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Record W2090145025 · doi:10.1002/er.1162

A simplified method for modelling the effect of blinds on window thermal performance

2005· article· en· W2090145025 on OpenAlexafffund
David Naylor, Hidayat Ullah Shahid, S. J. Harrison, Patrick H. Oosthuizen

Bibliographic record

VenueInternational Journal of Energy Research · 2005
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Ontario Institute of TechnologyQueen's UniversityToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLouverWindow (computing)FenestrationThermal comfortHeat exchangerThermalHeat transferGlazingComputer scienceSimulationAcousticsEnvironmental scienceMechanicsMaterials scienceMeteorologyMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

An approximate method is presented for predicting the effect of a louvered blind on the centre-glass thermal performance of a fenestration. The method combines a one-dimensional heat transfer model with data from a numerical simulation of the window and blind. Sample results for a blind mounted on the indoor surface of a window show the effect of blind slat angle on heat transmission. Both summer and winter conditions are considered. The results show that a louvered blind can improve the U-value of a standard double-glazed window by up to 37%. Also, the radiation heat exchange with the room can be dramatically reduced (by up to 60%), which will improve the level of occupant comfort. However, there was found to be a trade-off between U-value and occupant comfort; placing the blind closer to the window improves the U-value, but increases the radiation heat exchange with the room. The predictions from the present simplified method compare well with results from a full two-dimensional computational fluid dynamics solution of the conjugate blind/window interaction. Copyright © 2005 John Wiley & Sons, Ltd.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.504
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.037
GPT teacher head0.354
Teacher spread0.316 · 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 designSimulation or modeling
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

Citations15
Published2005
Admission routes2
Has abstractyes

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