Hollow Blades for Small Wind Turbines Operating at High Altitudes
Bibliographic record
Abstract
Since the air density reduces as altitude increases, operation of small wind turbines (SWTs), which usually have no pitch adjustment, remains challenging at high altitudes due largely to the reduction of starting aerodynamic torque. By reducing the moment of inertia through the use of hollow blades, this study aims to speed up the starting while maintaining the structural integrity of the blades and high output power. A horizontal axis turbine with hollow blades was designed for two sites in Iran with altitude of 500 m and 3000 m. The design variables are the distributions of the chord, twist, and shell thickness and the improvement of output power and starting are the design goals. Blade-element momentum (BEM) theory was employed to calculate these goals and beam theory was used for the structural analysis to investigate whether the hollow timber blades could withstand the aerodynamic and centrifugal forces. A combination of the goals formed the objective function and a genetic algorithm (GA) was used to find a blade whose output power at a predetermined tip speed ratio (TSR) and the starting performance were high while the stress limit was met. The results show that hollow blades have starting times shorter than solid ones by approximately 70%. However, in the presence of generator resistive torque, the algorithm could not find a blade for an altitude of 3000 m. To solve that problem, the tip speed ratio was added to other design variables and another optimization was done which led to the optimal blades for both altitudes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".