MétaCan
Menu
Back to cohort
Record W3123160069 · doi:10.1021/acs.iecr.0c05330

Particle Velocity Distribution and Its Prediction in a 14 m Two-Dimensional Circulating Fluidized Bed Riser

2021· article· en· W3123160069 on OpenAlexaff
Chengxiu Wang, Yang Xiao, Min Wang, Jiazhi Zhang, Xingying Lan, Jinsen Gao, Mao Ye, Jesse Zhu

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2021
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsWestern University
FundersChina University of Petroleum, BeijingNational Natural Science Foundation of China
KeywordsFluidized bed combustionAnnulus (botany)MechanicsParticle (ecology)FluidizationFlux (metallurgy)Work (physics)Flow (mathematics)Materials scienceFluidized bedEnvironmental scienceThermodynamicsPhysicsGeologyMetallurgyComposite material

Abstract

fetched live from OpenAlex

Particle velocity (Vp) is investigated comprehensively in a two-dimensional circulating fluidized bed (CFB) riser with a superficial gas velocity (Ug) of 5–7 m/s and a solids circulation rate (Gs) up to 500 kg/m2s which are similar to the industrial operating conditions. Results indicate that the lateral profile of Vp is nonuniform showing roughly a “core-annulus” or parabolic trend with Gs less than 300 kg/m2s, while it presents a uniform structure under relatively high solids loading conditions with Gs of 500 kg/m2s. For axial distribution of Vp, lower Vp is near the riser bottom, and it gradually increases along the riser, indicating flow development. Fluctuation of the average particle velocity along the riser is more vigorous when Gs is high up to 400 kg/m2s. Both axial and lateral particle velocities can be predicted using empirical correlations established in this work. Empirical equation to describe the relationship of Vp against solids holdup (εs) works better for low Gs. Based on the analysis, it may suggest different fluid mechanisms for low and high solids flux conditions.

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)0.001
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 score0.250
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.053
GPT teacher head0.287
Teacher spread0.234 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicGranular flow and fluidized bedsFrench-language works237,207