基于遗传算法的2 维叶型优化设计

为得到具有宽广低损失工作范围的静子叶片,采用基于遗传算法的优化平台对3个不同载荷水平的静子叶栅叶型的型面、安装角及弦长进行优化设计。通过比较3个叶栅优化前后的正常特性线,认为尾缘"负载荷区域"对改善叶栅性能是有益的,且其对叶栅性能的改善效果与叶片载荷有关。结果表明:优化后叶栅性能在设计点和非设计点都有明显改善;叶片表面等熵马赫数分布类似控制扩散叶型,最大载荷点位置前移,叶型前段弯度增加,尾缘出现"负载荷区域"。

Optimization Design on 2D Blade Profiles Based on Genetic Algorithm

In order to get stator blades with low loss in a wide range of attack angle, three stator blades profiles of different load levels were optimized by the genetic algorithm. According to contrasting the normal characteristic behind and after three cascades optimized, the negative load region on the trailing edge was significant for improving the cascade performance, which affects the optimized results and blade loadings of cascade performance. The results show that the optimized cascade performance has obvious improvements for design conditions and off design conditions. The distributions of isentropic Mach number on the blade surfaces is similar with the controlled diffusion profiles, the position of maximum loads moves forward, camber increased in front of blades, and the ＂negative load region＂ appears on the trailing edge.