DeepCFD: Efficient near-ground airfoil lift coefficient approximation with deep convolutional neural networks

TL;DR

This paper introduces DeepCFD, a deep convolutional neural network approach that efficiently predicts near-ground airfoil lift coefficients, significantly reducing computational time compared to traditional CFD methods.

Contribution

The paper presents a novel neural network model based on VGG architecture for accurate and fast prediction of airfoil lift-to-drag ratios near ground surfaces.

Findings

VGG-based CNN outperforms other CNN methods in accuracy.

DeepCFD significantly reduces prediction time.

The method effectively uses airfoil images and data for training.

Abstract

. Predicting and calculating the aerodynamic coefficients of airfoils near the ground with CFD software requires much time. However, the availability of data from CFD simulation results and the development of new neural network methods have made it possible to present the simulation results using methods like VGG, a CCN neural network method. In this article, lift-to-drag coefficients of airfoils near the ground surface are predicted with the help of a neural network. This prediction can only be realized by providing data for training and learning the code that contains information on the lift-to-drag ratio of the primary data and images related to the airfoil cross-section, which are converted into a matrix. One advantage of the VGG method over other methods is that its results are more accurate than those of other CNN methods.