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Record W2005250675 · doi:10.1243/095440803322611642

Effect of liquid addition on heat transfer in gas-fluidized beds of large light particles

2003· article· en· W2005250675 on OpenAlexafffund
Y. Nagahashi, John R. Grace, Yutaka Asako, Norman Epstein, D H Lee, Akira Yokogawa

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of British Columbia
FundersJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of Canada
KeywordsHeat transferMaterials scienceTube (container)Fluidized bedVaporizationThermal conductionFluidizationConvectionParticle (ecology)Volume (thermodynamics)ThermodynamicsHeat transfer enhancementConvective heat transferMechanicsHeat transfer coefficientComposite materialGeology

Abstract

fetched live from OpenAlex

When liquid is added to large-particle gas-fluidized beds where the liquid density is similar to the particle density, the liquid addition can cause a dramatic increase in the velocity range and intensity of fluidization. In this paper it is shown that this also leads to strong enhancement of the heat transfer between the bed and an immersed tube. Experiments were carried out using a heated tube of outer diameter 25 mm in air-fluidized beds of polystyrene particles or glass beads to which small quantities of water (typically equivalent to 10 per cent of the bed volume) were added. The results from the heat transfer experiments are explained with the aid of observations of the tube surface using an industrial endoscope located inside the tube. The key factors underlying the enhancement of heat transfer are identified to be heat conduction to liquid-solid aggregates, convection to the liquid phase, convection to the gas phase and vaporization.

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: 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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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

Citations5
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical EngineeringSame topicGranular flow and fluidized bedsFrench-language works237,207