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Prediction of minimum fluidization velocity in pulsed gas-solid fluidized bed

2020· preprint· en· W3122847072 on OpenAlexaff
Yanjiao Li, Chenyang Zhou, Guannan Lv, Yongxin Ren, Yuemin Zhao, Qingxia Liu, Zhonghao Rao, Liang Dong

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFluidizationFluidized bedMaterials scienceMechanicsWork (physics)Flow (mathematics)ThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract: Fluidized bed technology plays a vital role in petrochemistry and coal separation. To enhance fluidization stability, the flow is periodically introduced into the gas-solid fluidized bed to form a pulsed gas-solid fluidized bed. As the main fluidization parameter, the minimum fluidization velocity (umf) can reflect the change of the critical state of particles in the pulsed gas-solid fluidized bed, directly affecting the study of two-phase distribution in the bed. Due to lack of theoretical study on umf in pulsed gas-solid fluidized bed, the work proposed a novel method to predict umf comibined with soft sphere model. Meanwhile, the spring-damping and the resonance force models were established under the action of pulsating airflow. A theoretical model of umf was then derived for pulsed gas-solid fluidized bed based on experimental stress analysis of particles. The novel correlation is basically agreement with almost available data in the literatures and present work.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.025
GPT teacher head0.226
Teacher spread0.201 · 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 designSimulation or modeling
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

Citations12
Published2020
Admission routes1
Has abstractyes

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