Applying Hollow Blades for Small Wind Turbines Operating at High Altitudes
Bibliographic record
Abstract
Since the air density reduces as the altitude increases, operation of Small Wind Turbines (SWTs) which usually have no pitch mechanism, remains as a challengeable task at high altitudes due largely to the reduction of starting aerodynamic torque. By reducing the blades moment of inertia through the use of hollow blades, the study aims to mitigate that issue and speed up the starting. A three-bladed, 2 m diameter small horizontal axis wind turbine with hollow cross-section was designed for operating at two sites with altitude of 500 and 3,000 m. The design variables consist of distribution of the chord, twist and shell thickness along the blade. The blade-element momentum theory was employed to calculate the output power and starting time and, the beam theory was used for the structural analysis to investigate whether the hollow blades could withstand the aerodynamic and centrifugal forces. A combination of the starting time and the output power was included in an objective function and then, the genetic algorithm was used to find a blade for which the output power and the starting performance, the goals of the objective function, are high while the stress limitation, the objective function constraint, is also met. While the resultant stresses remain below the allowable stress, results show that the performance of the hollow blades is far better than the solid ones such that their starting time is shorter than the solid blades by approximately 70%. However, in the presence of the generator resistive torque, the algorithm could not find the blade for the altitude near to 3000 m. To solve that problem, the tip speed ratio of the turbine was added to other design variables and another optimization process was done which led to the optimal blades not only for the lower altitude but also for the higher one.
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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) | 0.000 | 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) | 0.001 | 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".