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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.09932 |
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| _version_ | 1866913665835335680 |
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| author | Elrefaie, Mohamed Hüttig, Steffen Gladkova, Mariia Gericke, Timo Cremers, Daniel Breitsamter, Christian |
| author_facet | Elrefaie, Mohamed Hüttig, Steffen Gladkova, Mariia Gericke, Timo Cremers, Daniel Breitsamter, Christian |
| contents | 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 real-time 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 to 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 3D flow field estimation. As we apply the trained network to three-dimensional highly turbulent PIV data, we are able to obtain flow estimates that maintain spatial resolution of the input image sequence. In contrast, the traditional methods produce the flow field of $\sim$16$\times$ lower resolution. 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 real-time flow field estimation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09932 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning Elrefaie, Mohamed Hüttig, Steffen Gladkova, Mariia Gericke, Timo Cremers, Daniel Breitsamter, Christian Fluid Dynamics 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 real-time 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 to 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 3D flow field estimation. As we apply the trained network to three-dimensional highly turbulent PIV data, we are able to obtain flow estimates that maintain spatial resolution of the input image sequence. In contrast, the traditional methods produce the flow field of $\sim$16$\times$ lower resolution. 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 real-time flow field estimation. |
| title | Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2401.09932 |