DeepCFD: Efficient near-ground airfoil lift coefficient approximation with deep convolutional neural networks
This work addresses a domain-specific problem in aerodynamics for engineers and researchers, offering an incremental improvement in prediction accuracy.
The paper tackles the time-consuming problem of predicting aerodynamic coefficients for airfoils near the ground using CFD software by employing a VGG-based convolutional neural network to approximate lift-to-drag coefficients, achieving more accurate results than other CNN methods.
. 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.