Towards a machine-learning-based large eddy simulation of offshore wind farms
This study introduces a Scale-Adaptive Machine-Learning Subgrid-Scale model developed to predict subgrid-scale turbulence within the framework of large eddy simulations for offshore wind farms. Unlike traditional subgrid-scale models that rely on blending of isotropy and scale similarity, the propos...
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| Vydáno v: | Computers & fluids Ročník 302; s. 106823 |
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| Jazyk: | angličtina |
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Elsevier Ltd
15.11.2025
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| ISSN: | 0045-7930 |
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| Abstract | This study introduces a Scale-Adaptive Machine-Learning Subgrid-Scale model developed to predict subgrid-scale turbulence within the framework of large eddy simulations for offshore wind farms. Unlike traditional subgrid-scale models that rely on blending of isotropy and scale similarity, the proposed approach leverages a supervised learning framework based on physically informed flow observables derived from mixed modelling theory and Leonard decomposition. The model employs a novel encoder–decoder neural network architecture designed to capture coherent enstrophy dynamics and multi-scale turbulence interactions. Skip connections and latent representations serve as implicit filters, enabling the model to represent both structural and functional aspects of turbulence. Trained using data from a scale-adaptive LES method, outcome of the presented model has been validated for its ability to learn and reproduce key turbulence characteristics, such as intermittency and energy transfer, across resolutions and flow scenarios. A-priori tests confirm its capacity to capture statistical turbulence features, while a-posteriori tests demonstrate that the model dynamically predicts eddy viscosity and produces flow fields comparable to high-resolution LES with traditional SGS models. When applied on coarser meshes, the model maintains accuracy, as evidenced by agreement in the ratio of subgrid to total kinetic energy. These findings support the potential of this machine-learning-based model as a physics-aware, scalable modelling approach for complex turbulent flows.
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•ML–LES integration using scale-adaptive and mixed modelling: Introduces SAM-SGS, a model that learns enstrophy dynamics and energy cascade.•Encoder–decoder architecture improves LES performance: Uses skip connections to boost interpretability, gradient flow, and spatial detail.•Scalable and generalizable AI for offshore LES: SAM-SGS adapts to flow variations, enabling robust LES in wind farm applications. |
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| AbstractList | This study introduces a Scale-Adaptive Machine-Learning Subgrid-Scale model developed to predict subgrid-scale turbulence within the framework of large eddy simulations for offshore wind farms. Unlike traditional subgrid-scale models that rely on blending of isotropy and scale similarity, the proposed approach leverages a supervised learning framework based on physically informed flow observables derived from mixed modelling theory and Leonard decomposition. The model employs a novel encoder–decoder neural network architecture designed to capture coherent enstrophy dynamics and multi-scale turbulence interactions. Skip connections and latent representations serve as implicit filters, enabling the model to represent both structural and functional aspects of turbulence. Trained using data from a scale-adaptive LES method, outcome of the presented model has been validated for its ability to learn and reproduce key turbulence characteristics, such as intermittency and energy transfer, across resolutions and flow scenarios. A-priori tests confirm its capacity to capture statistical turbulence features, while a-posteriori tests demonstrate that the model dynamically predicts eddy viscosity and produces flow fields comparable to high-resolution LES with traditional SGS models. When applied on coarser meshes, the model maintains accuracy, as evidenced by agreement in the ratio of subgrid to total kinetic energy. These findings support the potential of this machine-learning-based model as a physics-aware, scalable modelling approach for complex turbulent flows.
[Display omitted]
•ML–LES integration using scale-adaptive and mixed modelling: Introduces SAM-SGS, a model that learns enstrophy dynamics and energy cascade.•Encoder–decoder architecture improves LES performance: Uses skip connections to boost interpretability, gradient flow, and spatial detail.•Scalable and generalizable AI for offshore LES: SAM-SGS adapts to flow variations, enabling robust LES in wind farm applications. |
| ArticleNumber | 106823 |
| Author | Alam, Jahrul Pope, Kevin Marefat, H. Ali |
| Author_xml | – sequence: 1 givenname: H. Ali orcidid: 0000-0001-9274-7854 surname: Marefat fullname: Marefat, H. Ali email: hmarefat@mun.ca organization: Scientific Computing Program, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada – sequence: 2 givenname: Jahrul orcidid: 0000-0002-3075-591X surname: Alam fullname: Alam, Jahrul organization: Dept. of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada – sequence: 3 givenname: Kevin surname: Pope fullname: Pope, Kevin organization: Dept. of Mechanical and Mechatronics Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada |
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| Keywords | LES Encoder–decoder models Subgrid-scale model Machine learning Offshore wind farms |
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| SubjectTerms | Encoder–decoder models LES Machine learning Offshore wind farms Subgrid-scale model |
| Title | Towards a machine-learning-based large eddy simulation of offshore wind farms |
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