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Record W2125984309 · doi:10.1177/1744259112444021

Numerical modeling and experimental investigations of thermal performance of reflective insulations

2012· article· en· W2125984309 on OpenAlexaff
Hamed H. Saber, Wahid Maref, Gordon Sherrer, M. C. Swinton

Bibliographic record

VenueJournal of Building Physics · 2012
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAtticStack (abstract data type)Heat transferThermalMaterials scienceThermal insulationRoofSample (material)MetreConvectionNuclear engineeringMechanical engineeringMechanicsEngineeringComposite materialStructural engineeringComputer scienceThermodynamicsLayer (electronics)

Abstract

fetched live from OpenAlex

Reflective insulations are being used in attics, flat roof, and wall systems. Numerical modeling and experimental investigations were conducted to assess the thermal performance of assemblies with reflective insulations. In this article, the present model was used to verify the use of the ASTM C-518 test method for measuring the effective thermal resistances (R-values) of sample stacks comprising reflective insulations. Two tests were conducted on sample stacks using heat flow meter apparatus. The sample stack consists of two expanded polystyrene layers and a reflective insulation installed in between. The model predictions agreed with the measured heat fluxes within ±1%. The article also discusses the combined effect of heat transfer by convection and radiation in the airspace facing the reflective insulation, showing that the derived R-value from the test data resulted in underestimation of the effective R-value of the sample stack.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.252
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2012
Admission routes1
Has abstractyes

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