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Record W2016434440 · doi:10.2514/1.t4568

Modeling of Transport Phenomena in Low-Head Direct-Chill Caster for AA7050 Alloy

2015· article· en· W2016434440 on OpenAlexafffund
Mainul Hasan, Latifa Begum

Bibliographic record

VenueJournal of Thermophysics and Heat Transfer · 2015
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsMcGill University
FundersMcGill University
KeywordsSump (aquarium)Materials scienceHeat transferCastingHead (geology)AlloyMoldSlabWater coolingMechanicsTurbulenceMetallurgyComposite materialMechanical engineeringStructural engineeringGeology

Abstract

fetched live from OpenAlex

As an extension of earlier work, a three-dimensional numerical model has been developed for an industrial-scale low-head direct-chill slab casting process for the long freezing range aluminum alloy AA7050. The model has taken into account the coupled nature of the turbulent melt flow and the solidification heat transfer aspect of the direct-chill casting process. Computer simulations were performed to predict the velocity and temperature fields, the sump profile, the mushy thickness, and the shell thickness at the exit of the mold. Specifically, the aforementioned results were obtained for four casting speeds, varying from 60 to , for three metal–mold effective heat transfer boundary conditions, varying from 1.0 to , and for three pouring temperatures of inlet melt, namely, 645, 661, and 693°C. A stepwise change of the cooling water temperature in the mold and impingement and free-streaming regions, were considered to reflect the temperature history of the cooling water conditions in the industry. The importance of the present study lies in the fact that, by rolling the cast slabs from the direct-chill process, the obtained plates, sheets, strips, and foils of the aforementioned alloy are used extensively by the aerospace industry.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.023
GPT teacher head0.217
Teacher spread0.194 · 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 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

Citations4
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Thermophysics and Heat TransferSame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207