径。

In order to improve the isentropic efficiency and overall pressure rate of axial compressor, a new high performance rotating blade has been developed. A 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The optimization objective is maximum overall pressure rate and isentropie efficiency of rotor blade, as well as keeping the mass flow unchanged. The optimization simulation result shows that the optimized blade effectively improves the flow separation near the hub. The flow separation area moves towards trailing edge obviously and decreases the loss greatly. In the whole operating range, the isentropic efficiency is increased byl. 27% - 7.08%. At the same time, the mass flow and overall pressure rate are improved greatly. It is found that the optimized effect of twisted stacking of the subsonic blade is quite obvious. The optimization method is an efficient way to get a rotor blade with high performance.