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Record W2027383851 · doi:10.1115/imece2003-43967

Computational Modelling of Continuous Casting

2003· article· en· W2027383851 on OpenAlexaff
David Sinton, B. R. Baliga

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsMcGill UniversityUniversity of Toronto
Fundersnot available
KeywordsMechanicsHeat transferFluid dynamicsControl volumeCastingFinite element methodGridContinuous castingFinite volume methodMaterials scienceMechanical engineeringBoundary value problemNatural convectionComputational fluid dynamicsRotational symmetrySteady state (chemistry)Computer scienceThermodynamicsEngineeringPhysicsComposite materialChemistryMathematicsGeometry

Abstract

fetched live from OpenAlex

Computer simulations of fluid flow and heat transfer phenonmena in a continuous casting process with direct-chill (DC) boundary conditions are presented and discussed in this paper. The investigation is limited to a steady-state, two-dimensional axisymmetric system, used for DC continuous casting of a zero-freezing-range aluminum-magnesium alloy (A6063). An adaptive-grid numerical method is used in these simulations. The grid is designed to delineate the solid-liquid interface using a structured adaptation technique. The fluid flow and thermal fields are predicted using a control-volume finite element (CVFEM). Comparisons of the calculated solid-liquid interface geometries with those reported in earlier experimental and numerical studies are presented in this paper. In addition, the role of natural convection in this casting process is investigated and presented.

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: none
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.184
Teacher spread0.166 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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