Effect of Roof Slope on a Building-Mounted Wind Turbine
Bibliographic record
Abstract
Knowledge of the wind climate above peaked roofs is necessary to determine whether instal ling small wind turbines on low -rise peaked roof buildings is feasible. There is little published data available documenting how wind speeds above peaked roofs vary relative to a reference open field condition. The wind characteristics at a representative peaked roof barn in southern Ontario, Canada were investigated to help address this need. The barn was simulated using a boundary layer wind tunnel, and the commercial code Fluent. Field measurements at the barn were collected using sonic anemometers and c ompared to the simulation results. Wind speed amplification was confined to a region immediately above the roof and was relatively low for wind energy purposes. It was found that with Fluent, renormalization group (RNG) k -epsilon turbulence closure predict ed winds above the roof peak better than standard k -epsilon. Simulation of buildings with a range of roof slopes found that moderately sloped roofs appear to offer a better combination of wind speed amplification and low turbulence levels at the roof peak, compared to either flat or very steep roofs. Considering only wind -related factors, the placing of very small micro -wind turbines on roof peaks may be warranted. However, if sufficient space is available, placing small turbines on a tower, rather than on the peaked roof of a low -rise building, will usually be the best approach.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".