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Record W2326500577 · doi:10.2514/6.2003-1222

FENSAP-ICE: Roughness Effects on Ice Accretion Prediction

2003· article· en· W2326500577 on OpenAlexaff
Héloïse Beaugendre, François Morency

Bibliographic record

Venue41st Aerospace Sciences Meeting and Exhibit · 2003
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsGeologySurface roughnessAccretion (finance)Surface finishSea iceSea ice growth processesSea ice thicknessMaterials scienceArctic ice packClimatologyPhysicsAstrophysicsComposite material

Abstract

fetched live from OpenAlex

FENSAP-ICE is a 3D in-flight icing simulation system, built in a modular and interlinked way to successively solve each of flow, impingement, accretion, heat loads and performance degradation via new models based on the Euler/Navier-Stokes equations for the clean and degraded flow and new partial differential equations for the other three icing processes. This paper presents a number of improvements in calibrating the roughness in the flow module, FENSAP, and the evaluation of its influence on ice shape prediction. The paper also presents new 3D results of FENSAP-ICE.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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 designBench or experimental
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

Citations12
Published2003
Admission routes1
Has abstractyes

Explore more

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