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Record W2016660534 · doi:10.1002/fld.964

A projection scheme for incompressible multiphase flow using adaptive Eulerian grid: 3D validation

2005· article· en· W2016660534 on OpenAlexaff
Tongwen Chen, P Minev, K. Nandakumar

Bibliographic record

VenueInternational Journal for Numerical Methods in Fluids · 2005
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDiscretizationEulerian pathProjection methodGridCompressibilityProjection (relational algebra)MathematicsIncompressible flowMesh generationMultiphase flowComputational fluid dynamicsApplied mathematicsFinite element methodGeometryFlow (mathematics)Computer scienceMathematical analysisMathematical optimizationMechanicsAlgorithmPhysicsDykstra's projection algorithm

Abstract

fetched live from OpenAlex

A three-dimensional finite element method for incompressible multiphase flows with capillary interfaces is developed based on a (formally) second-order projection scheme. The discretization is on a fixed (Eulerian) reference grid with an edge-based local h-refinement in the neighbourhood of the interfaces. The fluid phases are identified and advected using the level-set function. The reference grid is then temporarily reconnected around the interface to maintain optimal interpolations accounting for the singularities of the primary variables. Using a time splitting procedure, the convection substep is integrated with an explicit scheme. The remaining generalized Stokes problem is solved by means of a pressure-stabilized projection. This method is simple and efficient, as demonstrated by a wide range of difficult free-surface validation problems, considered in the paper. Copyright © 2005 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
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)0.0020.001

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 head0.065
GPT teacher head0.413
Teacher spread0.348 · 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

Citations4
Published2005
Admission routes1
Has abstractyes

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Same venueInternational Journal for Numerical Methods in FluidsSame topicFluid Dynamics and Heat TransferFrench-language works237,207