The ability of forecasting flapping frequency of flexible filament by artificial neural network
作者： M. Fayed , M. Elhadary , H. Ait Abderrahmane , Bassem Nashaat Zakher
DOI: 10.1016/J.AEJ.2019.11.007
关键词: Simulation 、 Process (computing) 、 Backpropagation 、 Computational fluid dynamics 、 Artificial neural network 、 Aerodynamics 、 Experimental data 、 Numerical analysis 、 Computer science 、 Flapping
摘要: Abstract Artificial Neural Networks (ANNs) are reliable and computationally inexpensive compared to numerical methods such as CFD simulations experimental investigations in aerodynamics research. In this article, an Network (ANN) has been introduced predict the flapping frequencies of a filament placed 2-D soap-film tunnel. The multi-layer perception (MLP) networks have used developing while backpropagation Levenberg-Marquardt algorithm was perform training ANN. A part data considered for process rest prediction test suggested ANN results indicate that it can periodic with good accuracy. However, fails when presents amplitude modulation.
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