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Record W2668356334 · doi:10.2514/1.c034211

Finite Element Modeling of Nonequilibrium Fluid–Wall Interaction at High-Mach Regime

2017· article· en· W2668356334 on OpenAlexafffund
Marie-Eve Dumas, Wagdi G. Habashi, Guido S. Baruzzi, Dario Isola, Marco Fossati

Bibliographic record

VenueJournal of Aircraft · 2017
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsAvensys (Canada)McGill UniversityAnsys (Canada)Université de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCompute CanadaLockheed Martin Corporation
KeywordsMach numberMechanicsFinite element methodSlip (aerodynamics)PhysicsAerodynamicsGalerkin methodDiscretizationClassical mechanicsNon-equilibrium thermodynamicsJumpMathematicsMathematical analysisThermodynamics

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–Smoluchowki 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 nonequilibrium. Improvements are observed in the slip regime compared with available data. Nonetheless, the results in the transition regime highlight the need for more sophisticated physical modeling to address nonequilibrium 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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.449

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 science0.0000.000
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 head0.035
GPT teacher head0.303
Teacher spread0.268 · 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.

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

Citations4
Published2017
Admission routes2
Has abstractyes

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