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Record W2604757092

Finite element modeling of non-equilibrium fluid-wall interaction beyond the continuum regime

2016· article· en· W2604757092 on OpenAlexaff
Marie Eve Dumas, Wagdi G. Habashi, Marco Fossati, Guido S. Baruzzi, Dario Isola

Bibliographic record

VenueStrathprints: The University of Strathclyde institutional repository (University of Strathclyde) · 2016
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsAnsys (Canada)McGill University
Fundersnot available
KeywordsFinite element methodMechanicsSlip (aerodynamics)Galerkin methodDiscretizationJumpMach numberAerodynamicsClassical mechanicsMathematicsPhysicsMathematical analysisThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

The numerical modeling of the aerodynamic interactions at high-altitudes and high-Mach numbers is considered in view of its importance when studying problems where the continuum hypothesis at the foundation of the Navier-Stokes equations becomes invalid. One of the difficulties associated with these flight conditions is that both the velocity and the temperature of the fluid do not abide by the no - slip conditions at the wall. A weak-Galerkin Finite Element formulation of the Maxwell-Smoluchowsky model is introduced to discretize the velocity slip and temperature jump conditions with better accuracy than the standard Finite Element approximation. The methodology is assessed on configurations such as cylinders and spheres for flow conditions ranging from quasi-equilibrium to non-equilibrium. Improvements are observed in the slip regime compared to available data. Nonetheless, the results in the transition regime highlight the need for more sophisticated physical modeling to address non-equilibrium at the wall.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.216
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2016
Admission routes1
Has abstractyes

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