Development of Computational and Experimental Benchmark Data for Wind Turbine Aeroelastic Algorithms
Bibliographic record
Abstract
Efficient wind turbines with high power to cost ratio require rotor blades of high quality.This requires a comprehensive ability to predict a blade's aerodynamic performance and structural response when exposed to a variety of air flow conditions.Aeroelastic algorithms that are able to achieve this efficiently and rapidly as a tool for preliminary design stages are an ongoing concern for the professional practitioners.These aeroelastic algorithms require validation data upon which their accuracy can be determined.To this end, a validation process was investigated and applied to determine the accuracy of the Preliminary Aeroelastic Analysis of Wind Turbine (PAAC-WTB) algorithm, an algorithm that was recently developed in the Advanced Dynamic Research gtoup.A preliminary wind tunnel test was performed on a 3D-printed reduced scale model of the National Renewable Energy Laboratory (NREL) S809 blade, and experimental results were obtained to compare against algorithm results.The results I would like to thank and extend my sincere appreciation for my thesis supervisors, Professors Fred F. Afagh and Robert G. Langlois for their support and patience throughout the project.Their wisdom on all matters pertaining to this project was invaluable and I was fortunate to have them as my supervisors.Thanks and appreciation as well to my colleague and friend Alex McFarlane, a very talented and hard working individual and the main component for this project.May you always traverse difficult elements with fluidity.Major thanks and appreciation as
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".