  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.
