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Record W3112516268 · doi:10.1002/cjce.23993

Flow development and prediction of solids concentration in a large gas‐solids turbulent fluidized bed

2020· article· en· W3112516268 on OpenAlexvenueno aff
Zhunzhun Ma, Hongfang Ma, Weixin Qian, Yongzheng Li, Haitao Zhang, Qiwen Sun, Weiyong Ying

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsTurbulenceMaterials scienceFlow (mathematics)Fluidized bedMechanicsExponential functionThermodynamicsAnalytical Chemistry (journal)ChemistryChromatographyPhysicsMathematics

Abstract

fetched live from OpenAlex

Abstract In a 5 m high turbulent fluidized bed with a 0.3 m inner diameter, local solids concentration in the dense region and axial pressure difference along the column were measured using an optical fibre probe (PC6M) and differential pressure sensors (PX653), respectively. The time‐series signals of the solids concentration fluctuations are analyzed via statistical methods. The axial and radial profiles of solids concentration are non‐uniform. In the developing dense region, the radial profiles of solids concentration are significantly influenced by operating conditions, and the fluctuation of solid concentration is considerably enhanced with the development of flow. In the fully developed dense region, the axial solids concentration decreases monotonously with height, and the radial profiles of solids concentration are exponential. The flow development in the dense region could be accelerated by the superficial gas velocity. Based on the analysis of the characteristics of the radial solids concentration distribution in the dense region, an empirical correlation of this distribution is proposed. The formulated correlation is found to be consistent with the experimental data reported in the literature.

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 score0.284
Threshold uncertainty score0.398

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 integrity0.0000.000
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 head0.008
GPT teacher head0.169
Teacher spread0.161 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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