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Record W1967601535 · doi:10.4271/2011-38-0073

Aircraft Performance Degradation - the Effects of Inflight Icing upon Lift, Drag and Propulsive Efficiency

2011· article· en· W1967601535 on OpenAlexaff
Anthony Brown

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2011
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNational Research Council Canada
FundersFederal Aviation AdministrationNational Aeronautics and Space Administration
KeywordsDragLift (data mining)Aerospace engineeringEnvironmental scienceLift-to-drag ratioIcingMarine engineeringAeronauticsLift-induced dragDegradation (telecommunications)Automotive engineeringEngineeringMeteorologyComputer sciencePhysicsElectrical engineering

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Data is presented from a number of flight research aircraft, which have been involved in the research of the effects of inflight icing, in a variety of atmospheric supercooled droplet and mixed-phase icing environmental conditions. The aircraft Types considered cover both Pneumatic and Thermal Ice Protection Systems (IPS). Icing includes supercooled droplet impact icing upon airframe and propeller blades and cold-soaked frost icing. The drag effects of inflight icing, from mixed-phase small and large droplets encountered during the course of SALPEX cloud physics research operations, upon a Fokker F-27 turboprop transport aircraft, have been analyzed. Furthermore, during the course of AIRS 1.5 and AIRS II inflight icing flight research operations, the NRC Convair conducted aerodynamic characterization maneuvers, following and during icing accretion in a wide range of environmental conditions of altitude, air temperature, LWC and droplet spectra. The correlation of the effects of inflight icing accretion on the drag of the NRC Convair, with full droplet spectral parameters, for which the small (FSSP) and large (2D) portions of droplet spectra have been combined, has been analyzed. The changes to profile drag, C<sub>D</sub>o, lift-dependent drag, k, and lift-curve slope, a, were analyzed initially for correlation with Ta, LWC and FSSP MVD. With the airframe IPS ON, ΔC<sub>D</sub></div><div class="htmlview paragraph"> increases did not correlate with SLW exposure. Maximum Δk and Δa degradations correlated with each other, and with air temperature. With airframe IPS OFF, maximum Δa degradations correlated with exposure to SLW, whereas maximum ΔC<sub>D</sub></div><div class="htmlview paragraph"> and Δk correlated with exposure to small droplet conditions. Furthermore, correlation of the aerodynamic parametric effects with LWC and the full-spectra droplet spectral parameters of 50VD (or MVD, when defined as median volumetric diameter), 80VD, 95VD and spectral width, approximated as maximum droplet diameter. When combined with LWC, the parameters of 95VD and maximum droplet diameter provided the better correlation of C<sub>D</sub></div><div class="htmlview paragraph">, k and a icing-induced effects, essentially limiting canyons in the contour cross-plot of effects. The analysis also highlighted the need to include the full droplet size distribution for the determination of the most detrimental icing accretion environmental regimes. The effects of icing upon propulsive efficiency have been directly measured and correlated with atmospheric conditions. Finally, the drag effects of SLD icing upon an aircraft with high lift devices deployed has been analyzed and correlated with atmospheric conditions.</div></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)
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 score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
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 head0.008
GPT teacher head0.199
Teacher spread0.192 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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