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Record W2158819489 · doi:10.1002/fld.2588

An ALE mesh movement scheme for long‐term in‐flight ice accretion

2011· article· en· W2158819489 on OpenAlexaff
Marco Fossati, Rooh A. Khurram, Wagdi G. Habashi

Bibliographic record

VenueInternational Journal for Numerical Methods in Fluids · 2011
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsIcingAirfoilMesh generationMechanicsComputer scienceGeologyGeometryFinite element methodEngineeringMathematicsMeteorologyPhysicsStructural engineering

Abstract

fetched live from OpenAlex

SUMMARY The rather irregular shapes that glaze ice may grow into while accreting over the surface of an aircraft represent a major difficulty in the numerical simulation of long periods of in‐flight icing. There is a constant need for remeshing: a wasteful procedure. In the framework of ALE formulations, a mesh movement scheme is presented, in which frame and elasticity analogies are loosely coupled. The resulting deformed mesh preserves the quality of elements, especially in the near‐wall region, where accurate prediction of heat flux and shear stresses is required. The proposed scheme handles mesh deformation in a computationally efficient manner by localizing the mesh deformation. The 2D problem of ice accretion over single and multi‐element airfoils is considered here as a numerical experiment. Experimentally measured glaze ice shapes were used to evaluate the performance of the present approach. Copyright © 2011 John Wiley & Sons, Ltd.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.066
GPT teacher head0.406
Teacher spread0.340 · 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

Citations29
Published2011
Admission routes1
Has abstractyes

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Same venueInternational Journal for Numerical Methods in FluidsSame topicIcing and De-icing TechnologiesFrench-language works237,207