Tailoring incoming shear and turbulence profiles for lab‐scale wind turbines
Bibliographic record
Abstract
Abstract An active grid is used to generate a variety of turbulent shear profiles in a wind tunnel. The vertical bars are set to flap through varying angles across the test section producing a variation in the perceived solidity, resulting in a mean shear. The horizontal bars are used in a fully random operational mode to set the background turbulence level. It is demonstrated that mean velocity profiles with approximately the same shear can be produced with different turbulence intensities and local turbulent Reynolds numbers based on the Taylor microscale, λ. Conversely, it is also demonstrated that flows can be produced with similar turbulence intensity profiles but different mean shear. It is confirmed that the length scales and dynamics, the latter being assessed through the velocity spectra and probability density functions, do not vary significantly across the investigation domain. Such flows are of particular relevance for studies investigating the effect of in‐flow conditions on obstacles where these studies wish to decouple the effects of turbulence intensity and mean shear, a feat previously unattainable in experimental facilities. Given that the power output of wind turbines is known to be a function of both mean shear and turbulence intensity, the experimental methodology presented herein is invaluable to the wind turbine model testing community who, at present, cannot exert such control authority over the in‐flow conditions.
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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) | 0.000 | 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 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".