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Record W2045172189 · doi:10.1080/10618560410001729090

Modeling interfacial heat transfer from single or multiple deforming droplets

2004· article· en· W2045172189 on OpenAlexaff
Vala Mehdi-Nejad, J. Mostaghimi, S. Chandra

Bibliographic record

VenueInternational journal of computational fluid dynamics · 2004
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMechanicsAdvectionHeat transferVolume of fluid methodMomentum (technical analysis)Materials scienceMass transferVolume (thermodynamics)Momentum transferLevel set methodThermodynamicsFlow (mathematics)PhysicsOptics

Abstract

fetched live from OpenAlex

This paper presents a method to simulate heat transfer across the interface separating immiscible liquids. One-field volume tracking is used which involves solving one set of equations for conservation of mass, momentum and energy. The original idea behind volume tracking methods has been used not only to advect mass and momentum but also energy across cell boundaries. The van Leer method is used to approximate advection temperatures across the sharp temperature gradients existing at fluid/fluid interface. As applications to this method, the fall of single and multiple molten tin droplets in oil, and the rise of octane droplets in water are simulated.For molten tin droplets, results show a region of high temperature in the wake region behind the droplets and a temperature distribution along the droplet surface. During the simulation of the fall of two successive droplets, the heat dissipation from the downstream droplet causes a dramatic change in temperature distribution of the upstream droplet. Finally, large deformations are observed during the rise of octane droplets which greatly affects the heat transfer from these droplets to water.

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)0.001
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.227
Teacher spread0.213 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

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