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Record W2519033663 · doi:10.1002/cjce.22684

Characterization of flow regimes in fluidized beds by information entropy analysis of pressure fluctuations

2016· article· en· W2519033663 on OpenAlexvenueno aff
Jianbin Wang, Wenqi Zhong, Hao Zhang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsDimensionless quantityEntropy (arrow of time)MathematicsStatistical physicsFluidizationRényi entropyTransfer entropyTurbulenceThermodynamicsMechanicsPhysicsPrinciple of maximum entropyStatisticsFluidized bed

Abstract

fetched live from OpenAlex

Flow regimes in a gas‐solid cylindrical fluidized bed of 150 mm (i.d.) × 1.2 m (high) with three different sand masses (1.5, 3, and 4.5 kg) were studied via information entropy analysis of pressure fluctuations. Three classes of methods of information entropy were adopted to characterize the flow regimes. It is shown that the first‐class dimensional methods are suitable for illustrating the distinct characteristic of the flow regimes under different operating conditions, while the other two dimensionless methods are appropriate for finding the similarities or common rules. Tsallis entropy and Renyi entropy do not provide more valuable information than Shannon entropy. Most importantly, the Shannon entropy method is a good choice for characterizing the flow regimes but is weak at revealing the transition velocity (U c ) clearly. This deficiency can be made up by the method of component Shannon entropy. Shannon entropy is partitioned into 4 component Shannon entropies (named CH1CH4), which may be related with a 4‐stage fluidization process assumption. A rule is found that the transition of flow regimes transition from bubbling bed to turbulent bed takes place when the contribution proportion of CH3 is exceeded by that of CH1, and it brings new enlightenment on interpretation of the transition mechanism.

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 score0.079
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.003
GPT teacher head0.160
Teacher spread0.157 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

Explore more

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