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Record W2070041137 · doi:10.1177/1094342003017001006

Applying Parmetis to Structured Remeshing for Industrial CFD Applications

2003· article· en· W2070041137 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe International Journal of High Performance Computing Applications · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsCompute CanadaPolytechnique Montréal
FundersCentres de Recerca de CatalunyaPolytechnique MontréalUniversité Laval
KeywordsComputer scienceSmoothingDomain decomposition methodsAdaptation (eye)Context (archaeology)Parallel computingComputational fluid dynamicsComputational scienceDomain (mathematical analysis)ComputationClass (philosophy)AlgorithmFinite element methodMathematicsArtificial intelligenceAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

This paper presents the current strategy used in IP-OO RT, an ongoing project to extend the application domain of a C++ toolkit library for iterative mesh adaptation. OO RT is a class library for sequential structured, unstructured and hybrid mesh adaptation used mainly in the context of CFD computations, that performs iterative mesh refinement, coarsening and smoothing in 3D. Extensions to parallelize mesh adaptation using ParMeTiS for domain decomposition and MPI high-level communication schemes are investigated here. Numerical simulations on realistic cases show that the parallel strategy scales with problem size and the number of processors, but singular behaviors are sometimes encountered at subdomain interfaces when conflicting instructions collide.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.561
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.039
GPT teacher head0.322
Teacher spread0.283 · 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