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Record W2137085099 · doi:10.2514/1.c032259

Scaling of Lift Degradation Due to Antiicing Fluids

2013· article· en· W2137085099 on OpenAlexfundno aff
Andy P. Broeren, James T. Riley

Bibliographic record

VenueJournal of Aircraft · 2013
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsnot available
FundersNational Research Council CanadaNational Aeronautics and Space Administration
KeywordsAllowance (engineering)AirplaneLift (data mining)Lift coefficientEnvironmental scienceWind tunnelAerodynamicsDragMarine engineeringAerospace engineeringEngineeringMechanicsComputer scienceMechanical engineeringPhysicsReynolds number

Abstract

fetched live from OpenAlex

In recent years, North American civil airworthiness authorities have conducted research to develop the allowance times for aircraft operations in ice-pellet precipitation. These allowance times are critical to ensure the safety and efficient operation of commercial and cargo flights. Wind-tunnel testing with uncontaminated antiicing fluids and fluids contaminated with simulated ice pellets has been carried out to better understand the flowoff characteristics and resulting aerodynamic effects. The percent lift loss was determined at an 8 deg angle of attack and used as one of the evaluation criteria in determining the allowance times. This paper describes how the lift loss was related to the loss in the maximum lift of a Boeing 737-200ADV airplane through the aerodynamic acceptance test performed for fluids qualification. A loss in the maximum lift coefficient of 5.24% on the B737-200ADV airplane (which was adopted as the threshold in the aerodynamic acceptance test) corresponds to a lift loss of 7.3% on the test model at an 8 deg angle of attack. A statistical analysis was performed to account for data scatter in the correlation and indicated that the upper limit of lift loss on the test model was 9.2%. Therefore, for cases resulting in lift loss from 7.3 to 9.2%, extra scrutiny of the visual observations is required for evaluating fluid performance with contamination and establishing appropriate allowance times.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.304

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 head0.008
GPT teacher head0.210
Teacher spread0.201 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2013
Admission routes1
Has abstractyes

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