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Record W4206361712 · doi:10.2514/6.2022-1039

Cruise Performances Improvement of the Regional Jet CRJ700 using an Adaptive Winglet

2022· article· en· W4206361712 on OpenAlex

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.

Bibliographic record

VenueAIAA SCITECH 2022 Forum · 2022
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsWingtip deviceDragCruiseAerodynamicsLift-to-drag ratioAerospace engineeringLift-induced dragDeflection (physics)ElevatorComputer scienceMarine engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2022-1039.vid This novel research evaluates how an adaptive winglet can improve an aircraft flight performance. A one-rotation axis adaptive winglet was designed and applied to the CRJ700 aircraft. Then, using a validated aerodynamic model, several winglet deflection angles were analyzed for several flight conditions of the CRJ700. The paper shows that the lift-over-drag ratio or the fitness of the aircraft was improved by up to 5.42% using an adaptive winglet for typical CRJ700 cruise conditions. Finally, it was simulated aerodynamic performance during a cruise performed at Mach number 0.75. This research shows that the adaptive winglet allows a regional aircraft such the CRJ700 to improve its cruise performance in terms of lift, drag of lift-to-drag ratio. Moreover, it has shown that the adaptive configuration allows to customize the optimization criteria in flight, which is an interesting advantage.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.384

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 head0.018
GPT teacher head0.217
Teacher spread0.199 · 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