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Record W1998087160 · doi:10.1002/anac.200310015

The Weighted Upwinding Finite Volume Method for the Convection Diffusion Problem on a Nonstandard Covolume Grid

2004· article· en· W1998087160 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueApplied Numerical Analysis & Computational Mathematics · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUpwind schemeFinite volume methodMathematicsNumerical diffusionApplied mathematicsGridConservation lawUnstructured gridMathematical optimizationPéclet numberDiffusionConvection–diffusion equationMathematical analysisGeometryMechanicsThermodynamicsPhysicsDiscretization

Abstract

fetched live from OpenAlex

Abstract In this paper we propose a weighted upwinding finite volume method on a nonstandard covolume grid for the variable coefficient convection‐diffusion problems. We give a simple method of choosing the optimal weighted factors depending on the local Peclet's numbers of the original problem. With the optimal factors the method overcomes numerical oscillation and avoids the numerical dispersion and has high‐order computing accuracy. The conservation law and the maximum principle are proved. The second‐order error estimates in L 2 and discrete H 1 norms are obtained for the optimal weighted upwinding finite volume method. Numerical experiments are given to illustrate the performance of the method. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.287
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.014
GPT teacher head0.285
Teacher spread0.272 · 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