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Record W1996518708 · doi:10.1115/1.2746918

Surface Tension Measurement at High Temperatures by using Dynamics of Melt Flow

2007· article· en· W1996518708 on OpenAlex
A. Moradian, J. Mostaghimi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Fluids Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSurface tensionMechanicsDrop (telecommunication)Materials scienceMaximum bubble pressure methodNozzleInviscid flowRotational symmetryThermodynamicsChemistryPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Surface tension of melts at high temperature has significant effects on different industrial processes. In a new containerless method for surface tension measurement, an atmospheric radio-frequency inductively coupled plasma melts metallic or ceramic rods and a high-speed charge-coupled device records the drop formation caused by melting. Pendant drops produced by the melt flow are compared with the theoretical Young–Laplace (YL) profiles. Moreover, the dynamics of the melt flow is mimicked by using numerical simulations of drop injection from a nozzle. The numerical model solves the axisymmetric Navier–Stokes equations for both the melt and the surrounding gas by using the finite volume method. Since the YL equations provide theoretical pendant drop profiles based on an inviscid quasiequilibrium condition, a detailed study of the differences between experimental, numerical, and theoretical profiles demonstrates some of the hydrodynamic effects influencing the surface tension measurement methods, which are based on drop profiles. Results from this surface tension measurement method, in addition to a discussion on the hydrodynamic effects, are presented.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.197
Teacher spread0.187 · 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