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Record W2089470729 · doi:10.2514/1.8025

Three-Dimensional Distributed Mass Weighting for Noninverted Convective Skew Upwinding

2004· article· en· W2089470729 on OpenAlexafffund
Emmanuel O. Ogedengbe, G.F. Naterer

Bibliographic record

VenueJournal of Thermophysics and Heat Transfer · 2004
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUpwind schemeSkewMechanicsWeightingConvectionInflowOutflowNode (physics)MathematicsPhysicsApplied mathematicsMeteorologyMathematical analysisDiscretization

Abstract

fetched live from OpenAlex

Numerical studies of skew upstream differencing are presented for three-dimensional convective heat transfer problems. Two new types of noninverted skew upwind schemes are developed and compared, so that local inversion of influence coefficient matrices is not required. Unlike previous schemes, skew upwind values of temperature are expressed explicitly in terms of surrounding nodal variables. Different mass weighting alternatives, including 3-node/3-point and 4-node/8-point formulations are developed and evaluated. Results are presented for three application problems, that is, convective step change of temperature, tank inflow/outflow, and radial heat flow in ar otating hollow sphere. Although the effects of upwind nodal asymmetry appear minor in the first and second problems, noticeable improvement with 4-node/8-point interpolation is observed in the rotating sphere problem. Additional reduction of CPU time due to noninverted convective upwinding is reported.

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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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

Citations9
Published2004
Admission routes2
Has abstractyes

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