Implementing Large-Eddy Simulation Capability in a Compressible Mesoscale Model
Bibliographic record
Abstract
Abstract The large size of modern wind turbines and wind farms triggers processes above the surface layer, which extend to the junction between microscales and mesoscales, and pushes the limits of existing approaches to predict the wind. The main objectives of this study are thus to introduce and evaluate an approach that will better account for physical processes within the atmospheric boundary layer (ABL), and allow for both microscale and mesoscale modeling. The proposed method, in which mathematical model and main numerical aspects are presented, combines a mesoscale approach with a large-eddy simulation (LES) model based on the Compressible Community Mesoscale Model (MC2). It is evaluated relying on a shear-driven ABL case allowing the authors to assess the model behavior at very high resolution as well as more specific numerical aspects such as the vertical discretization and time and space splitting of turbulence-related terms. The proposed LES-capable mesoscale model is shown to perform on par with other similar reference LES models, while being slightly more dissipative. A new vertical discretization of the turbulent processes eliminates a spurious numerical mode in the solution. Finally, the splitting of horizontal and vertical turbulence-related terms is shown to have no impact on the results of the test cases. It is thus demonstrated that the revised MC2 is suitable at both microscales and mesoscales, thus setting a strong foundation for future work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".