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Record W2312802161 · doi:10.2514/6.2004-2533

Anisotropic 3-D Mesh Adaptation for Turbulent Flows

2004· article· en· W2312802161 on OpenAlexafffund
F. Suerich-Gulick, Claude Lepage, Wagdi G. Habashi

Bibliographic record

Venue34th AIAA Fluid Dynamics Conference and Exhibit · 2004
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsMcGill University
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesMcGill University
KeywordsTurbulenceAdaptation (eye)AnisotropyComputer sciencePhysicsMechanicsOptics

Abstract

fetched live from OpenAlex

Most turbulence models require that the height of the first layer of elements in the boundary layer fall within a specific range of Y + and, in the cases where wall functions are used, that the elements be orthogonal to the wall. In this paper, a 3-D mesh adaptation module is extended to account for these requirements of the turbulence model in order to obtain a mesh more suitable for turbulent solutions on tetrahedral meshes and on hybrid unstructured meshes with layers of prisms on no-slip walls. Y + adaptation for unstructured meshes is implemented by modifying the error metric of near-wall elements to obtain an appropriate grid point distribution in the boundary layer, while providing solutionbased adaptation in the remainder of the domain. For cases where prisms are used, the height of the first prism at the wall is set to a specified Y + , again with solution-based adaptation in the remainder of the domain. For turbulence models with wall functions, a transition flag is developed to detect separation, stagnation, and recirculation zones, and to determine if Y + adaptation is locally appropriate. Results are presented that demonstrate the improvements in the meshes that result from these new functions and the corresponding improvement of the CFD solutions.

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 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: none
Teacher disagreement score0.549
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 head0.012
GPT teacher head0.209
Teacher spread0.196 · 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.

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
Published2004
Admission routes2
Has abstractyes

Explore more

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