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Record W2142649766 · doi:10.1002/fld.620

A computational Lagrangian–Eulerian advection remap for free surface flows

2003· article· en· W2142649766 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal for Numerical Methods in Fluids · 2003
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVolume of fluid methodAdvectionLaminar flowUnstructured gridTurbulenceMechanicsSolverComputer scienceEulerian pathFinite volume methodComputational fluid dynamicsFlow (mathematics)GeometryAlgorithmMathematicsComputational sciencePhysicsApplied mathematicsMathematical optimizationLagrangianThermodynamics

Abstract

fetched live from OpenAlex

Abstract A VOF‐based algorithm for advecting free surfaces and interfaces across a 2‐D unstructured grid is presented. This algorithm is based on a combination of a Computational Lagrangian–Eulerian Advection Remap and the Volume of the Fluid method (CLEAR‐VOF). A set of geometric tools are used to remap the advected shape of the volume fraction from one cell onto the Eulerian fixed unstructured grid. The geometric remapping is used to compute the fluxes onto a group of neighbouring cells of the mesh. These fluxes are then redistributed and corrected to satisfy the conservation of mass. Here, we present methods for developing identification algorithms for surface cells and incorporating them with CLEAR‐VOF. The CLEAR‐VOF algorithm is then tested for translation of several geometries. It is also incorporated in a finite element based flow solver and tested in a laminar flow over a broad‐crested weir and a turbulent flow over a semi‐circular obstacle. 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.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.095
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.025
GPT teacher head0.374
Teacher spread0.349 · 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