Machine-learning wall model of large-eddy simulation for low- and high-speed flows over rough surfaces
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arXiv
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| Format: | Preprint |
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2026
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| author | Ma, Rong Lozano-Duran, Adrian |
| author_facet | Ma, Rong Lozano-Duran, Adrian |
| contents | We present a wall model for large-eddy simulation that incorporates surface-roughness effects and is applicable across low- and high-speed flows, for both transitional and fully rough conditions. The model, implemented using an artificial neural network, is trained on a direct numerical simulation database of compressible turbulent channel flows over rough walls. The dataset contains 372 cases spanning a wide range of irregular roughness topographies, including Gaussian and Weibull distributions, Mach numbers 0~3.3, and friction Reynolds numbers 180~2000. We employ an information-theoretic, dimensionless learning method to identify the inputs with the highest predictive power for the dimensionless wall friction and wall heat flux. Predictions are accompanied by a confidence score derived from a spectrally normalized neural Gaussian process, which quantifies uncertainty in regions that deviate from the training dataset. The model performance is first evaluated a-priori on 110 turbulent channel flow cases, yielding prediction errors below 4%. The model is assessed a-posteriori in wall-modeled large-eddy simulations across diverse test cases. These include over 160 subsonic and supersonic turbulent channel flows with rough walls, a transonic high-pressure turbine (HPT) blade with Gaussian roughness, a high-speed compression ramp with sandpaper roughness, and three hypersonic blunt bodies with sand-grain roughness. Results show that the proposed wall model typically achieves a-posteriori predictive accuracy within 10% for wall shear stress and within 15% for wall heat flux, with high confidence in the channel flows and HPT blade cases. In the rough-wall compression ramp and hypersonic blunt bodies, the model captures the heating augmentation with errors ranging 0%~20%. In the cases with the highest errors, the reduced performance is correctly detected by a drop in the confidence score. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20786 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine-learning wall model of large-eddy simulation for low- and high-speed flows over rough surfaces Ma, Rong Lozano-Duran, Adrian Fluid Dynamics We present a wall model for large-eddy simulation that incorporates surface-roughness effects and is applicable across low- and high-speed flows, for both transitional and fully rough conditions. The model, implemented using an artificial neural network, is trained on a direct numerical simulation database of compressible turbulent channel flows over rough walls. The dataset contains 372 cases spanning a wide range of irregular roughness topographies, including Gaussian and Weibull distributions, Mach numbers 0~3.3, and friction Reynolds numbers 180~2000. We employ an information-theoretic, dimensionless learning method to identify the inputs with the highest predictive power for the dimensionless wall friction and wall heat flux. Predictions are accompanied by a confidence score derived from a spectrally normalized neural Gaussian process, which quantifies uncertainty in regions that deviate from the training dataset. The model performance is first evaluated a-priori on 110 turbulent channel flow cases, yielding prediction errors below 4%. The model is assessed a-posteriori in wall-modeled large-eddy simulations across diverse test cases. These include over 160 subsonic and supersonic turbulent channel flows with rough walls, a transonic high-pressure turbine (HPT) blade with Gaussian roughness, a high-speed compression ramp with sandpaper roughness, and three hypersonic blunt bodies with sand-grain roughness. Results show that the proposed wall model typically achieves a-posteriori predictive accuracy within 10% for wall shear stress and within 15% for wall heat flux, with high confidence in the channel flows and HPT blade cases. In the rough-wall compression ramp and hypersonic blunt bodies, the model captures the heating augmentation with errors ranging 0%~20%. In the cases with the highest errors, the reduced performance is correctly detected by a drop in the confidence score. |
| title | Machine-learning wall model of large-eddy simulation for low- and high-speed flows over rough surfaces |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2601.20786 |