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Record W2321119082 · doi:10.1061/9780784412626.039

Wind Turbulence and Load Sharing Effects on Ballasted Roof-Top Solar Arrays

2012· article· en· W2321119082 on OpenAlexaff
Matthew T.L. Browne, Scott Gamble, Mike Gibbons

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsRowan Williams Davies & Irwin (Canada)
Fundersnot available
KeywordsRoofBallastWind engineeringStructural engineeringAerodynamicsEnclosureStiffnessMarine engineeringWind tunnelEngineeringLift (data mining)Environmental scienceAerospace engineeringComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

Solar arrays installed on roofs of low-rise commercial buildings are especially popular since it makes good use of previously unexploited real estate. These systems are usually restrained to resist wind-induced lift-off and sliding using ballast, penetrations, adhesives, or combinations thereof. In the case of ballasted systems, reducing the added weight on the roof is often the primary objective of designers, while maintaining a certain level of reliability. This can be accomplished through aerodynamic design and other methods, but sometimes more simply through the utilization of load sharing between adjacent panels. This is accomplished through the vertical bending stiffness of the racking system. This paper presents a discussion of the varied wind tunnel testing and analysis methods currently employed around the world, with a focus on the correlation effects associated with load sharing. A finite element model, in conjunction with wind tunnel data on a roof-top array, is used to demonstrate the impact of array stiffness on wind loads in a time- and spatially-varying sense. Practical guidance to determine ballast requirements is also provided.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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