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Record W2167159301 · doi:10.1177/0040517510395994

Characterizing the performance of a single-layer fabric system through a heat and mass transfer model - Part II: Thermal and evaporative resistances

2011· article· en· W2167159301 on OpenAlexaff
Dan Ding, Tian Tang, Guowen Song, André McDonald

Bibliographic record

VenueTextile Research Journal · 2011
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceThermalAir layerThermal resistanceAir gap (plumbing)Work (physics)Heat transferComposite materialEvaporative coolerMass transferLayer (electronics)ConvectionPorosityMechanicsNatural convectionMechanical engineeringThermodynamicsEngineering

Abstract

fetched live from OpenAlex

In Part I of this work, a heat and mass transfer model was developed to calculate the thermal and evaporative resistances of a single-layer fabric system. Using this model, the effects of environmental conditions, air gap, and material properties on the thermal and evaporative resistances have now been studied. The thickness of the air gap and that of the fabric layer were shown to contribute significantly to both the thermal resistance and evaporative resistance. The occurrence of natural convection in the air gap can cause decreases in thermal and evaporative resistances, and needs to be considered to determine the optimal air gap thickness. The porosity of the fabric layer has a distinct effect on the two resistances, and is an excellent property to help achieve both thermal protection and comfort. This work provides the fundamental basis for the optimization of garment fit and material properties to achieve good performance of the clothing system.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.214
GPT teacher head0.343
Teacher spread0.129 · 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

Citations39
Published2011
Admission routes1
Has abstractyes

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