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Record W2131925584 · doi:10.2514/6.2009-4128

FENSAP-ICE: Modeling of Water Droplets and Ice Crystals

2009· article· en· W2131925584 on OpenAlex
Shezad Nilamdeen, Wagdi G. Habashi, Martin S. Aubé, Guido S. Baruzzi

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsMcGill University
FundersMcGill University
KeywordsIcingTurbofanIce crystalsMeteorologyEnvironmental scienceSea iceSea ice growth processesWater iceGeologySea ice thicknessAtmospheric sciencesArctic ice packClimatologyAerospace engineeringEngineeringPhysicsAstrobiology

Abstract

fetched live from OpenAlex

A series of high-altitude turbofan engine malfunctions characterized by flameouts and rollbacks have been reported in recent years. The source of these incidents has been related to the presence of ice crystals at high altitudes and, consequently, to a dangerous ice build-up engine components found upstream. Ice can then shed and may cause mechanical damage and performance losses to downstream components. In order to provide a numerical tool to analyze such situations, FENSAP-ICE has been extended to model mixed-phase flows that combine air, water and ice crystals, and the related ice accretion. The approach is validated against test results from the Cox & Co. Icing Tunnel.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.236

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 integrity0.0000.000
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 head0.011
GPT teacher head0.209
Teacher spread0.197 · 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

Quick stats

Citations26
Published2009
Admission routes2
Has abstractyes

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