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Record W2967452963 · doi:10.1021/acs.iecr.9b01751

Tracking the Flow Dynamics in Circulating Fluidized Bed through High-Speed Photography

2019· article· en· W2967452963 on OpenAlexafffund
Xiaoyang Wei, Jesse Zhu

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2019
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsMechanicsWork (physics)Tracking (education)Particle (ecology)Flux (metallurgy)Fluidized bedFlow (mathematics)Particle velocityFluidized bed combustionFluidizationMaterials scienceThermodynamicsPhysicsGeology

Abstract

fetched live from OpenAlex

In gas–solid circulating fluidized beds, particle velocity and solids flux are crucial parameters for understanding the gas–solid interaction. In the literature, the time-average information has been investigated intensely, but the analysis of instantaneous information remains lacking. In this work, flow behavior in a narrow rectangular circulating fluidized bed has been recorded using a high-speed camera. Then, with a verified correlation between grayscale and solids holdup, particle velocity is computed by tracking local solids holdup characteristics using a two-dimensional cross-correlation, and solids flux is computed by multiplying the particle velocity with particle density and local solids holdup. The time-average information in this work is found to be consistent with that of previous publications. Instantaneous information of particle velocity and solids flux, including relations with local solids holdup, instantaneous radial distributions, instantaneous solids circulating rates, and impacts of macro-fluctuation is analyzed systematically and discussed in detail for the first time.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.275
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2019
Admission routes2
Has abstractyes

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