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Record W2139963201 · doi:10.1002/cjce.22143

Dominant flow structures in gas–liquid–solid fluidized beds

2014· article· en· W2139963201 on OpenAlexvenueno aff
Omid Arjmandi‐Tash, Reza Zarghami

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsFlatness (cosmology)MechanicsFluidizationSkewnessBubbleStandard deviationFluidized bedMaterials scienceSluggingLow frequencyRange (aeronautics)Flow (mathematics)Analytical Chemistry (journal)ChemistryThermodynamicsChromatographyPhysicsMathematicsComposite material

Abstract

fetched live from OpenAlex

Effective time series analysis techniques in time and frequency domains were applied to characterize three‐phase fluidization under a wide range of gas and liquid velocities. The experiments were carried out in a laboratory scale fluidized bed, operated under ambient conditions. Standard deviation of pressure fluctuations successfully detected four distinct regimes, namely compacted bed, agitated bed, coalesced and discrete bubble regimes (or discrete and dispersed bubble regimes at extremely low gas velocities). A minimum in skewness and average cycle frequency and a maximum in flatness indicated a minimum deviation from larger structures of the bed. A transition between macro and finer structures occurred during agitated bed regime and accordingly, minimum liquid fluidization velocity was perceived by skewness, flatness, and average cycle frequency analyses. The results showed that at very low liquid velocities in the compacted bed regime, the power spectrum at lower frequencies slightly increased with increasing liquid velocity. This results in the consecutive appearance of single bubbles of low frequencies. With further increase in the liquid velocity at agitated bed regime the power spectrum at lower frequencies gradually decreased. At higher liquid velocities near U Lmf , the peak dominant frequency is transmitted from low frequency ranges of 2 Hz to the higher frequency of about 10 Hz.

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 score0.099
Threshold uncertainty score0.635

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 head0.004
GPT teacher head0.175
Teacher spread0.171 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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