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Record W2090032493 · doi:10.1115/fedsm2006-98021

A Mean-Field Free-Energy Lattice Boltzmann Model for Liquid-Vapor Interfaces

2006· article· en· W2090032493 on OpenAlexfundno aff
Shiming Li, Danesh K. Tafti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsnot available
FundersUniversity of AlbertaNational Science Foundation
KeywordsSurface tensionLattice Boltzmann methodsCapillary waveDispersion relationMean field theoryPhysicsThermodynamicsvan der Waals forceBoltzmann equationVapor pressureCondensed matter physicsQuantum mechanics

Abstract

fetched live from OpenAlex

A nonlocal pressure equation is proposed for liquid-vapor interfaces based on mean-field theory. The new nonlocal pressure equation is shown to be a generalized form of the nonlocal pressure equation of the van der Waals theory or the “square-gradient theory”. The proposed nonlocal pressure is implemented in the mean-field free-energy lattice Boltzmann method (LBM) proposed by Zhang et al (2004). The modified LBM is applied to simulate equilibrium interface properties and the interface dynamics of capillary waves. Computed results are validated with Maxwell constructions of liquid-vapor coexistence densities, theoretical relationship of variation of surface tension with temperature, theoretical planar interface density profiles, and the dispersion relation between frequency and wave number describing the dynamics of capillary waves. It is shown that the modified LBM gives very good agreement with the theories. In addition, preliminary calculations of phase transition and binary droplet coalescence are also 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.

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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0040.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.250
Teacher spread0.229 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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