Towards a machine-learning-based large eddy simulation of offshore wind farms
Bibliographic record
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. • 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".