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Heat and Moisture Modelling of Vacuum Insulated Retrofits with Experimental Validation

2021· article· en· W4200017632 on OpenAlexaff
Brock Conley, Cynthia A. Cruickshank, Christopher Baldwin

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsCarleton University
Fundersnot available
KeywordsVacuum insulated panelMaterials scienceComposite materialThermal conductivityThermal insulationRelative humidityMoistureDynamic insulationHumidityThermalComposite numberThermal resistanceStructural engineeringEngineeringLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Vacuum insulation panels (VIPs) offer 8-10 times the thermal resistance of fiberglass insulation and would fit the need for a low conductivity exterior insulation. A composite insulation panel using VIPs encased in rigid foam was developed, built, and tested. Two different sizes of VIPs were used for that stage of the project, and after monitoring and evaluation, they showed contrasting results. A simulation study was performed to find the optimal VIP solution that maximized the effective thermal conductivity and minimized the mould growth potential. In total, 5 wall assemblies with VIPs used as the exterior insulation were simulated using WUFI and WUFI2D. The simulations showed that the humidity levels at the inside face of the OSB inboard of the VIPs decreased when 200 mm by 300 mm VIPs were used, but they did not reach the thermal performance thresholds of R5.28 m 2 K/W. The hygrothermal analysis showed that under similar conditions, a VIP insulated wall assembly would have a lower relative humidity at the sheathing surface compared to EPS and XPS. The one-and two-dimensional simulations were compared and found that WUFI Pro was capable to evaluate a VIP-insulated wall assembly.

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 score0.110
Threshold uncertainty score0.345

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 head0.028
GPT teacher head0.214
Teacher spread0.185 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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