Flow imaging as an alternative to non-intrusive measurements and surrogate models through vision transformers and convolutional neural networks

Abstract

A numerical framework is proposed whereby flow imaging data are leveraged to extract relevant information from flowfield visualizations. To this end, a vision transformer (ViT) model is developed to predict quantities of interest from images of unsteady flows. Here, the unsteady pressure distribution, the aerodynamic coefficients, and the skin friction coefficient are computed for an airfoil under dynamic stall as an example. The network is capable of identifying relevant flow features present in the images and associate them to the airfoil response. Results demonstrate that the model is effective in interpolating and extrapolating between flow regimes and for different airfoil motions, meaning that ViT-based models may offer a promising alternative for sensors in experimental campaigns and for building robust surrogate models of complex unsteady flows. In addition, we uniquely treat the image semantic segmentation as an image-to-image translation task that infers semantic labels of structures from the input images in a supervised way. Given an input image of the velocity field, a resulting convolutional neural network generates synthetic images of any corresponding fluid property of interest. In particular, we convert the velocity field data into pressure in order to subsequently estimate the pressure distribution over the airfoil in a robust manner. This approach proves to be effective in mapping between flowfield properties.