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Record W2996913467 · doi:10.2514/6.2020-0085

A local adaptive remeshing procedure for unsteady incompressible viscous flows

2020· article· en· W2996913467 on OpenAlex
Étienne Muller, Yohann Vautrin, Dominique Pelletier, André Garon

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

VenueAIAA Scitech 2020 Forum · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceAdaptive mesh refinementMesh generationFinite element methodContext (archaeology)Process (computing)Domain (mathematical analysis)Mathematical optimizationAlgorithmCompressibilityComputational scienceMathematicsMechanicsMathematical analysisStructural engineeringEngineeringGeologyPhysics

Abstract

fetched live from OpenAlex

This work highlights a new mesh adaption procedure which takes action locally. The procedure is specially designed for the simulation of unsteady flows. The methodology is explained in a two-dimensional context but could be extended to tackle three-dimensional problems. This approach is intended to be an interesting alternative to techniques based on local mesh subdivision or fusion. The method uses the gradient recovery technique of Zhu and Zienkiewicz to estimate the spatial error, and an advancing front meshing tool to mesh the computational domain. The elements removed from the mesh, denoted seeds, are identified by their size variation predicted by the mesh adaption method. The mesh updates are triggered by several stopping criteria which also suspend the time-integration. The process is therefore completely automatic. The work presented here was carried out within the framework of the finite element method.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.366
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.287
Teacher spread0.258 · 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