A critical insight into the most effective CFD settings to model atmospheric stability in wind energy applications
Bibliographic record
Abstract
Abstract An accurate reconstruction of the Atmospheric Boundary Layer (ABL) is key for the estimation of energy production and loads in modern wind turbines since, at current rotor heights, the vertical structure of the ABL is heavily influenced by thermal stratification effects. The wind power community usually accounts for these effects by semi-empirical modifications to the neutral ABL profile obtained using linear solvers such as WAsP; this approach, however, may not be suitable for sites with complex terrain, time-dependent thermal effects, or dense canopies. In these applications, methods with higher fidelity, such as Computational Fluid Dynamics (CFD) approaches based on steady or unsteady Reynolds Averaged Navier-Stokes (RANS) equations are needed. While CFD is recognized as providing more accurate results for these realistic cases, its use is still not a common best practice. In the current study, the WindModeller CFD tool by Ansys was employed to assess the effect of various inlet boundary conditions on the predicted wind speed profiles in unstable and stable atmospheric conditions. The test case is a wind farm in North Dakota, USA, characterized by a simple terrain but strong atmospheric stability effects and temperature fluctuations. The comparison of CFD results with experimental measurements and WAsP-CFD simulations reveals a high level of agreement between CFD and experimental data in stable conditions. In the case of unstable conditions, despite the good representation of the wind field, a high sensitivity to inlet boundary conditions is highlighted.
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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 | 0.000 | 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) | 0.000 | 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".