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Record W2324674516 · doi:10.2514/6.2006-3711

A High-Order Accurate Unstructured Newton-Krylov Solver for Inviscid Compressible Flows

2006· article· en· W2324674516 on OpenAlex
Amir Nejat, Carl Ollivier‐Gooch

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.

Bibliographic record

Venue36th AIAA Fluid Dynamics Conference and Exhibit · 2006
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeneralized minimal residual methodInviscid flowDiscretizationJacobian matrix and determinantApplied mathematicsBackward Euler methodSolverNewton's methodMathematicsRobustness (evolution)ComputationComputer scienceIterative methodMathematical optimizationAlgorithmMathematical analysisNonlinear system

Abstract

fetched live from OpenAlex

*† A Newton-Krylov unstructured flow solver is developed for higher-order computation of the Euler equations using an upwind scheme. The Generalized Minimal Residual (GMRES) algorithm is used for solving the linear system arising from implicit time discretization of the governing eqautions. An Incomplete Lower-Upper factorization technique is employed as the preconditioning strategy, and an approximate first order Jacobian as the preconditioning matrix. A proper implementation of limiter for higher-order discretization is discussed and a new formula for higher-order limiter is introduced. A defect correction procedure is used for the start-up process before performing Newton iterations. All orders of accuracy show fast convergence characteristics demonstrating the robustness of the proposed approach.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.199
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