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Calculation of Initial Stage of Solidified Shell Deformation during <i>γ</i> to <i>δ</i> Transformation in Mold

2019· article· en· W2984595865 on OpenAlexaff
Kohei Furumai, A.B. Phillion, Hatem S. Zurob

Bibliographic record

VenueISIJ International · 2019
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMoldMaterials scienceHeat transferHeat fluxInfiltration (HVAC)Composite materialShell (structure)Flux (metallurgy)ThermalMetallurgyMechanicsThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Solidification shell deformations within the mold during continuous casting have been calculated in order to clarify the influence of mold flux infiltration variability on the cooling rate, the width of the low heat flux region, the height of air gap, the unevenness of solidified shell, and the resulting strain in the solidified shell. A sequentially coupled thermal-mechanical finite element model has been developed to perform the calculations. The simulation includes heat transfer and shell deformation in a growing solidified shell, along with the delta-to-gamma transformation. Further, it takes into account the effects of variability in mold flux infiltration and air gap formation on heat transfer into the mold, as well as the effect of cooling rate on the thermal expansion resulting from delta-to-gamma transformation. The results showed that mild cooling and small width of low heat flux region (i.e. low variability in mold flux infiltration) strongly decrease the height of the air gap, the unevenness in the solidified shell and the strain in the solidified shell. It is confirmed that it is important to prevent the variation and optimize the cooling rate in mold flux infiltration, especially at near the meniscus region of δ to γ transformation in order to minimize longitudinal crack formation.

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)0.000
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.243
Teacher spread0.234 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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