Journal Article

Prediction of aerodynamic flow fields using convolutional neural networks

Duraisamy, Karthik, Pan, Shaowu, Afshar, Yaser, Kaushik, Shailendra, Bhatnagar, Saakaar

2019

Overview

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 as training data. The CNN can automatically detect essential features with minimal human supervision and is 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. The use of specific convolution operations, parameter sharing, and gradient sharpening are shown to enhance the predictive capabilities of the CNN. We explore the network architecture and its effectiveness in predicting the flow field for different airfoil shapes, angles of attack, and Reynolds numbers.

• Publisher: Springer Berlin Heidelberg,Springer,Springer Nature B.V
• Subject: Aerodynamic forces / Aerodynamics / Analysis / Angle of attack / Artificial neural networks / Classical and Continuum Physics / Computational fluid dynamics / Computational Science and Engineering / Convolution / Engineering / Flow control / Neural networks / Original Paper / Predictions / Shape recognition / Sharpening / Theoretical and Applied Mechanics