Predicting wake meandering in real-time through instantaneous measurements of wind turbine load fluctuations
Bibliographic record
Abstract
The present study deals with the real-time estimation of the wind turbine wake meandering and the determination of the best indicators to measure in order to build a real-time wake meandering model. Results are obtained through experiments performed in an atmospheric boundary layer wind tunnel, where a specific set-up enables to measure simultaneously the incoming lateral velocity fluctuations, the lateral force fluctuations applied to the wind turbine model and to track in real-time the lateral position of its wake. It is an extension of a previous work about the determination of good candidates to build some real-time predictors of the wake meandering. The strong correlations between the incoming transverse velocity fluctuations, the lateral force fluctuations and the lateral position of the wake farther downstream are quantified, confirming that the large-scale turbulent eddies impact directly the wake meandering. Three different versions of a wake meandering predictor model are compared. It leads to the conclusion that the monitoring of the global force fluctuations applied to a wind turbine could be used to predict in real-time the meandering of the generated wake.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".