Mohamed Elrefaie, Steffen Hüttig, Mariia Gladkova, Timo Gericke, Daniel Cremers, Christian Breitsamter
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On-site aerodynamics using stereoscopic PIV and deep optical flow learning
We introduce recurrent all-pairs field transforms for stereoscopic particle image velocimetry (RAFT-StereoPIV). Our approach leverages deep optical flow learning to analyze time-resolved and double-frame particle images from on-site measurements, particularly from the ‘Ring of Fire,’ as well as from wind tunnel measurements for fast aerodynamic analysis. A multi-fidelity dataset comprising both Reynolds-averaged Navier–Stokes (RANS) and direct numerical simulation (DNS) was used to train our model. RAFT-StereoPIV outperforms all PIV state-of-the-art deep learning models on benchmark datasets, with a 68 % error reduction on the validation dataset, Problem Class 2, and a 47 % error reduction on the unseen test dataset, Problem Class 1, demonstrating its robustness and generalizability. In comparison with the most recent works in the field of deep learning for PIV, where the main focus was the methodology development and the application was limited to either 2D flow cases or simple experimental data, we extend deep learning-based PIV for industrial applications and three-component two-dimensional (3C2D) velocity estimation. We believe that this study brings the field of experimental fluid dynamics one step closer to the long-term goal of having experimental measurement systems that can be used for fast flow field estimation.
期刊介绍:
Experiments in Fluids examines the advancement, extension, and improvement of new techniques of flow measurement. The journal also publishes contributions that employ existing experimental techniques to gain an understanding of the underlying flow physics in the areas of turbulence, aerodynamics, hydrodynamics, convective heat transfer, combustion, turbomachinery, multi-phase flows, and chemical, biological and geological flows. In addition, readers will find papers that report on investigations combining experimental and analytical/numerical approaches.