Development of the Dual Vertical Axis Wind Turbine Using Computational Fluid Dynamics
Bibliographic record
Abstract
Small vertical axis wind turbines (VAWTs) are good candidates to extract energy from wind in urban areas because they are easy to install, service, and do not generate much noise; however, the efficiency of small turbines is low. Here-in a new turbine, with high efficiency, is proposed. The novel design is based on the classical H-Darrieus VAWT. VAWTs produce the highest power when the blade chord is perpendicular to the incoming wind direction. The basic idea behind the proposed turbine is to extend that said region of maximum power by having the blades continue straight instead of following a circular path. This motion can be performed if the blades turn along two axes; hence, it was named dual vertical axis wind turbine (D-VAWT). The analysis of this new turbine is done through the use of computational fluid dynamics (CFD) with two-dimensional (2D) and three-dimensional (3D) simulations. While 2D is used to validate the methodology, 3D is used to get an accurate estimate of the turbine performance. The analysis of a single blade is performed and the turbine shows that a power coefficient of 0.4 can be achieved, reaching performance levels high enough to compete with the most efficient VAWTs. The D-VAWT is still far from full optimization, but the analysis presented here shows the hidden potential and serves as proof of concept.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".