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Record W2002218350 · doi:10.1115/1.4029478

Three-Dimensional Numerical Study of a Low Head Direct Chill Slab Caster for Aluminum Alloy AA5052

2015· article· en· W2002218350 on OpenAlexaff
Mainul Hasan, Latifa Begum

Bibliographic record

VenueJournal of Thermal Science and Engineering Applications · 2015
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsCasterIngotMaterials scienceCastingHead (geology)SuperheatingComputational fluid dynamicsMetallurgySlabMoldLiquid metalSump (aquarium)AluminiumAlloyComposite materialMechanicsStructural engineeringEngineeringThermodynamics

Abstract

fetched live from OpenAlex

A 3D numerical study is carried out for a vertical direct chill (DC) rolling ingot caster for an aluminum alloy (AA-5052). The model incorporated the coupled turbulent melt flow and solidification aspects of the casting process. The caster consists of a low-head hot-top mold. The melt is assumed to have been delivered through the entire top cross section of the caster. The previously verified in-house computational fluid dynamics (CFD) code is used to investigate the effects of the important parameters such as casting speed, inlet melt superheat, and mold-metal contact effective heat transfer coefficient (HTC) on the low-head casting process. It is found that the sump depth (SD), liquid depth, and mushy thickness (MT) at the center of the ingot increase linearly with the casting speed while the shell thickness (ST) at the exit of the mold decreases linearly with the casting speed. Useful correlations concerning the above quantities with casting speed have been provided for the benefit of DC casting operators.

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 score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.301
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

Citations3
Published2015
Admission routes1
Has abstractyes

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