A Neural Network Approach to Estimate Transient Aerodynamic Properties of a Flapping Wing System

Abstract

Understanding the causal impacts among various parameters is essential for designing micro aerial vehicles (MAVs). The simulation of computational fluid dynamics (CFD) provides us with a technique to calculate aerodynamic forces precisely. However, even a single result regularly takes considerable computational time. Machine learning, due to the advance in computer hardware, shows another approach that can speed up the analysis process. In this study, we introduce an artificial neural network (ANN) framework to predict the transient aerodynamic forces and the corresponding energy consumption. Instead of considering the whole transient changes of each parameter as inputs, we utilised the technique of Fourier transform to simplify the ANN structure for minimising the computation cost. Furthermore, two typical activation functions, rectified linear unit (ReLU) and sigmoid, were attempted to build the network. The validity of the method was further examined by comparing it with CFD simulation. The result shows that both functions are able to provide highly accurate estimations that can be implemented for model construction under this framework. Consequently, this novel approach makes it possible to reduce the complexity of analysis, study the flapping wing aerodynamics and enable a more efficient way to optimise parameters.

Keywords

• micro aerial vehicle
• flapping wing
• neural network
• aerodynamics