进气畸变下风扇叶型多目标优化
作者： National; Key; Laboratory; of; Science; and; Technology; on; Aero-Engine; Aero-Thermodynamics; School; of; Energy; and; Power; Engineering; Beihang; University; Beijing; China; Collaborative; Innovation; Center; of; Advanced; Aero-Engine; Beijing; China; Chengdu; Aircraft; Industrial; (Group); Co.; Ltd.; Chengdu; China
摘要：BLI(Boundary Layer Ingestion)推进系统能显著降低飞机耗油率,但会随之带来风扇进气畸变问题,并严重影响其气动性能。为降低进气畸变条件下风扇叶型的损失并提高其抗畸变能力,选取某可控扩散叶型CDA(controlleddiffusion airfoil)为研究对象,以最小化叶型损失和损失对攻角的敏感性为优化目标,通过多目标遗传算法MOGA(Multi-objective genetic algorithm)结合BP(Back-Propagation)神经网络对叶型进行多目标优化。经过优化,得到了在进气畸变条件下有较好气动性能的风扇叶型。与初始叶型相比,叶型在正攻角下的损失显著降低,同时其损失对攻角的敏感性降低了32%,低总压损失范围拓宽了21%。
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