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Record W4387079192 · doi:10.1016/j.powtec.2023.119014

Flow regimes and transitions in an ultra-high temperature gas-solid fluidized bed

2023· article· en· W4387079192 on OpenAlexfundno aff
Qingjin Zhang, Liangliang Fu, Guangwen Xu, Dingrong Bai

Bibliographic record

VenuePowder Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersUniversity of British ColumbiaNational Natural Science Foundation of China
KeywordsFluidizationFluidized bedFlow (mathematics)Materials scienceMechanicsThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Gas-solid fluidized bed reactors are widely used in industrial operations and thus have led to a significant amount of fundamental research over decades. For better design and operation of fluidized bed systems, it is essential to understand the fluidization regime and the related transition conditions. Dr. John R. Grace, a prominent figure and pioneer in fluidization science and technology, has made significant contributions to understanding fluidized bed systems, including fluidization regimes and their transitions. This study is a tribute to his extraordinary legacy, focusing on investigations of fluidization regimes and transitions in a laboratory-scale gas-fluidized bed operating from ambient to 1600 °C. The results indicate that the fluidization regime transitions follow three distinct pathways across different temperature ranges: below 300 °C, from 300 to 1400 °C, and above 1400 °C. These distinctive transitions arise as a result of the varying significances of hydrodynamic and interparticle forces in the fluidized bed of particles investigated.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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 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

Citations9
Published2023
Admission routes1
Has abstractyes

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