Numerical investigation of Savonius wind turbine farms
Bibliographic record
Abstract
The Savonius vertical axis wind turbine is a simple device, easy to manufacture, has good starting characteristics, and rotates with wind from any direction; nevertheless, it has a lower efficiency than the other wind turbines. The aim of this paper is to numerically explore the non-linear unsteady flow over a conventional Savonius using three dimensional computations with emphasis on the placement of these turbines in a linear array and the effect of an obstacle that acts as a wind deflector. First, an infinite array of turbines is used to study the gap distance between the wind turbine axis rotors. This investigation is conducted via numerical simulations based on the computational fluid dynamics computer program Fluent 14.5. It is found that a gap distance L = 1.4R gives a very good performance. Second, four farms with different number of turbines—from 3 to 21 turbines—are studied. The effect on the power coefficient of the number of turbines in each farm is reported and analyzed. Third, a new arrangement that includes an obstacle at one end of the array of turbines is presented. The best configuration explored in this work increases the power coefficient of each Savonius wind turbine by 82% compared to a single turbine. Finally, the effect of the wind direction for the best configuration is presented and the range of wind angles for which the farm outperforms isolated turbines is calculated.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".