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Record W1970644618 · doi:10.1007/s10853-006-1129-x

Modeling the impact of a molten metal droplet on a solid surface using variable interfacial thermal contact resistance

2006· article· en· W1970644618 on OpenAlexaff
Minxia Xue, Yoav Heichal, S. Chandra, J. Mostaghimi

Bibliographic record

VenueJournal of Materials Science · 2006
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceThermal contact conductanceSolid mechanicsContact resistanceComposite materialSurface finishAluminiumHeat transferSurface roughnessContact areaContact angleAlloyInterfacial thermal resistanceThermalSubstrate (aquarium)Liquid metalSolid surfaceThermal resistanceMechanicsThermodynamicsLayer (electronics)

Abstract

fetched live from OpenAlex

An analytical model of the true area of contact between molten metal and a rough, solid surface has been used to calculate thermal contact resistance and to predict how it changes with surface roughness, substrate thermal properties and contact pressure. This analytical model was incorporated into a three-dimensional, time-dependent numerical model of free-surface flows and heat transfer. It was used to simulate impact, spreading and solidification of molten metal droplets on a solid surface while calculating contact resistance distributions at the liquid–solid interface. Simulations were done of the impact of 4 mm diameter molten aluminum alloy droplets and 50 μm diameter plasma sprayed nickel particles on steel plates. Predicted splat shapes were compared with photographs taken in experiments and simulated substrate temperature variation during droplet impact was compared with measurements.

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)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.277
Teacher spread0.259 · 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

Citations69
Published2006
Admission routes1
Has abstractyes

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