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Record W4226162414 · doi:10.3390/buildings12040425

Effect of Temperature on Long-Term Thermal Conductivity of Closed-Cell Insulation Materials

2022· article· en· W4226162414 on OpenAlexaffabout
Sudhakar Molleti, David van Reenen

Bibliographic record

VenueBuildings · 2022
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceThermal conductivityComposite materialPolystyreneThermal insulationConductivityAtmospheric temperature rangeService lifePolymerThermodynamicsChemistryLayer (electronics)

Abstract

fetched live from OpenAlex

This study examines the isolated impact of temperature on the accelerated aging of closed-cell foam insulation materials. Laboratory aging of closed-cell foams was conducted at three temperatures: −10, 23 and 50 °C. This was to cover the range of the material’s in-service range within a Canadian climate. Three polyisocyanurate foam and one extruded polystyrene foam insulations were considered in the study. The three polyisocyanurate foam products tested showed significant variability in polynomial functions at lower mean temperatures when newly manufactured; however, these differences were found to diminish over time. Thermal conductivity decreased between 0% and 17% depending on mean temperature following equivalent 5-year aging at 23 °C. Aging polyisocyanurate specimens at −10 °C was found to decrease rate of change in thermal conductivity by approximately 5% compared to aging at 23 °C when measured at a mean temperature of 24 °C, while aging at 50 °C had a smaller impact. However, at mean temperatures below the beginning of condensation point, there was greater variability in the impact of aging temperature. For the extruded polystyrene, thermal conductivity decreased between 8% and 10% following equivalent 5-year aging at 23 °C and displayed a linear relationship with a 23% difference in thermal conductivity between −10 °C and 40 °C.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.420

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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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 designBench or experimental
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

Citations11
Published2022
Admission routes2
Has abstractyes

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