MétaCan
Menu
Back to cohort
Record W4362588127 · doi:10.1002/cjce.24904

Numerical simulations and validation of gas–solid flows in a fluidized‐bed roaster based on the <scp>CFD‐DPM</scp> model

2023· article· en· W4362588127 on OpenAlexvenueno aff
Xue‐Yi Ma, Dexi Wang, Bo Liu, Hui Dong, Liang Zhao

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsComputational fluid dynamicsFlow (mathematics)MechanicsFluidized bed combustionComputer simulationMaterials scienceFluidized bedMechanical engineeringEngineeringWaste managementPhysics

Abstract

fetched live from OpenAlex

Abstract In view of the complex gas–solid flow characteristics in a fluidized‐bed roaster, the discrete phase model (DPM) provided by ANSYS software was used to numerically analyze the model using a coupled algorithm. The asymmetric flow phenomenon in the transition section at the top of the furnace was found to be unfavourable to the gas–solid flow, and an inverted U‐shaped furnace structure was proposed to optimize the transition section at the top of the furnace. A large cold experimental setup was built to verify the model. The results showed that the air velocity is mainly in the axial upward direction; under the action of the gas, the solid particles and the air velocity are basically in the same direction. The main furnace and subfurnace connection section changes the movement of the gas–solid mixture, and its unreasonable structure leads to the asymmetric flow phenomenon of the gas–solid fluid at the top of the furnace. Compared with the previous furnace structure, the uniformity of gas–solid flow in the optimized ‘inverted U‐shaped’ structure has been significantly improved. The cold experimental results are in good agreement with the numerical simulation results, which verifies the accuracy of the proposed model.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.414

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 head0.013
GPT teacher head0.199
Teacher spread0.186 · 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 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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicGranular flow and fluidized bedsFrench-language works237,207