dorsal/arxiv
View SchemaRapid Prediction of Three-Dimensional Scour Flow around Bridge Piers via Body-Fitted Coordinate-Based U-Net
| Authors | Tokio Morimoto |
|---|---|
| Categories | |
| ArXiv ID | 2601.06506vv1 |
| URL | https://arxiv.org/abs/2601.06506 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Predicting three-dimensional (3D) turbulent flows around bridge piers is a prerequisite for assessing local scour, a primary cause of infrastructure failure. While Computational Fluid Dynamics (CFD) captures complex flow features - such as horseshoe vortices - its high cost hinders real-time risk assessment. This study presents a physics-aware deep learning surrogate using a Body-Fitted Coordinate (BFC) system, BFC-UNet, designed to rapidly reconstruct 3D Reynolds-Averaged Navier-Stokes (RANS) solutions on curved domains. Unlike voxel-based Convolutional Neural Networks (CNNs) prone to staircase errors, the proposed architecture leverages a BFC system to predict the bed shear stress accurately. By transforming the physical O-grid into a canonical computational space, the model preserves the geometric integrity of the no-slip boundary. Trained on 2,304 simulations parameterized by inlet velocity and scour depth, BFC-UNet predicts velocity, pressure, and bed shear stress distributions with an R2 value of > 0.98. It infers a full 3D domain (200,000 cells) in just 8 milliseconds on a single Graphics Processing Unit (GPU) - achieving a speed-up of five orders of magnitude over the CFD solver. Crucially, the model captures the topological evolution of vortex structures, including wake expansion and diving flows. These findings position BFC-UNet as a promising foundation for real-time digital twins, bridging rigorous fluid mechanics with data-driven efficiency.
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"abstract": "Predicting three-dimensional (3D) turbulent flows around bridge piers is a prerequisite for assessing local scour, a primary cause of infrastructure failure. While Computational Fluid Dynamics (CFD) captures complex flow features - such as horseshoe vortices - its high cost hinders real-time risk assessment. This study presents a physics-aware deep learning surrogate using a Body-Fitted Coordinate (BFC) system, BFC-UNet, designed to rapidly reconstruct 3D Reynolds-Averaged Navier-Stokes (RANS) solutions on curved domains. Unlike voxel-based Convolutional Neural Networks (CNNs) prone to staircase errors, the proposed architecture leverages a BFC system to predict the bed shear stress accurately. By transforming the physical O-grid into a canonical computational space, the model preserves the geometric integrity of the no-slip boundary. Trained on 2,304 simulations parameterized by inlet velocity and scour depth, BFC-UNet predicts velocity, pressure, and bed shear stress distributions with an R2 value of \u003e 0.98. It infers a full 3D domain (200,000 cells) in just 8 milliseconds on a single Graphics Processing Unit (GPU) - achieving a speed-up of five orders of magnitude over the CFD solver. Crucially, the model captures the topological evolution of vortex structures, including wake expansion and diving flows. These findings position BFC-UNet as a promising foundation for real-time digital twins, bridging rigorous fluid mechanics with data-driven efficiency.",
"arxiv_id": "2601.06506",
"authors": [
"Tokio Morimoto"
],
"categories": [
"physics.flu-dyn"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Rapid Prediction of Three-Dimensional Scour Flow around Bridge Piers via Body-Fitted Coordinate-Based U-Net",
"url": "https://arxiv.org/abs/2601.06506",
"version": "v1"
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