# Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks

## TL;DR

This paper introduces a CNN-based model that rapidly predicts flow fields around airfoils, significantly reducing computation time compared to traditional RANS simulations, enabling near-real-time aerodynamic analysis.

## Contribution

The study presents a novel CNN architecture tailored for flow field prediction over airfoils, demonstrating high accuracy and speed in diverse conditions, surpassing conventional methods.

## Key findings

- CNN predicts velocity and pressure fields orders of magnitude faster than RANS.
- The model effectively generalizes across different airfoil shapes and flow conditions.
- Robustness to noise and specific convolution techniques enhance prediction accuracy.

## Abstract

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. The use of specific convolution operations, parameter sharing, and robustness to noise are shown to enhance the predictive capabilities of CNN. We explore the network architecture and its effectiveness in predicting the flow field for different airfoil shapes, angles of attack, and Reynolds numbers.