Airfoil

Turbulence

Engineering

Aerospace engineering

Lift coefficient

Reynolds number

Aerodynamics

Computer science

Thermodynamics

Physics

Pattern recognition (psychology)

Politics

Political science

Lift (data mining)

Artificial intelligence

Representation (politics)

Meteorology

Artificial neural network

Convolutional neural network

Machine learning

Multilayer perceptron

The adaptability of the convolutional neural network (CNN) technique for aerodynamic meta-modeling tasks is probed in this work. The primary objective is to develop suitable CNN 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 is conducted to illustrate the concept of the technique. A multi-layered perceptron (MLP) is also used for the training sets. The MLP results are compared with that of 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 a geometric representation.

Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient