A NUMERICAL STUDY ON THE FLOW DIVERGENCE AROUND A HIGH SOLIDITY VERTICAL AXIS WIND TURBINE
Bibliographic record
Abstract
This thesis reports on a numerical investigation into the three-dimensional flow divergence around a high solidity vertical axis wind turbine. Three-dimensional unsteady Reynolds averaged Navier-Stokes simulations of an H-type vertical axis wind turbine were used to examine the impact of turbine aspect ratio and tip speed ratio on the flow divergence. The turbine height was changed to alter the turbine aspect ratio, while keeping the diameter constant, to ensure that the solidity and tip speed ratio values were comparable between the different aspect ratios tested. The power output of the turbine consistently increased with aspect ratio and the optimal tip speed ratio for peak performance was negligibly affected. The flow divergence results showed that larger aspect ratio turbines had significantly more flow divergence with a 1 m/s entrance velocity difference between the smallest and largest cases. These two results where contradictory as a larger aspect ratio turbine was more efficient even though it had a smaller fraction of the upstream flow entering the upwind pass. The reason for this result was that impact of the tip effects, which caused a power reduction near the end of the blades. The distance from the blade tips that experienced a power reduction was constant for turbines of aspect ratio one and greater, resulting in a smaller turbine having a greater fraction of its height effected by the tips. This caused the overall power output for a smaller aspect ratio turbine to be lower even though its centre performance was higher, due to an increased entrance velocity. The change in flow divergence with tip speed ratio was also examined to better understand the driving force behind the divergence. It was found that the turbine power output was not the direct cause of flow divergence. The blade forces, specifically the force generated in the upstream direction had a strong linear correlation with the upstream flow divergence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".