进气畸变下风扇叶型多目标优化（英文）

Multi-objective Optimization of a Fan Airfoil Adaptive for the Inlet Distortion

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(controlled diffusion 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%。

Boundary Layer Ingestion (BLI) propulsion systems can significantly reduce the aircraft fuel burn but it can bring the inlet distortion problem to the fan and reduce its aerodynamic performance. To reduce the profile loss and enhance the distortion- tolerant ability of a fan airfoil operating under the fixed inlet distortion, this paper presents an optimization strategy for a controlled diffusion airfoil (CDA) optimization through the multi- objective genetic algorithm (MOGA) with Back- Propagation (BP) neural network.The optimization objectives are to minimize the profile loss and its sensitivity to the incidence angle. After the optimization, a fan airfoil adaptive for the fixed inlet distortion has been obtained. Compared with the conventional CDA, the profile loss of this fan airfoil is decreased under the positive incidence, and its sensitivity to the incidence angle is decreased by 32%. Simultaneously, the low loss incidence range is widened by 21%.