MétaCan
Menu
Back to cohort
Record W4310225048 · doi:10.1080/00405000.2022.2150956

Measuring convection heat transfer coefficients and thermal resistance for protective fabrics using a heated cylinder in a wind tunnel

2022· article· en· W4310225048 on OpenAlexafffund
Tamsaki Asawo, David A. Torvi, David S. Sumner

Bibliographic record

VenueJournal of the Textile Institute · 2022
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWind tunnelCylinderHeat transfer coefficientMaterials scienceMechanicsThermal resistanceHeat transferConvective heat transferConvectionThermalThermodynamicsEnvironmental scienceMeteorologyEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

One tradeoff in design of protective clothing for firefighters and other workers is between thermal protection and thermal stress management. Standard methods for assessing thermal stress management of protective clothing include guarded hot plate and mannequin tests. In this study, a fabric-covered heated cylinder was used in a wind tunnel to measure convection heat transfer coefficients and thermal resistance at various air speeds. Measurements demonstrated the impact of air permeability and wind speed on these parameters. When the cylinder was covered with low permeability fabrics, convection heat transfer coefficients were similar to values predicted using a correlation for bare cylinders. Thermal resistance measurements generally ranked fabrics in the same order as guarded hot plate tests. This cylinder test is more representative of the body’s geometry than hot plate tests, but less expensive to conduct than mannequin tests, and could serve as a bridge between these two tests for design purposes.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.071
GPT teacher head0.274
Teacher spread0.203 · 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".

Quick stats

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the Textile InstituteSame topicTextile materials and evaluationsFrench-language works237,207