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Record W1986640707 · doi:10.1021/ie701744g

Simulation of Moisture Uptake and Transport in a Bed of Urea Particles

2008· article· en· W1986640707 on OpenAlexafffund
Xiaodong Nie, Richard W. Evitts, Robert W. Besant

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2008
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCakingMoistureDiffusionParticle (ecology)SorptionWater vaporConvectionHeat transferWater contentThermal conductionThermodynamicsMaterials scienceChemistryMechanicsComposite materialAdsorptionPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Granular urea is a commonly used fertilizer, and it is subject to caking when exposed to small quantities of moisture. In this paper, coupled heat and moisture transport equations are used to predict one-dimensional temperature and moisture content distributions in a bed of bulk granular urea particles when humid air flows uniformly through the bed. The moisture sorption and transport processes consider two computational domains—water vapor diffusion inside each particle and water vapor convection and diffusion in the interstitial air space in the urea particle bed. For energy transport, the temperature is assumed to be uniform inside each particle, but convective heat transfer and conduction between the urea particles and the interstitial air outside particles occur throughout the bed. Comparisons between simulations and data show agreement within the experimental uncertainties for low Reynolds number conditions, where both internal particle and external bed sorption processes are important for porous urea particles in bulk storage.

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 score0.269
Threshold uncertainty score0.489

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 integrity0.0000.001
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 head0.090
GPT teacher head0.303
Teacher spread0.213 · 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

Citations8
Published2008
Admission routes2
Has abstractyes

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