Parametric study of H-Darrieus vertical-axis turbines using CFD simulations
Bibliographic record
Abstract
A parametric study of vertical axis turbines of the H-Darrieus type is conducted using state-of-the-art Computational Fluid Dynamics (CFD) and the k-ω Shear Stress Transport RANS model in its unsteady form. Although most parameters have previously been investigated individually, the effect of solidity, number of blades, tip speed ratio, Reynolds number, fixed blade pitch angle, and blade thickness on the aerodynamic efficiency of the turbine is evaluated using the same performance evaluation set-up in order to determine what would be the best aerodynamic configuration and operation parameter in a given application. The quantitative impact of 3D effects associated with the blade aspect ratio and the use of end-plates is also investigated. For high-Reynolds applications, optimal radius-based solidity is found to be around σ=0.2, while higher solidities show a lower maximum efficiency than what was previously published using simpler streamtube based methods. In 3D, a small blade aspect ratio (AR=7) leads to a relative efficiency drop of nearly 60% compared to the 2D prediction. Longer blades improve the 3D efficiency greatly. End-plates are found to have a positive effect on power extraction performances, as long as their size and thus their drag are limited.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".