Time‐adaptive wind turbine model for an LES framework
Bibliographic record
Abstract
Abstract Most large‐eddy simulation studies related to wind energy have been carried out either by using a fixed pressure gradient to ensure that mean wind direction is perpendicular to the wind turbine rotor disk or by forcing the flow with a geostrophic wind and timely readjusting the turbines' orientation. This has not allowed for the study of wind farm characteristics with a time‐varying wind vector. In this paper, a new time‐adaptive wind turbine model for the large‐eddy simulation framework is introduced. The new algorithm enables the wind turbines to dynamically realign with the incoming wind vector and self‐adjust the yaw orientation with the incoming wind vector similar to real wind turbines. The performance of the new model is tested first with a neutrally stratified atmospheric flow forced with a time‐varying geostrophic wind vector. A posteriori, the new model is used to further explore the interaction between a synthetic time‐changing thermal atmospheric boundary layer and an embedded wind farm. Results show that there is significant potential power to be harvested during the unstable time periods at the cost of designing wind turbines capable of adapting to the enhanced variance of these periods. Stable periods provide less power but are more constant over time with an enhanced lateral shear induced by an increased change in wind direction with height. Copyright © 2015 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".