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Record W2257761182 · doi:10.4271/2015-01-2566

Tailplane with Positive Camber for Reduced Elevator Hinge Moment

2015· article· en· W2257761182 on OpenAlexaff
Reuben Chandrasekharan, Nick Iarocci, Sherry Vafa, Iyad Akel

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2015
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsElevatorHingeCamber (aerodynamics)Structural engineeringMoment (physics)Computer scienceEngineeringAutomotive engineeringPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">The Learjet 85 is a business jet with an unpowered manual elevator control and is designed for a maximum dive Mach number of 0.89. During the early design, it was found that the stick force required for a 1.5g pull-up from a dive would exceed the limit set by FAA regulations. A design improvement of the tailplane was initiated, using 2D and 3D Navier-Stokes CFD codes. It was discovered that a small amount of positive camber could reduce the elevator hinge moment for the same tail download at high Mach numbers. This was the result of the stabilizer forebody carrying more of the tail download and the elevator carrying less. Consequently, the elevator hinge-moment during recovery from a high-speed dive was lower than for the original tail.</div><div class="htmlview paragraph">Horizontal tails are conventionally designed with zero or negative camber since a positive camber can have adverse effects on tail stall and drag. The tailplane sections for the Learjet 85 were tailored to minimize these adverse effects while achieving a significant reduction in elevator hinge-moment. High-speed wind-tunnel tests on a model instrumented for hinge moments, confirmed the benefits of the tailplane with positive camber. A patent has been filed for this tail design.</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)0.001
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 score0.954
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 studies0.0000.000
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 head0.018
GPT teacher head0.255
Teacher spread0.236 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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