Aerodynamic performance improvement of 3-PB VAWT using blades with optimized tilted angles
Bibliographic record
Abstract
In the present work, a new configuration of the three-part blade (3-PB) Vertical Axis Wind Turbine (VAWT) is introduced. This new configuration is designed to further improve the aerodynamic performance of the 3-PB VAWT by tilting all three parts of every single blade along its central chord line. An optimization process is conducted to find the best tilt angle of blade parts in order to maximize the average total torque coefficient. The optimization process is applied to reference 3-PB VAWT with the help of a Genetic Algorithm (GA) and Artificial Neural Network (ANN) using the solutions of three-dimensional Reynolds averaged Navier-Stokes (RANS) equations at wind speed of 7 m/s and tip speed ratios from 0.44 to 1.77. Having analyzed different sets of tilt angles, a configuration with tilt angles of 30°, 31° , and 30° with respect to part 1, 2, and 3 was detected to be the best choice. The tilted 3-PB VAWT shows promising improvements in most tip speed ratios. Among them, a maximum improvement of 42.99% on the average of the total torque coefficient occurred at tip speed ratio of 0.89. • 3D numerical simulations are conducted at TSRs from 0.44 to 1.77 • An optimization algorithm is implemented to determine the best configuration. • Optimized configuration produces average torque higher than the untilted turbine. • Tilt angle increases the performance of 3-PB VAWT mostly in the downwind region. • Tilted blades produce higher average torque at lower TSRs.
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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 | 0.000 | 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".