基于并行遗传算法的向心涡轮气动优化设计

Aerodynamic Optimization Design of Radial Turbine Based on Parallel Genetic Algorithm

为了提高向心涡轮轮周效率,保持流量、膨胀比不变,以流道、安装角、型面为设计变量,基于并行遗传算法的优化方法,对某微型发动机向心涡轮叶片气动性能进行多变量耦合的自动优化设计,利用商用软件NUMECA进行3维流场计算分析,并比较了优化前后向心涡轮转子的总体性能。结果表明:在设计工况下,向心涡轮的轮周效率提高近3%,流量也略有增加,膨胀比近似不变;在非设计工况下,优化叶片效率均高于初始叶片的,向心涡轮的整体性能得到提高。该算法不仅可自动实现多变量耦合优化,而且可高效地得到高气动性能叶片。

In order to improve the wheel efficiency of radial turbine and keep the mass flow and expansion ratio unchanged,taking the channel,installation angle and profile as design variables,the multi-variables coupling optimization design of dynamic performance for a micro aeroengine radial turbine blade was conducted based on the parallel genetic algorithm.The three dimensional flow field of the radial turbine was calculated and analyzed by NUMECA,and the initial and optimization performance was compared.The results show that the wheel efficiency has an increase of 3％ approximately,mass flow is slightly increased,and the expansion ratio keeps approximately unchanged at design condition.The efficiency of optimized blade is higher than that of the initial blade,and the overall performance of the radial turbine is increased at off-design conditions.The parallel genetic algorithm not only could realize mulii-variables coupling,but also could obtain high aerodynamic performance blade efficiently.