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Record W2166667176 · doi:10.2514/6.2011-3854

High-Order CENO Finite-Volume Schemes for Multi-Block Unstructured Mesh

2011· article· en· W2166667176 on OpenAlex

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

Venue20th AIAA Computational Fluid Dynamics Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFinite volume methodComputer scienceBlock (permutation group theory)Volume (thermodynamics)Mesh generationOrder (exchange)Unstructured gridPolygon meshFinite element methodMathematicsEngineeringMechanicsComputational fluid dynamicsStructural engineeringComputer graphics (images)GeometryPhysics

Abstract

fetched live from OpenAlex

High-order discretization techniques remain an active area of research in Computational Fluid Dynamics (CFD) since they offer the potential to significantly reduce the computational costs necessary to obtain accurate predictions when compared to lowerorder methods. In spite of the successes to date, efficient, universally-applicable, highorder discretizations remain somewhat illusive, especially for more arbitrary unstructured meshes. A novel, high-order, Central Essentially Non Oscillatory (CENO), cell-centered, finite-volume scheme is examined for the solution of the conservation equations of inviscid, compressible, gas dynamics on multi-block unstructured meshes. This scheme was implemented for both two- and three-dimensional meshes consisting of triangular and tetrahedral computational cells, respectively. The CENO scheme is based on a hybrid solution reconstruction procedure that combines an unlimited high-order k-exact, least-squares reconstruction technique with a monotonicity preserving limited piecewise linear least-squares reconstruction algorithm. Fixed central stencils are used for both the unlimited high-order k-exact reconstruction and the limited piecewise linear reconstruction. In the proposed hybrid procedure, switching between the two reconstruction algorithms is determined by a solution smoothness indicator that indicates whether or not the solution is resolved on the computational mesh. This hybrid approach avoids the complexities associated with reconstruction on multiple stencils that other essentially non-oscillatory (ENO) and weighted ENO schemes can encounter. As such, it is well suited for solution reconstruction on unstructured mesh. The CENO scheme for unstructured mesh is described and analyzed in terms of accuracy, computational cost, and parallel performance. In particular, the accuracy of reconstructed solutions for arbitrary functions and idealized flows is investigated as a function of mesh resolution. The ability of the scheme to accurately represent solutions with smooth extrema while maintaining robustness in regions of under-resolved and/or nonsmooth solution content (i.e., solutions with shocks and discontinuities) is demonstrated for a range of problems.

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: Methods · Consensus signal: none
Teacher disagreement score0.590
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
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)0.0000.000

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 head0.024
GPT teacher head0.227
Teacher spread0.203 · 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