A generalisable data-augmented turbulence model with progressive and interpretable corrections for incompressible wall-bounded flows
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| Format: | Preprint |
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2025
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| _version_ | 1866913920450560000 |
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| author | Rincón, Mario J. Reclari, Martino Yang, Xiang I. A. Abkar, Mahdi |
| author_facet | Rincón, Mario J. Reclari, Martino Yang, Xiang I. A. Abkar, Mahdi |
| contents | The integration of interpretability and generalisability in data-driven turbulence modelling remains a fundamental challenge for computational fluid dynamics applications. This study yields a generalisable advancement of the $k$-$ω$ Shear Stress Transport (SST) model through a progressive data-augmented framework, combining Bayesian optimisation with physics-guided corrections to improve the predictions of anisotropy-induced secondary flows and flow separation simultaneously. Two interpretable modifications are systematically embedded: 1) a non-linear Reynolds stress anisotropy correction to enhance secondary flow predictions, and 2) an activation-based separation correction in the $ω$-equation, regulated by an optimised power-law function to locally adjust turbulent viscosity under adverse pressure gradients. The model is trained using a multi-case computational fluid dynamics-driven a posteriori approach, incorporating periodic hills, duct flow, and channel flow to balance correction efficacy with baseline consistency. Validation across multiple unseen cases -- spanning flat-plate boundary layers, high-Reynolds-number periodic hills, and flow over diverse obstacle configurations -- demonstrates enhanced accuracy in velocity profiles, recirculation zones, streamwise vorticity, and skin friction distributions while retaining the robustness of the original $k$-$ω$ SST in attached flows. Sparsity-enforced regression ensures reduced parametric complexity, preserving computational efficiency and physical transparency. Results underscore the framework's ability to generalise across geometries and Reynolds numbers without destabilising corrections, offering a validated framework toward deployable, data-augmented turbulence models for numerical simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18568 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A generalisable data-augmented turbulence model with progressive and interpretable corrections for incompressible wall-bounded flows Rincón, Mario J. Reclari, Martino Yang, Xiang I. A. Abkar, Mahdi Fluid Dynamics The integration of interpretability and generalisability in data-driven turbulence modelling remains a fundamental challenge for computational fluid dynamics applications. This study yields a generalisable advancement of the $k$-$ω$ Shear Stress Transport (SST) model through a progressive data-augmented framework, combining Bayesian optimisation with physics-guided corrections to improve the predictions of anisotropy-induced secondary flows and flow separation simultaneously. Two interpretable modifications are systematically embedded: 1) a non-linear Reynolds stress anisotropy correction to enhance secondary flow predictions, and 2) an activation-based separation correction in the $ω$-equation, regulated by an optimised power-law function to locally adjust turbulent viscosity under adverse pressure gradients. The model is trained using a multi-case computational fluid dynamics-driven a posteriori approach, incorporating periodic hills, duct flow, and channel flow to balance correction efficacy with baseline consistency. Validation across multiple unseen cases -- spanning flat-plate boundary layers, high-Reynolds-number periodic hills, and flow over diverse obstacle configurations -- demonstrates enhanced accuracy in velocity profiles, recirculation zones, streamwise vorticity, and skin friction distributions while retaining the robustness of the original $k$-$ω$ SST in attached flows. Sparsity-enforced regression ensures reduced parametric complexity, preserving computational efficiency and physical transparency. Results underscore the framework's ability to generalise across geometries and Reynolds numbers without destabilising corrections, offering a validated framework toward deployable, data-augmented turbulence models for numerical simulations. |
| title | A generalisable data-augmented turbulence model with progressive and interpretable corrections for incompressible wall-bounded flows |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2503.18568 |