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Record W4283206477 · doi:10.2514/6.2022-3369

Conservative hyperbolic droplet solver for aircraft icing

2022· article· en· W4283206477 on OpenAlexaff
Maxime Blanchet, Simon Bourgault-Côté, Éric Laurendeau

Bibliographic record

VenueAIAA AVIATION 2022 Forum · 2022
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsUpwind schemeAirfoilFinite volume methodTotal variation diminishingMathematicsSolverApplied mathematicsRoe solverHyperbolic partial differential equationEulerian pathMechanicsMathematical analysisPhysicsMathematical optimizationRiemann solverDiscretizationLagrangianPartial differential equation

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2022-3369.vid This paper joins two different methods from the literature to obtain a second-order finite volume convective scheme for computing the Eulerian two-phase flows composed of air and small water droplets. The work examines the numerical solution for the various numerical instabilities as well as zero and negative values met in several numerical application cases from the literature for the water to air volume fraction. The numerical problems are due to the non-strictly hyperbolic nature of the Eulerian droplet equations along with the scheme used. A term similar to the particle pressure is proposed to modify the problem from a weakly to a strictly hyperbolic one. A modified Harten-Lax-van Leer-Contact convective scheme is used on the modified hyperbolic equations. Results with the new scheme are presented on 2D and 2.5D test problems as a verification. The proposed HLLC scheme improves on a classical upwind scheme for the single and three-element airfoil cases examined

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 score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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