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Record W4313261991 · doi:10.1021/acs.iecr.2c03347

Heat Transfer Intensification Mechanism of a Baffled Fluidized Bed Heat Exchanger

2022· article· en· W4313261991 on OpenAlexaff
Jiantao Li, Xiuying Yao, Lei Zhang, Chunxi Lu, Zheng‐Hong Luo, Xiaotao Bi

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2022
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of British Columbia
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsBaffleHeat transferFluidized bedHeat exchangerHeat transfer coefficientMaterials scienceMoving bed heat exchangerDynamic scraped surface heat exchangerPlate heat exchangerShell and tube heat exchangerMechanicsFluidizationMicro heat exchangerThermodynamicsConcentric tube heat exchangerPlate fin heat exchangerHeat spreaderCritical heat flux

Abstract

fetched live from OpenAlex

Gas–solid fluidized bed heat exchangers are commonly applied to remove heat in highly exothermic processes. The heat transfer performance is related to hydrodynamics. Baffles, such as ring-type baffles, louver baffles, and packings, are widely applied to improve the hydrodynamics in a fluidized bed. In this study, a baffled fluidized bed heat exchanger was proposed to enhance the bed-to-surface heat transfer coefficient. The baffles are specially designed to intensify the contact characteristics between heat transfer tubes and fluidized bed medium. The hydrodynamics and heat transfer characteristics were compared between the conventional and the newly designed baffled heat exchanger to explore the intensification mechanism in dilute and dense fluidization regions. Proper arrangement of the baffles in the fluidized bed heat exchanger increases the time fraction of the particle packet as well as the solid holdup, which enhances the heat transfer coefficient in dilute region. For the dense region, the contact time between the packet and heat transfer surface is decreased in the baffled fluidized bed heat exchanger due to the reduced bubble size, increased bubble frequency, and enhanced radial movement of bubbles and particles. Therefore, the bed-to-surface heat transfer in the dense region is significantly intensified.

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 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 score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.070
GPT teacher head0.277
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

Citations6
Published2022
Admission routes1
Has abstractyes

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