numerical study of vertical axis wind turbine performance in turbulent flows and behind turbine wakes
Bibliographic record
Abstract
Wind energy is a clean, renewable, and cost-effective alternative to conventional power sources. It does not require fuel, does not emit pollution, and can be installed near the places where electricity is needed, reducing transmission losses. Vertical Axis Wind Turbines (VAWTs) are a type of wind energy technology that have some advantages over Horizontal Axis Wind Turbines (HAWTs), such as being able to able to harvest wind from all directions, generate less noise, and offering a simpler structure. However, VAWTs also have lower efficiency than HAWTs, and their performance is affected by the wake, which is a lower-velocity turbulent flow behind the rotating blades. \nThe wake can interfere with the operation of other wind turbines downstream, but it can also enhance the performance of smaller VAWTs. The aim of this research is to use Computational Fluid Dynamics (CFD) to study the effect of wake on different sizes of VAWTs, and to understand how the turbulence generated by the wake influences the power output of these turbines. This thesis presents a CFD methodology and identifies the strengths and weaknesses of CFD for the simulation of the interaction of the wake with downstream VAWTs. The contributions are threefold. First, the understanding of how turbulence intensity affects the VAWT’s performance. Second, calculating the performance of a wind turbine that is in the wake of another turbine and the study of some particular VAWTs placement configurations. Third, quantifying the limitations of CFD and identifying when it is appropriate to use two-dimensional models for flow simulations of multiple VAWTs. This research has documented an increased performance of about 20% for small turbines in high turbulence intensity flows. Furthermore, a 20% increase in power output from an optimization array of VAWTs was identified. Finally, this work suggests that two-dimensional CFD simulations are adequate for simulation pairs of upstream and downstream turbines if the turbines are low-solidity and low aspect ratio turbine types.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".