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Record W2157328224 · doi:10.2514/6.2011-3046

A Hybrid, Explicit-Implicit, Second Order in Space and Time TVD scheme for One-Dimensional Scalar Conservation Laws

2011· article· en· W2157328224 on OpenAlexafffund
Farhang Norouzi, E. Timofeev

Bibliographic record

Venue20th AIAA Computational Fluid Dynamics Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsConservation lawScalar (mathematics)Computer scienceScheme (mathematics)LawApplied mathematicsMathematicsMathematical analysisGeometryPolitical science

Abstract

fetched live from OpenAlex

A hybrid, explicit-implicit, method for hyperbolic conservation laws is proposed. The scheme is of the second order both in space and in time for both, explicit and implicit, modes on smooth solutions. In order to satisfy the Total Variation Diminishing (TVD) property and obtain monotone solutions in the presence of discontinuities, TVD limiters are applied to both spatial and temporal gradients of solution. The Lower-Upper-Symmetric-GaussSeidel (LU-SGS) approximate factorization method is used to solve the discretized and linearized governing equations. Iterations are performed at each time step to eliminate the factorization and linearization errors. The numerical results for the linear advection equation and the Burgers equation demonstrate the performance of the proposed scheme in comparison with other existing hybrid methods.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.208
Teacher spread0.192 · 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

Citations3
Published2011
Admission routes2
Has abstractyes

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