MétaCan
Menu
Back to cohort
Record W2026014145 · doi:10.2514/6.2008-1325

Effect of Roof Slope on a Building-Mounted Wind Turbine

2008· article· en· W2026014145 on OpenAlexafffundabout
William David Lubitz, Rohan Hakimi

Bibliographic record

Venue46th AIAA Aerospace Sciences Meeting and Exhibit · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversity of Guelph
FundersUniversity of Guelph
KeywordsRoofTurbineMarine engineeringEnvironmental scienceGeologyMeteorologyAerospace engineeringEngineeringCivil engineeringPhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.245
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2008
Admission routes3
Has abstractyes

Explore more

Same venue46th AIAA Aerospace Sciences Meeting and ExhibitSame topicWind and Air Flow StudiesFrench-language works237,207