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Record W2145450204 · doi:10.2514/2.998

Aerodynamic Computations Using the Convective-Upstream Split-Pressure Scheme with Local Preconditioning

2000· article· en· W2145450204 on OpenAlexaff
Marian Nemec, David W. Zingg

Bibliographic record

VenueAIAA Journal · 2000
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAerodynamicsComputationMechanicsScheme (mathematics)Upstream (networking)Computational fluid dynamicsConvectionPhysicsAerospace engineeringEnvironmental scienceComputer scienceMathematicsEngineeringMathematical analysisAlgorithm

Abstract

fetched live from OpenAlex

The implementation of the convective-upstream-split-pressure (CUSP) approach to numerical dissipation is presented for an approximately factored algorithm in conjunction with time-derivative local preconditioning. An inexpensive flux limiter is used to blend the low- and high-order CUSP dissipation to capture shocks without oscillations. The resulting algorithm is applied to several subsonic and transonic turbulent aerodynamic flows and compared with results computed using the matrix dissipation scheme. Grid convergence studies are used to assess global errors. The results show the CUSP scheme to be very effective in providing good shock capturing, low numerical dissipation in boundary layers, and low numerical errors. For the flow regimes studied, accuracy is not significantly compromised when the limiter is based on the pressure variable only, leading to significant savings in computational expense. For freestream Mach numbers below 0.2, the convergence rate and accuracy of the solver are significantly improved by preconditioning the CUSP scheme. Overall, the CUSP scheme provides accuracy similar to that of matrix dissipation at a reduced computational cost.

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 score0.330
Threshold uncertainty score0.547

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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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

Citations12
Published2000
Admission routes1
Has abstractyes

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