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Record W2609805598 · doi:10.3390/met7050154

Discrete Element Method Investigation of Bulk Density and Electrical Resistivity of Calcined Coke Mixes

2017· article· en· W2609805598 on OpenAlexafffund
Behzad Majidi, Geoffroy Rouget, Mario Fafard, Donald Ziegler, Houshang Alamdari

Bibliographic record

VenueMetals · 2017
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversité LavalAlcoa (Canada)Natural Sciences and Engineering Research Council of Canada
FundersNatural Sciences and Engineering Research Council of CanadaAlcoa
KeywordsElectrical resistivity and conductivityMaterials scienceDiscrete element methodElectrical contactsSphere packingRADIUSParticle (ecology)Particle densityParticle sizePetroleum cokeAnodeCokeComposite materialMineralogyMetallurgyMechanicsElectrodeVolume (thermodynamics)ChemistryThermodynamicsElectrical engineeringPhysicsGeology

Abstract

fetched live from OpenAlex

Packing density and electrical resistivity of particles assemblies are important factors for a variety of applications of granular materials. In the present work, a three-dimensional imaging technique is coupled with the discrete element method (DEM) to model anode grade calcined coke particles. Three-dimensional DEM models of samples with different size distribution of particles were studied to obtain the inter-particle contact information. As the content of fine particles increased, a higher inter-particle contact density and smaller average contact radius was observed in the samples. Confronting the DEM data and experimental measurements of electrical resistivity showed the simultaneous effects of packing density and contact density. Samples with higher contact density and smaller contact radius in general held high electrical resistivities. However, if increasing the contact density does not modify contacts between large particles, this will have a positive effect on packing density, so a lower electrical resistivity was obtained.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.272
Teacher spread0.252 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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