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Temperature-Dependent Thermal Performance of Closed-Cell Foam Insulation Board in Roof Constructions

2020· article· en· W3026628173 on OpenAlexaffabout
Gurpreet Singh Jagdev, Phalguni Mukhopadhyaya

Bibliographic record

VenueJournal of Cold Regions Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRoofThermal insulationThermal resistanceMaterials scienceThermalComposite materialVacuum insulated panelThermal bridgeBuilding insulationDynamic insulationThermal conductivityMoistureStructural engineeringEngineeringMeteorology

Abstract

fetched live from OpenAlex

Thermal performance of an insulation material is influenced by the in-service temperature condition. Unlike most other insulation materials, thermal resistance (R-value) of polyisocyanurate (polyiso or PIR) foam insulation with “captive blowing agent” varies nonlinearly with temperature. Building designers consider the constant R-value of different insulating materials for building design and energy calculations, and hygrothermal simulation software tools, such as WUFI, consider the linear temperature-dependent R-value, even for polyiso. However, neither the linear temperature-dependent thermal resistance nor the constant thermal resistance value of polyiso represents the actual thermal performance. This paper aims to quantify the impact of in-service boundary temperature conditions in Canadian climates on the thermal performance of polyiso foam insulation board used in EPDM and PVC roof constructions. Hygrothermal simulations were performed using WUFI Pro, which considers real climate data and hygrothermal properties of constituent roof components for evaluating moisture and temperature conditions in roof constructions. Based on heating degree days (HDD), ten different cities were selected between climate Zone 4 (HDD < 3,000) and Zone 8 (HDD ≥ 7,000). The thermal resistance measurements were conducted using heat flow meter apparatus on four polyiso insulation boards (two new and two aged) of different sizes [thickness, new: 1 in. (25 mm) and 2 in. (51 mm); aged: 2 in. (51 mm) and 3 in. (76 mm)] at five mean temperatures of −4°C (25°F), 4.5°C (40°F), 10°C (50°F), 24°C (75°F), 43°C (110°F), and at a temperature differential of 28°C (50°F). The measured thermal resistance data of the four samples at different mean temperatures were normalized with calculated thermal resistance of each sample at 22°C (72°F). The normalized R-value variation was calculated using in-service boundary temperature conditions determined from hygrothermal simulations and considering linearly varied thermal resistance with temperature, for the selected ten Canadian cities. The normalized R-value data of four polyiso samples revealed 17.4% decrease in R-value under winter conditions of Zone 8.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.009
GPT teacher head0.174
Teacher spread0.165 · 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 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".

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Citations5
Published2020
Admission routes2
Has abstractyes

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