Modelling of small turbine tail fin dynamics
Bibliographic record
Abstract
Many small horizontal-axis turbines use a tail fin to point the blades into the wind. This 'passive' yaw response avoids the complexity and cost of 'active' yaw controls used in large turbines, but can lead to high yaw rates and high gyroscopic loads on key turbine components as well as reduced power output. This chapter reviews the basic aerodynamics of tail fins and describes the development of the yaw response equation for a new tail fin module for the well-known and freely available OpenFAST aeroelastic code for turbine design, analysis and certification. Wind tunnel measurements of the yaw response of tail fins without a rotor and nacelle were used to determine the model constants, starting with generic shapes such as a delta wing whose aerodynamic performance is well known. Some results from a detailed wind tunnel investigation of generic shapes with a range of aspect ratios and a model of a complex tail fin from a commercial turbine are presented and analysed. In addition, we describe tests of a model turbine with the rotor starting as it yaws into the wind at low wind speed. This aspect of tail fin operation is characterised by low response frequency and often large yaw angles. Since the tail fin is essential for starting the turbine, the new tail fin module incorporates the important nonlinearities in yaw response, caused by large angles, aspect ratios and significant yaw bearing friction. New experiments on very high yaw angles and varying tail boom lengths are presented and analysed. We finish by discussing the methodology of tail fin 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".