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Record W2167346371 · doi:10.2514/1.j051200

Eddy-Preserving Limiter for Unsteady Subsonic Flows

2012· article· en· W2167346371 on OpenAlexafffund
Kaveh Mohamed, Siva Nadarajah, Marius Paraschivoiu

Bibliographic record

VenueAIAA Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsConcordia UniversityMcGill University
FundersCompute CanadaUniversité de SherbrookeUniversité Laval
KeywordsVortexDissipationDiscretizationAirfoilLift coefficientMechanicsInterpolation (computer graphics)Lift (data mining)Large eddy simulationMathematicsConservation lawVortex sheddingPhysicsClassical mechanicsReynolds numberTurbulenceMathematical analysisComputer scienceMotion (physics)

Abstract

fetched live from OpenAlex

A slope-limiting algorithm for second-order monotone upstream-centered schemes for conservation laws is introduced to reduce the dissipation of vortices in flow simulations. The algorithm is based on the reconstruction of velocity components along the principle axes of the vortex and the augmentation of the central gradients’ weight for the interpolation of velocity components on the swirl plane of the vortex. The performance of the scheme in different vortical flow problems is investigated. The proposed limiting algorithm has been able to considerably reduce the dissipation of vortices provided that the spatial and temporal discretization of the problem have been fine enough to resolve the length and time scales of the corresponding vortical motion. In particular, the scheme has significantly outperformedthe conventionalvan Albadalimiter to resolvethesecond peak inthe lift coefficient spectrainthe case of a NACA0021 airfoil at a poststall condition.

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 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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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 teacher head, 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

Citations5
Published2012
Admission routes2
Has abstractyes

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