Power enhancement CFD based study of Darius wind turbine via roof corner placement
Bibliographic record
Abstract
In this paper, the concept of positioning micro-scale wind turbines on the roof of buildings is being studied for a Darrieus wind turbine, located above the roof of a cubic building at two different positions and operating under different wind flow conditions. The turbine has a height of 2 m and is positioned at 1 or 2 m from the top of the roof of a 30.5 m cubic building. The simulation methodology based on 3D unsteady computational fluid dynamics is first presented, including mesh details and experimental validation of the unperturbed flow baseline configuration. The simulation of different configurations shows that the turbine's Coefficient of Power can reach 0.55 by positioning it above the side corners of the building when the wind reaches the building at 45°. This position indicates that the synergy between the building and the turbine is quite strong such that the turbine should be placed not on top of the frontal corner but on top of one of the side corners. The power produced by this turbine at this location is 464 W. This placement leads to a significant increase in comparison with the maximum coefficient of power (Cp) of 0.32 (equivalent to a power of 378 W) when the turbine does not interact with a building. This increase in performance is very impressive. An increase of 25% in the power extracted can lead to better integration of wind turbines on roofs.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".