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Record W2031487048 · doi:10.1002/cjce.20610

Numerical simulation of the transient temperature distribution inside moving particles

2011· article· en· W2031487048 on OpenAlexvenueno aff
Robin Schmidt, Petr A. Nikrityuk

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersFederalno Ministarstvo Obrazovanja i Nauke
KeywordsMechanicsHeat transferLaminar flowClassical mechanicsPhysicsMaterials scienceThermodynamics

Abstract

fetched live from OpenAlex

Abstract This work is devoted to the two‐dimensional (2D) numerical simulation of heat and fluid flow by granular mixing in a horizontally rotating kiln. The heat and fluid flow in the gas phase are solved directly using a fixed Eulerian grid. At the same time, the particle dynamics and their collisions are solved on a Lagrangian grid. The no‐slip boundary condition on the particle surface is implemented using the fictitious boundary method. The heat transfer inside the particles is calculated utilising two models: the first is the direct solution of the energy conservation equation in Lagrangian and Eulerian space and the second is the so‐called linear model, which assumes a homogeneous distribution of the temperature inside each particle. Numerical simulations showed that if the thermal diffusivity of the gas phase significantly exceeds the same parameter of the particles, the linear model over‐predicts the heating rate of the particles. The analysis of the time‐averaged flow field inside the kiln showed that in the gas phase a double vortex structure is formed which increases the convective heat transfer in the upper part of the particulate bed. The influence of the particle size, the angular velocity of the drum and the fluid on the heating rates of particles is studied and discussed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.232

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 head0.009
GPT teacher head0.168
Teacher spread0.159 · 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

Citations19
Published2011
Admission routes1
Has abstractyes

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