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Numerical study of dendritic growth during solidification using front-tracking method

2016· article· en· W2532170765 on OpenAlexaff
Mohamad Ali Jaafar, Stéphane Gibout, Daniel R. Rousse, Jean-Pierre Bédécarrats

Bibliographic record

VenueJournal of Physics Conference Series · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSolidification and crystal growth phenomena
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsTracking (education)Interface (matter)Front (military)AnisotropyCode (set theory)MechanicsHomogeneousSet (abstract data type)Materials scienceComputer scienceBiological systemDirectional solidificationSimulationMechanical engineeringPhysicsStatistical physicsOpticsComposite materialEngineeringMicrostructure

Abstract

fetched live from OpenAlex

A two dimensional front-tracking method is developed in order to model dendritic growth during solidification processes of pure substances. The method uses a sequential set of moving marker points to describe and track the liquid-solid interface which evolves over a fixed background mesh describing the whole medium. The code behaviour is first checked by a simple stable case of solidification to provide homogeneous velocity at the interface. Then, test examples of unstable solidification cases considering different modes of anisotropy are performed. Finally, interface evolution, with primary and secondary branches, is described, showing the ability of the code to study realistic dendritic growth characteristics.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.310
Teacher spread0.255 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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