Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks

Understand

An approximation model based on convolutional neural networks (CNNs) is proposed for flow field predictions.

• The CNN is used to predict the velocity and pressure field in unseen flow conditions and geometries given the pixelated shape of the object.
• In particular, we consider Reynolds Averaged Navier-Stokes (RANS) flow solutions over airfoil shapes.
• The CNN can automatically detect essential features with minimal human supervision and shown to effectively estimate the velocity and pressure field orders of magnitude faster than the RANS solver, making it possible to study the impact of the airfoil shape and operating conditions on the aerodynamic forces and the flow field in near-real time.