Kinematic parameter optimization of a flapping ellipsoid wing based on the data-informed self-adaptive quasi-steady model

This paper constructs an optimization framework based on the data-informed self-adaptive quasi-steady model. The framework aims at achieving a specific aerodynamic force coefficient by optimizing the kinematic parameters of the flapping motion of an ellipsoid wing. All the model coefficients of this quasi-steady model are calibrated empirically by the data-informed training. At each optimization iteration, the data-informed training is implemented by the local ridge regression, where the initial training samples are extracted from simulation examples, and the weight coefficients are calculated by the compactly supported radial basis function with the previous optimal solution as the center point. Furthermore, a numerical simulation is conducted to evaluate the accurate aerodynamic force coefficient corresponding to the current optimal solution. The relative error between the accurate simulation result and optimization objective is calculated as the convergence criteria of the optimization. Then, the effects of the kinematic parameters on the time-averaged lift coefficient are first investigated, which indicate that the in-phase flapping with high flapping angle amplitude and medium geometric angle of attack amplitude is beneficial to the lift coefficient. Moreover, the kinematic optimization is conducted for a three-dimensional flapping ellipsoid wing in the hovering mode. The results demonstrate that the leading-edge vortex is crucial for the force generation. Moreover, in one flapping period, the asymmetrical wake and two unequal lift coefficient peaks emerge under the figure-O motion pattern while the vortex structures are highly symmetrical under the figure-8 motion pattern.