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Record W2002496352 · doi:10.2140/memocs.2014.2.45

Particles for fluids: SPH versus vortex methods

2014· article· en· W2002496352 on OpenAlexfundno aff
A. Colagrossi, G. Graziani, Mario Pulvirenti

Bibliographic record

VenueMathematics and Mechanics of Complex Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsnot available
FundersOtto von Guericke University MagdeburgUniversität Duisburg-EssenFreie Universität BerlinBilkent ÜniversitesiCentre National de la Recherche ScientifiqueAkademie Věd České RepublikyUniversity of North Carolina at Chapel HillUniversität zu KölnUniversità degli Studi di PaviaCarnegie Mellon UniversityMinistero dell’Istruzione, dell’Università e della RicercaUniversité de LyonUniversität WienMcGill UniversityIndian National Science AcademyUniversidad Rey Juan CarlosUniversity of PittsburghLouisiana State UniversityWayne State UniversityVanderbilt University
KeywordsVortexMechanicsClassical mechanicsPhysics

Abstract

fetched live from OpenAlex

We review the basic ideas underlying two popular particle methods for fluids: smoothed-particle hydrodynamics and the vortex method. We discuss convergence problems, numerical schemes, and examples of practical simulations.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.049
GPT teacher head0.311
Teacher spread0.262 · 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

Citations20
Published2014
Admission routes1
Has abstractyes

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