Abstract
A combination of finite element modeling (FEM) and artificial neural network (ANN) is employed to estimate the surface pressure of flapping wing micro air vehicle. The ANN training patterns are prepared by varying the surface pressure distributions and calculating their associated strains through FEM. Through the well-trained network, the surface pressure can be estimated instantly by the strains measured during flapping. The maximum flapping frequency that represents the strength of flapping wings is then predicted using maximum strain criterion, in which the critical strain was measured using the standard ASTM specimens. The relation between the flapping frequencies and strains is a curve fitted by the data measured under lower and safer flapping frequencies.
