The ability of forecasting flapping frequency of flexible filament by artificial neural network

Abstract

Artificial Neural Networks (ANNs) are reliable and computationally inexpensive compared to numerical methods such as CFD simulations and experimental investigations in aerodynamics research. In this article, an Artificial Neural Network (ANN) has been introduced to predict the flapping frequencies of a filament placed in a 2-D soap-film tunnel. The multi-layer perception (MLP) networks have been used in developing the Artificial Neural Network while the backpropagation Levenberg-Marquardt algorithm was used to perform the training of the ANN. A part of the experimental data was considered for the training process while the rest for the prediction test of the suggested ANN. The ANN results indicate that it can predict the frequencies of the periodic flapping with good accuracy. However, it fails when the flapping presents amplitude modulation.