一种仿真数据驱动的小型无人机旋翼优化设计框架

Simulation Data-Driven Rapid Optimization Design Framework for Small UAV Rotor Blades

由于无人机旋翼叶片的复杂几何形状及其空气动力学性能对其三维外形的高灵敏度,当前小型无人机旋翼叶片的优化设计普遍存在由维度灾难(Curse of Dimensionality)带来的高计算成本的问题。为了解决这一问题,提出了一种基于类函数/形状函数变换方法(Class/Shape Function Transformation)的高效三维叶片参数化模型;建立了高精度计算流体力学(Computational Fluid Dynamics)模型,与实验数据相比,所有转速下旋翼推力的仿真结果的误差在5%以内;基于仿真数据构建了旋翼扭矩和推力的高保真自扩展自适应混合代理模型(Extended Adaptive Hybrid Functions);应用了遗传算法(Genetic Algorithm)执行以悬停推力水平为约束,最小化旋翼扭矩为目标的旋翼外形优化设计。与基于翼型E387的原旋翼相比,优化模型的扭矩减少12.5%,悬停效率提高了8.32%,总重相对减少24.23%。最终,对优化后的旋翼叶片进行了流固耦合分析以验证其结构安全性与上述优化框架的可行性。

Due to the complicated geometry of the Unmanned Aerial Vehicle(UAV)rotor and the high sensitivity of the aerodynamic performance to the blade shape,many deficiencies exist in the current design and optimization of three-dimension rotor blades,such as dimensionality curse and large computational cost.To address these problem,a new optimization framework with a high-fidelity model is developed.The high-accuracy Computational Fluid Dynamics(CFD)model is built with rotor thrust errors within 5%compared with experimental data.A 3 D blade model based on the Class Function/shape Function Transformation(CST)method is proposed.A hybrid surrogate model,Extended Adaptive Hybrid Function is constructed for rotor thrust and torque.Genetic Algorithm(GA)is used to perform the optimization of the torque at hovering thrust level.Compared with the E387 baseline rotor,optimization results show that the torque and the mass reduce by 10.71%and 26.78%,respectively,and the Figure of Merit(FM)increases by 4.5%.Then,the one-way fluid-structural interaction is carried out to verify the feasibility of the proposed optimization framework.