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MODELING OF FLUID FLOW AND HEAT TRANSFER OF AA1050 ALUMINUM ALLOY IN A MODERN LOW-HEAD DIRECT-CHILL SLAB CASTER

2016· article· en· W2548165968 on OpenAlexaff
Latifa Begum, Mainul Hasan

Bibliographic record

VenueHeat Transfer Research · 2016
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceSlabMoldComputational fluid dynamicsHead (geology)Sump (aquarium)Heat transferMechanicsCastingCasterHeat transfer coefficientWater coolingContinuous castingSuperheatingFlow (mathematics)Composite materialMechanical engineeringThermodynamicsStructural engineeringGeology

Abstract

fetched live from OpenAlex

A low-head hot-top mold is modeled for the vertical direct-chill casting (DCC) process where the melt is assumed to have been delivered through the entire top cross section of the caster. The previously verified in-house 3D Computational Fluid Dynamics (CFD) code is extended to model an industrial-sized AA1050 slab for the above caster for steady-state operation. For the generalization of the predicted results, nondimensional parameters governing this problem were identified. To keep consistency with the industrial cooling strategy, a stepwise change of the cooling water temperature in the mold, in the impingement and in free streaming regions was considered. A series of parametric studies were conducted by varying the important DCC process parameters, namely the casting speed ranging from 60 to 180 mm/min, inlet melt superheat, ranging from 16°C to 64°C, as well as the effective heat transfer coefficient (HTC) at the metal–mold contact region, varying from 0.75 to 3.0 kW/(m2·K). The velocity field, the temperature distributions, and the local surface temperature profiles are presented and discussed. The sump depth and the mushy thickness at the ingot center are seen to increase linearly with the increasing casting speed, whereas the shell thickness at the exit of the mold decreases linearly with the casting speed. The thickness of the solid shell at the mold exit is increased by about 4% for the aforementioned increase in HTC. Correlations of the above-mentioned quantities with casting speed are reported to provide useful guidelines for vertical DCC design engineers and 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.040
GPT teacher head0.270
Teacher spread0.230 · 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.

Study designBench or experimental
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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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