Effects of an escarpment on flow parameters of relevance to wind turbines
Bibliographic record
Abstract
Abstract Assessing potential costs and benefits of siting wind turbines on escarpments is challenging, particularly when the upstream fetch is offshore leading to more persistent wind speeds in power producing classes, but an increased importance of stable stratification under which terrain impacts on the flow may be magnified. In part because of a lack of observational data, critical knowledge gaps remain and there is currently little consensus regarding optimal models for flow characterization and turbine design calculations. We present a unique dataset comprising measurements of flow parameters conducted over a 10–14 m escarpment at turbine relevant heights (from 9 to 200 m) and use them to evaluate model simulations. The results indicate good agreement in terms of the wind speed decrease before the terrain feature and the increase at (and downwind of) the escarpment of ~3–5% at turbine hub‐heights. However, the horizontal extent of the region, in which the impact of the escarpment on the mean flow is evident, is larger in the models than the measurements. A region of high turbulence was indicated close to the escarpment that extended through the nominal rotor plane, but the horizontal extent of this region was narrow (<10 times the escarpment height, H) in both models and observations. Moving onshore the profile of turbulence was more strongly influenced by higher roughness of a small forest. While flow angles close to the escarpment were very complex, by a distance of 10 H, flow angles were <3° and thus well within limits indicated by design standards. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".