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Record W4283275410 · doi:10.1002/cjce.24510

A unified finite volume framework for phase‐field simulations of an arbitrary number of fluid phases

2022· article· en· W4283275410 on OpenAlexvenueno aff
Milad Bagheri, Bastian Stumpf, Ilia V. Roisman, Abdolrahman Dadvand, Martin Wörner, Holger Marschall

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
FundersTechnische Universität DarmstadtDeutsche Forschungsgemeinschaft
KeywordsDiscretizationCompressibilityMechanicsWork (physics)Focus (optics)BubbleEnergy minimizationPhase (matter)Isothermal processField (mathematics)Volume of fluid methodComputer scienceMathematical optimizationPhysicsMathematicsThermodynamicsFlow (mathematics)Mathematical analysisOptics

Abstract

fetched live from OpenAlex

Abstract While the phase‐field methodology is widely adopted for simulating two‐phase flows, the simulation of an arbitrary number ( N ≥ 2) of fluid phases at physical fidelity is non‐trivial and requires special attention concerning mathematical modelling, numerical discretization, and solution algorithm. We present our most recent work with a focus on validation for multiple immiscible, incompressible, and isothermal phases, enhancing further our library for diffuse interface phase‐field interface capturing methods in OpenFOAM (FOAM‐extend 4.0/4.1). The phase‐field method is an energetic variational formulation based on the work of Cahn and Hilliard where the interface is composed of a physical diffuse layer resembling realistic interfaces. The evolution of the phases is then governed by the minimization of the free energy of the system. The accuracy of the method is demonstrated for a number of test problems, including a floating liquid lens, bubble rise in two stratified layers, and drop impact onto thin liquid film.

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 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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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 teacher head, 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

Citations6
Published2022
Admission routes1
Has abstractyes

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