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Record W2129697028 · doi:10.1002/cnm.719

A free surface updating methodology for marker function‐based Eulerian free surface capturing techniques on unstructured meshes

2004· article· en· W2129697028 on OpenAlexafffund
Steven Dufour, Ahamadi Malidi

Bibliographic record

VenueCommunications in Numerical Methods in Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsPolytechnique Montréal
FundersAUTO21 Network of Centres of ExcellenceNetworks of Centres of Excellence of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsDiscretizationPolygon meshFree surfaceFunction (biology)Computational fluid dynamicsSurface (topology)Finite element methodVariable (mathematics)Computer scienceFlow (mathematics)Applied mathematicsInterface (matter)AlgorithmMathematicsComputational scienceMathematical optimizationMechanicsMathematical analysisGeometryPhysicsEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Abstract The numerical modelling of non‐miscible free surface flows using an interface capturing strategy, where a marker function is used to identify each fluid, is popular in the computational science literature. As the marker function is advected by the fluid flow, the region of transition of the marker gets deformed, this being caused by the finite element discretization of the transport equation, the time‐integration scheme and the type of mesh used. The challenge is to update this region of transition at regular time intervals, without artificially creating or losing mass. A methodology is proposed to update the region of transition of the marker variable on unstructured meshes, using a local least‐squares‐based strategy which reduces numerical mass loss. It is shown that this technique proves to be an effective strategy for the updating of an advected marker variable. Copyright © 2004 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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.344
Teacher spread0.289 · 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
GenreMethods

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

Citations2
Published2004
Admission routes2
Has abstractyes

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