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Record W1892598544 · doi:10.1002/cjce.22312

CFD simulation of the unsteady flow of a single coal log in a pipe

2015· article· en· W1892598544 on OpenAlexvenueno aff
Wenjuan Li, Shengyong Lu, Yong Liu, Rupei Wang, Qunxing Huang, Jianhua Yan

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersProject 211Zhejiang University
KeywordsCoalMechanicsDragFluentPressure dropTurbulenceFlow (mathematics)Coal miningShear forceComputational fluid dynamicsGeologyEngineeringStructural engineeringPhysicsWaste management

Abstract

fetched live from OpenAlex

Abstract This study proposes a numerical simulation of the unsteady turbulent flow of a single coal log in a pipe using a dynamic mesh method. An eccentric two‐dimensional dynamic model is developed in FLUENT 6.2. The model contains a straight section and a bent section. Comparisons of the coal log velocity and the pressure distributions of the pipe and the coal log surface are made between the calculated results and the experimental data. The shear force and the friction coefficient are also calculated to verify changes of the influence of water on the coal log. The results of this simulation display the dynamic motion of the coal log for startup and turning. Water drag force moves the coal log, making the coal log velocity greater than the water velocity. The lifting force lifts the coal log and moves its tail slightly upward. The pressure drop of the whole pipe is respectively larger in the presence of the coal log. The pressure along the coal log surface slowly declines. The appearance of a bend impedes the motion of the coal log and enhances abrasion. The coal log has difficulty being suspended due to decreased lifting force and increased friction. The pressure on the coal log surface rises.

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 score0.156
Threshold uncertainty score0.257

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 head0.015
GPT teacher head0.188
Teacher spread0.172 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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