The complex wing motion of insects and small birds leads to high lift production in nature. Specific lift-enhancing mechanisms arise from the combination of a horizontal stroke motion and a secondary wing pitching motion. Research around flapping wing nano air vehicles (FWNAVs) copies this wing motion to fly small drones, in an effort to benefit from the same mechanisms. A flapping wing aerodynamic model can predict the force production of the FWNAV and helps to understand the influence of the wing kinematics thereon. This work starts from a quasi-steady model expression for the aerodynamics and proposes a wind-tunnel procedure to identify the force coefficients in the model empirically. The procedure is based on a test case performed by the AVT-202 research team that combines wing translation and wing rotation, imitating the aerodynamic condition of the flapping wing in a simplified setup. Comparison of measurements and model simulations demonstrates that the procedure can be used to update the model parameters for our specific FWNAV configuration, leading to an empirical relation for the force coefficients. Simulations with the updated model using the empirical relations show good agreement with measured force production for a wide range of input variables.

昆虫和小鸟复杂的翅膀运动导致自然界产生高升力。特定的升力增强机制产生于水平冲程运动和辅助机翼俯仰运动的组合。围绕扑翼纳米飞行器 (FWNAV) 的研究复制了这种机翼运动来飞行小型无人机，以期从相同的机制中受益。扑翼空气动力学模型可以预测 FWNAV 的力产生，并有助于了解机翼运动学对其的影响。这项工作从空气动力学的准稳态模型表达式开始，并提出了一个风洞程序来凭经验确定模型中的力系数。该程序基于 AVT-202 研究团队执行的一个测试案例，该案例结合了机翼平移和机翼旋转，在简化的设置中模仿扑翼的空气动力学条件。测量和模型模拟的比较表明，该程序可用于更新我们特定 FWNAV 配置的模型参数，从而导致力系数的经验关系。使用经验关系对更新模型进行的模拟显示，对于广泛的输入变量，与测得的力产生非常吻合。