A robust data-driven model for flapping aerodynamics under different hovering kinematics

Abstract

Flapping wing micro air vehicles (FWMAVs) are highly maneuverable, bio-inspired drones that can assist in surveys and rescue missions. Flapping wings generate various unsteady lift enhancement mechanisms challenging the derivation of reduced models to predict instantaneous aerodynamic performance. In this work, we propose a robust data-driven, quasi-steady reduced order model (ROM) to predict the lift and drag coefficients within a flapping cycle. The model is derived for a rigid ellipsoid wing with different parameterized kinematics in hovering conditions. The proposed ROM is built via a two-stage regression. The first stage, defined as “in-cycle” (IC), computes the parameters of a regression linking the aerodynamic coefficients to the instantaneous wing state. The second stage, defined as “out-of-cycle,” links the IC weights to the flapping features that define the flapping motion. The training and test datasets were generated via high-fidelity simulations using the overset method, spanning a wide range of Reynolds numbers and flapping kinematics. The two-stage regressor combines ridge regression and Gaussian process regression to provide estimates of the model uncertainties. The proposed ROM shows accurate aerodynamic predictions for a wide range of kinematics. The model performs best for smooth kinematics that generates a stable leading edge vortex (LEV). Remarkably accurate predictions are also observed in dynamic scenarios where the LEV is partially shed, the non-circulatory forces are considerable, and the wing encounters its own wake.