Analysis Of PIV Images Of Transonic Buffet Flow By Recurrent Deep Learning Based Optical Flow Prediction
C. Lagemann, E. Mäteling, M. Klaas, W. Schröder
Chair of Fluid Mechanics and Institute of Aerodynamics, RWTH Aachen University, Germany
In the past few years, several algorithms have been proposed that leverage deep learning techniques within the data analysis workflow of particle-image velocimetry (PIV) experiments. This emerging body of work has shown that deep learning has the potential to match or outperform state-of-the-art classical algorithms in terms of efficiency, accuracy, and spatial resolution. Due to the significant relevance of PIV experiments in the broader fluid mechanics community, progress in PIV processing approaches based on state-of-the-art machine learning tools has a major impact across a range of problems in applied physics and engineering where velocity components of flow fields need to be determined. In contrast to existing methods, these approaches are general, near-automated and yield per-pixel flow estimates. Primary work on deep learning for PIV is promising, but important questions concerning the application to challenging engineering problems remain open and more scientific analyses are necessary to substantiate the general confidence amongst practitioners in such neural network based tools. This motivates us to employ a novel deep learning based PIV approach called RAFT-PIV to a challenging application of a recent research subject. That is, we use our neural optical flow approach to evaluate Background-Oriented Schlieren (BOS) and PIV images obtained by measurements of the transonic flow around a supercritical DRA-2303 profile and demonstrate that it can be used as a direct one-to-one replacement for standard cross-correlation based counterparts.