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Record W1977448525 · doi:10.1115/1.4005426

Isothermal Modeling of Meniscus Oscillation in the Continuous Strip Casting Process

2011· article· en· W1977448525 on OpenAlexafffund
Kevin W. Wilcox, A. Gordon L. Holloway, Andrew G. Gerber

Bibliographic record

VenueJournal of Fluids Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsShadowgraphMeniscusMechanicsMaterials scienceContinuous castingOscillation (cell signaling)CastingNozzleMechanical engineeringComposite materialOpticsEngineeringChemistryPhysics

Abstract

fetched live from OpenAlex

In the continuous strip casting process a meniscus forms a compliant boundary between the casting nozzle and transporting conveyor. Movement of this meniscus during casting has been shown to create surface defects, which require extensive cold work to remove and limit the minimum thickness for which sections may be cast. This paper discusses experimental work conducted to test an analytical model of the meniscus oscillation. A high frame rate shadowgraph technique was used on an isothermal water model of the casting process to observe meniscus motion, and thus allow the calculation of meniscus frequency, amplitude, contact points and contact angles. Both natural frequency and flow excited tests were conducted. Natural frequency tests were also conducted using mercury which has a nonwetting contact angle typical of molten metals. The experimental results were found to be in good agreement with the predictions of theory for both wetting and nonwetting conditions. The experimentally verified analytical model for meniscus motion is valuable to the design of a continuous casting process because it describes the effect of geometrical parameters on meniscus motion and thus provides an opportunity to mitigate the effects of boundary motion on surface quality.

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 score0.155
Threshold uncertainty score0.357

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 head0.018
GPT teacher head0.203
Teacher spread0.185 · 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

Citations1
Published2011
Admission routes2
Has abstractyes

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