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Record W2997468069 · doi:10.2514/6.2020-0338

Automatic adaptive remeshing for unsteady interfacial flows with surface tension

2020· article· en· W2997468069 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

VenueAIAA Scitech 2020 Forum · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceEulerian pathContext (archaeology)Computational fluid dynamicsGridFinite element methodInterface (matter)Surface (topology)AlgorithmComputational scienceMechanicsApplied mathematicsMathematicsGeometryPhysicsEngineeringBubbleParallel computingStructural engineering

Abstract

fetched live from OpenAlex

We demonstrate the capabilities of an automatic, adaptive, local or global, remeshing algorithm developed for flows with interfaces and/or moving boundaries by using it in the context of two-dimensional unsteady interfacial flows with surface tension. Our formulation solves the Navier-Stokes equations using the finite element method, a front-tracking approach to represent the interface and an Arbitrary Lagragian-Eulerian formulation coupled with a pseudo-solid approach to handle the deformation of the underlying computational grid. Verification and validation cases are run to ensure that the code correctly implements the right equations for the problems at hand and that physically-meaningful results can be obtained with the models and methods used. The capabilities of the algorithm in handling large-scale displacements and deformations of the fluid-fluid interfaces and its ability to efficiently and accurately capture the small-scale, high-frequency phenomena that appear in this class of problems is investigated on two problems which feature Rayleigh-Taylor and Kelvin-Helmholtz instabilities.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.796

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 head0.013
GPT teacher head0.200
Teacher spread0.187 · 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