Estimating BOS Image Deformation With A Lightweight CNN

Abstract

We introduce a Convolutional Neural Network (CNN) to post-process recordings obtained by means of Background Oriented Schlieren (BOS), a popular technique to visualize compressible/convective flows. To reconstruct BOS image deformation, we devised a lightweight network (LIMA) that has comparatively fewer parameters to train, allowing the deployment of the network on embedded GPU hardware. To train the CNN, we introduce a novel strategy based on the generation of synthetic images with random, irrotational displacement field that mimic those provided by real BOS recording. This allows us to generate a large number of training examples at minimal computational cost. To assess the accuracy of the reconstructed displacement, we consider test cases consisting of synthetic images with sinusoidal displacement as well as images obtained in a real experimental study of flow past and inside a heated hollow hemisphere. By comparing the prediction of the CNN with conventional post-processing techniques such as Direct Image Correlation (DIC) and Particle Image Velocimetry (PIV) cross-correlation, we show that LIMA gives more accurate and robust results for the synthetic example. When applied to the recordings from the real experiment, all methods provide consistent deformation fields. As they offer similar or better accuracy at a fraction of the computational costs, properly designed CNNs offer a valuable alternative to conventional post-processing techniques for BOS experiments.