Wind turbine wake effect visualization and LiDAR measurement techniques
Bibliographic record
Abstract
The expansion of wind energy development has resulted in larger wind farms and closer placement of turbines to utilize the space available. Each turbine produces a wake that affects downstream turbines, which causes issues such as production loss, blade loading, and fluctuating electrical output. To minimize this impact, the wake produced by turbines must first be understood. Experimental methods were used in the exploration of the wake effects of wind turbines. Smoke visualization inside the University of Waterloo Wind Generation Facility was used to determine the helical vortex wake distribution behind a 3.3 m diameter turbine as well as the tip vortex shedding from the blade. Wind Doppler Light Detection and Ranging (LiDAR) measurement devices were modified and used to measure wind turbine wake velocities. The University of Waterloo Wind Energy Group has a ZephIR z150 LiDAR to use in this study. The LiDAR was verified under normal operation for accuracy against a cup and vane anemometer. The LiDAR was then verified for accuracy and to determine the position of measurements after modifications necessary for future wake measurement experiments.
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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".