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Record W2602185725 · doi:10.1021/acs.iecr.6b04444

Heat Transfer in a Pulsed Fluidized Bed of Biomass Particles

2017· article· en· W2602185725 on OpenAlexafffund
Dening Jia, Xiaotao Bi, C. Jim Lim, Shahab Sokhansanj, Atsushi Tsutsumi

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHeat transferMechanicsFluidized bedConvective heat transferFlow (mathematics)Particle (ecology)ThermodynamicsBubbleMaterials scienceConvectionParticle sizeChemistryGeologyPhysics

Abstract

fetched live from OpenAlex

Bed-to-surface heat transfer coefficients of various biomass particles were measured in a pulsed fluidized bed. Effects of flow rate, pulsation frequency, particle size distribution, fines, and vibration on heat transfer were investigated. Higher gas flow rates generally yielded higher heat transfer coefficients. Natural frequency was found to be optimum as it offered ample bed movement and internal solid circulation without too much inactivity between pulsation cycles. Heat transfer was also heavily influenced by the interaction between gas convective and particle convective heat transfer, which was verified by the proposed heat transfer model. Two mechanisms, one of which treated the flow-on and flow-off period within a pulsation cycle individually, the other utilized the actual bubble rise velocity obtained via high-speed imaging were identified to account for the significantly different flow behavior below and above natural frequency. Good agreement was observed between experimental data and modeled results.

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)0.001
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 score0.016
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.313
Teacher spread0.223 · 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

Citations27
Published2017
Admission routes2
Has abstractyes

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