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Record W1814704746 · doi:10.4271/2003-01-2092

A History of Ice Protection System Development at Sikorsky Aircraft

2003· article· en· W1814704746 on OpenAlexfundno aff
Robert J. Flemming

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2003
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsDevelopment (topology)Aerospace engineeringComputer scienceAeronauticsSystems engineeringAstrobiologyEngineeringPhysics

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Modern rotorcraft must have the capability to operate in all-weather conditions. Sikorsky Aircraft has conducted icing research and ice protection system development for helicopters over the past 58 years and the pace of that work has accelerated during the past two decades. Sikorsky participated in several helicopter icing flight tests, conducted wind tunnel tests of scale models and full-scale components, tested simulated ice shapes, and developed analytical tools for use in the design, certification, and qualification for flight in icing conditions. Engine inlets, airspeed systems, main rotor droop stops, and windshields are generally protected by thermal anti-icing systems. When rotor ice protection is required, rotors are protected with electrothermal deice systems. The UH-60A BLACK HAWK electrothermal rotor ice protection system, developed in the late 1970s, has been installed in 2400 H-60 helicopters and it remains one of the most effective rotor ice protection systems. This paper traces the history of Sikorsky icing tests, presents information on the development of the BLACK HAWK rotor ice protection system, discusses data acquired during model-scale and full-scale airfoil and rotor icing tests, describes methods used to support ice protection system development and qualification/certification, and includes the status of BLACK HAWK Growth Rotor Blade icing qualification and S-92 icing certification.</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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.014
GPT teacher head0.199
Teacher spread0.185 · 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

Citations16
Published2003
Admission routes1
Has abstractyes

Explore more

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