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Record W2117374030 · doi:10.1520/jai102036

Convective Mass Transfer Coefficients for Gypsum and Wood Paneling

2009· article· en· W2117374030 on OpenAlexaffabout
Conrad R. Iskra, Christopher M. James, Prabal Talukdar, Carey J. Simonson

Bibliographic record

VenueJournal of ASTM International · 2009
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsUniversity of SaskatchewanGolder Associates (Canada)
Fundersnot available
KeywordsGypsumMass transferConvectionMaterials scienceEnvironmental scienceChromatographyMechanicsChemistryComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract This paper presents the measured convective mass transfer coefficients for humid air flowing above an initially dry gypsum and wood panel (equilibrium with 30 % RH air) in the transient moisture transfer (TMT) facility at the University of Saskatchewan. The convective coefficients are measured during laminar flow at three different Reynolds numbers (Re=800, 1500, and 2100) and with three different humidities in the air stream (50, 70, and 90 % RH). The results show that the measured mass transfer coefficients are lower than the values measured above an open water surface by Iskra and Simonson in a previous work. For gypsum, the convective mass transfer coefficient varies from 0.0016 to 0.0033 m/s (1.2×10−8 to 2.4×10−8 s/m) with 95 % confidence uncertainty bounds ranging from ±20 to ±57 %. For wood, the convective mass transfer coefficient varies from 0.0001 to 0.0008 m/s (9.4×10−10 to 5.7×10−9 s/m) with 95 % confidence uncertainty bounds ranging from ±50 to±300 %. The experimental data are analyzed with a 1-D heat and mass transfer numerical model to check the discrepancy with the experimental data. The uncertainty associated with an indirect determination of mass transfer coefficient is discussed.

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 score0.134
Threshold uncertainty score0.279

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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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

Citations4
Published2009
Admission routes2
Has abstractyes

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