CFD modeling of vertical-axis wind turbine wake interaction
Bibliographic record
Abstract
Since wind turbines placed in wind farms need to minimize their footprint on the ground, the effects of the wake must be considered. Placement optimization, turbine spacing, and direction of rotation are known to affect the performance of vertical-axis wind turbines (VAWTs). However, rigorous numerical modeling methodologies that investigate the influence of these characteristics are lacking, especially in the case of large wind turbines. The goal of this study is to analyze turbine configurations that might enhance the power production of VAWT farms using two-dimensional CFD models based on the Star CCM+ package. The novelty of this work is to analyze wind farm configurations for very large turbines. This is important because large turbines are much more performant than small turbines and have a high power coefficient. Results show that CFD simulations adequately capture the performance of wind turbines in farms with multiple VAWTs. In general, if a second rotor is spaced more than 10 turbine diameters downstream of the first rotor, the effect of the wake is less significant. Furthermore, a specific farm configuration with five VAWTs is investigated and shows a 20% increase in power output compared with the same number of turbines operating in isolation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".