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Record W413799172

Development of a collision force model for ice impact within a level set method

2013· preprint· en· W413799172 on OpenAlexaff
François Morency, Héloïse Beaugendre

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2013
Typepreprint
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsCollisionTrajectoryMechanicsIcingSpring (device)CylinderBoundary (topology)Surface (topology)Work (physics)GeologyComputer sciencePhysicsStructural engineeringEngineeringGeometryMeteorologyMathematicsMathematical analysisMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

De-icing systems remove ice formed on aircraft protected surfaces according to a periodic cycle by reducing the adhesive shear strength between ice and surface. In the present work, an immersed boundary method is used to predict ice-shedding trajectories and impact. At the start of shedding trajectory calculations, when the ice-shed particle is still close to the wing surface, there is a high probability of collision with the surface. A linear spring collision model is presented assuming the interacting bodies are connected tangentially and normally by linear springs. The verification of the model is done with results from literature and study of trajectories around a cylinder.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 science0.0010.001
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 head0.050
GPT teacher head0.288
Teacher spread0.238 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2013
Admission routes1
Has abstractyes

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