Design and Characterization of a Trailer-Based Horizontal-Axis Wind Turbine Test Rig
Bibliographic record
Abstract
This paper presents a mobile testing rig developed for small wind turbine (SWT) experimental work to orchestrate, cost-effectively, turbine performance characterization in both controlled wind inflow speeds and turbulent ambient flows. It facilitates off-grid testing of up to a 1 kW wind turbine. It is a dual-purpose machine that can be towed behind a vehicle to conduct steady-state tests (track testing) or be parked to collect unsteady field data (field testing), all with the same rotor and instrumentation. Its mechanical design included computational fluid dynamics (CFD) analysis to gauge the potential impact of towing vehicle disturbance on the free stream available to the rotor. To provide a compelling platform for full rotor speed control, a reconfigurable control system coupled to an electric vehicle controller with regenerative braking technology has been modeled and implemented into its electrical design. Uncertainty analysis has also been rigorously conducted to project the error bounds pertaining to both precision and bias components of the testing results. The rig has been tested in a towed scenario and blade element momentum (BEM) simulations have been compared with the actual aggregate performance curves obtained experimentally. Future work involves testing in unsteady winds, for which the rig was ultimately designed in order to better understand unsteady rotor performance and adaptive design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".