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Record W2061778421 · doi:10.1115/1.2844580

Inhomogeneous Multifluid Model for Prediction of Nonequilibrium Phase Transition and Droplet Dynamics

2008· article· en· W2061778421 on OpenAlexaff
Andrew G. Gerber

Bibliographic record

VenueJournal of Fluids Engineering · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsNon-equilibrium thermodynamicsMechanicsPhase transitionTransonicPhysicsStatistical physicsMetastabilityInertiaMomentum (technical analysis)NucleationClassical mechanicsThermodynamicsAerodynamics

Abstract

fetched live from OpenAlex

A pressure based Eulerian multifluid model for application to phase transition with droplet dynamics in transonic high-speed flows is described. It is implemented using an element-based finite-volume method, which is implicit in time and solves mass and momentum conservation across all phases via a coupled algebraic multigrid approach. The model emphasizes treatment of the condensed phases, with their respective velocity and thermal fields, in inertial nonequilibrium and metastable gas flow conditions. The droplet energy state is treated either in algebraic form or through transport equations depending on appropriate physical assumptions. Due to the complexity of the two-phase phenomena, the model is presented and validated by exploring phase transition and droplet dynamics in a turbine cascade geometry. The influence of droplet inertia on localized homogeneous nucleation is examined.

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: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.019
GPT teacher head0.219
Teacher spread0.200 · 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

Citations46
Published2008
Admission routes1
Has abstractyes

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Same venueJournal of Fluids EngineeringSame topicnanoparticles nucleation surface interactionsFrench-language works237,207