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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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.006
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 teacher head, not a consensus.

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