基于人工蜂群算法与NURBS的吸附式叶型优化设计

为提高轴流压气机吸附式叶型优化设计的质量,设计了一套基于人工蜂群(ABC)算法和非均匀有理B样条(NURBS)的自动优化设计系统.对人工蜂群算法的运行机制进行了深入分析,并与目前广泛使用的遗传算法(GA)进行了对比.对比发现人工蜂群算法可以更好地逼近全局最优值且收敛效率提高50%.使用NURBS对叶型进行参数化,并研究了参数化过程中的畸变问题.将抽吸参数与叶栅参数同时作为优化变量,使用该系统对一吸附式叶栅进行了优化.结果显示:抽吸槽设置在58.44%轴向弦长位置处流动损失下降明显,与优化前未抽吸叶型相比流动损失降低64.8%,气流分离得到有效抑制.

Optimization of aspirated airfoil based on artificial bee colony algorithm and NURBS

To improve the optimization design quality of aspirated airfoil of axial compressor, an automatic optimization system was developed based on artificial bee colony (ABC) algorithm and non-uniform rational B-splines (NURBS). The mechanism of ABC algorithm was researched and the performance was compared with genetic algorithm (GA) commonly used at present. The comparison indicated that ABC algorithm can obtain better global optimum and the convergence speed was 50% higher than GA. NURBS was applied to parameterize the airfoil and distortion problems in the process were studied. The optimization system was validated by optimizing an aspirated cascade, both parameters of the profile and the suction settings were treated as optimization variables. Results show that the best suction location which could bring the most performance improvement is at about 58.44% axial chord length. 64.8% decrease of the flow loss is obtained if compared with the initial cascade and the separation of the flow is restrained.