A generalisable data-augmented turbulence model with progressive and interpretable corrections for incompressible wall-bounded flows Article Swipe

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Mario Javier Rincón , Martino Reclari , Xiang I. A. Yang ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2503.18568

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.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2503.18568
PDF: https://arxiv.org/pdf/2503.18568
OA Status: green
OpenAlex ID: https://openalex.org/W4416525409

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DOI: https://doi.org/10.48550/arxiv.2503.18568

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Title: A generalisable data-augmented turbulence model with progressive and interpretable corrections for incompressible wall-bounded flows

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-03-24

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Authors: Mario Javier Rincón, Martino Reclari, Xiang I. A. Yang

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Landing page: https://arxiv.org/abs/2503.18568

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