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Record W2075546897 · doi:10.2514/2.3113

FENSAP-ICE's Three-Dimensional In-Flight Ice Accretion Module: ICE3D

2003· article· en· W2075546897 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Aircraft · 2003
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsIcingNacelleAerospace engineeringAccretion (finance)AerospaceIcing conditionsMeteorologyGeologyComputer scienceMechanicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Two-dimensional and quasi-three-dimensional in-flight ice accretion simulation codes have been increasingly used by the aerospace industry in the last two decades as an aid to the certification process. Such codes predict two-dimensional sectional ice shapes, which are then manufactured from a light material and attached as disposable profiles on test aircraft to investigate them for stability under icing encounters. Although efficient for calculating ice shapes on simple geometries, current codes encounter major difficulties or simply cannot simulate ice shapes on truly three-dimensional geometries such as nonaxisymetric nacelles, high-lift wings, engine intakes, or systems that combine external and internal flows. Modern computational fluid dynamics approaches may not encounter or engender these difficulties, and FENSAP-ICE is a combination of four modules forming a complete and generic in-flight icing simulation system, built in a way to solve successively impingement, ice accretion, heat loads, and performance degradation. The set of equations describing FENSAP-ICE's airflow solver, FENSAP, its impingement module, DROP3D, and its accretion module, ICE3D, are presented

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.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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