MétaCan
Menu
Back to cohort
Record W2394883367 · doi:10.1080/13640461.2015.1110872

Low-head direct chill slab casting of aluminium alloy AA-6061: 3-D numerical study

2016· article· en· W2394883367 on OpenAlexafffund
Mainul Hasan, Latifa Begum

Bibliographic record

VenueInternational Journal of Cast Metals Research · 2016
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSump (aquarium)Materials scienceSlabCastingMetallurgyAluminiumHeat transferHead (geology)AlloyContinuous castingComposite materialMechanicsGeology

Abstract

fetched live from OpenAlex

An industrial-sized vertical low-head direct chill slab casting process for the aluminium alloy AA-6061 is modelled by taking into account the 3-D turbulent melt flow and heat transfer in the liquid sump and by giving proper consideration of the mushy region solidification aspect of the process. Computed results for the steady-state phase of the casting process are presented for four casting speeds, varying from 60 to 180 mm min−1, for three metal–mould effective heat transfer boundary conditions, varying from 1.0 to 4.0 kW m−2 K−1 and for three inlet melt superheats of 16, 32 and 64 °C. A step-wise change of the cooling water temperature in the mould, impingement and free streaming regions are considered to reflect the actual operations. Detailed results in the form of velocity and temperature fields, solidification shell and mushy region thickness, sump depth and temperature profiles at four critical locations along the caster are provided 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 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 score0.028
Threshold uncertainty score0.056

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 head0.055
GPT teacher head0.355
Teacher spread0.300 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Cast Metals ResearchSame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207