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Record W1599172566 · doi:10.4271/2003-01-2105

FENSAP-ICE Applications to Complete Rotorcraft Configurations

2003· article· en· W1599172566 on OpenAlexaff
Guido S. Baruzzi, Iyad Akel, Wagdi G. Habashi, J.C. Narramore

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2003
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsComputer scienceAerospace engineeringAeronauticsSystems engineeringEngineering

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">The FENSAP-ICE system was first conceived for fixed-wing aircraft and air induction system icing, but major developments are underway to augment its capabilities for icing simulation of rotorcraft and tiltrotor aircraft. A project is underway, under the auspices of the RITA (Rotorcraft Industry Technology Association) to reduce icing certification flight tests through use of second-generation three-dimensional (3D) Computational Fluid Dynamics (CFD) based technologies. The plan is to supplement traditional icing tunnel and flight-testing with modern 3D inflight icing simulation tools that facilitate the development and certification of all-weather operations rotorcraft and tilt-rotor aircraft. A viscous fully 3D ice accretion and runback modeling capability is being developed and initial correlation studies to both 2D and 3D icing test cases have produced very good results. An actuator disk model has also been developed to account for the effects of the main and tail rotors on the flow field and the subsequent changes to ice accretion patterns. The present paper will describe droplet impingement and ice accretion results in terms of increasing geometrical complexity from 2D trimmed airfoil sections, to full 3D tiltrotor aircraft in forward flight, with propellers modeled.</div>

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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 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

Citations7
Published2003
Admission routes1
Has abstractyes

Explore more

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