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Record W2604235747 · doi:10.1061/9780784480502.022

Numerical Simulation of Forced Convective Heat Transfer Coefficients on the Facade of Low-and High-Rise Buildings

2017· article· en· W2604235747 on OpenAlexafffund
Meseret T. Kahsay, Girma Bitsuamlak, Fitsum Tariku

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsBritish Columbia Institute of TechnologyWestern University
FundersCanada Research Chairs
KeywordsGlazingFacadeHeat transfer coefficientComputational fluid dynamicsConvective heat transferHeat transferMechanicsReynolds-averaged Navier–Stokes equationsCeiling (cloud)Forced convectionMaterials scienceEnvironmental scienceStructural engineeringEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

Many commercial and institutional buildings use glazed curtain walls from floor to ceiling, however, glazing has very little ability to control heat flow. Previous studies have shown that the thermal resistance of glazing is usually less than other components of a building’s envelope. In order to evaluate building energy consumption accurately knowledge of the convective heat transfer coefficient (CHTC) distribution over the surface of the building is important. In this paper, high-resolution 3D steady reynolds-averaged navier-stokes (RANS) computational fluid dynamics (CFD) simulations of forced convective heat transfer at the windward facade of five buildings with various building heights having 3, 10, 15, 20 and 30 storeys are presented. The influence of building height on CHTC distribution is investigated at different Reynolds numbers ranging from 0.7x106 to 33x106. It was observed that as H increases from 10m to 101m, the surface average-CHTC on the windward façade increases by about 55%. Moreover, a correlation has been developed as function of building height.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.235

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)0.0000.000

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 head0.013
GPT teacher head0.245
Teacher spread0.232 · 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

Citations3
Published2017
Admission routes2
Has abstractyes

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