LIFT AND DRAG FORCE PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS

Abstract


 
 
 The adaptability of the convolutional neural network (CNN) technique is probed for aerodynamic meta- modeling task. The primary objective is to develop a suitable architecture for variable flow conditions and object geometry, in addition to identifying a sufficient data preparation process. Multiple CNN structures were trained to learn the lift coefficients of the airfoils with a variety of shapes in multiple flow Mach numbers, Reynolds numbers, and diverse angles of attack. This was conducted to illustrate the concept of the methodology. Multi-layered perceptron (MLP) solutions were also obtained and compared with the CNN results. The newly proposed meta-modeling concept has been found to be comparable with the MLP in learning capability; and more importantly, our CNN model exhibits a competitive prediction accuracy with minimal constraints in geometric representation.