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Record W2012667674 · doi:10.2514/6.2008-299

Lift/Drag Ratios of Aircraft with Outboard Horizontal Stabilizers

2008· article· en· W2012667674 on OpenAlexaff
J. A. C. KentŽfield

Bibliographic record

Venue46th AIAA Aerospace Sciences Meeting and Exhibit · 2008
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDragLift-induced dragMarine engineeringLift (data mining)Aerospace engineeringLift-to-drag ratioEnvironmental scienceAeronauticsGeologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Predictions are presented of the performances, in terms of lift/drag (L/D) ratios, of aircraft with outboard horizontal stabilizers. The predicted performances show significant increases in L/D ratios ranging from approximately 30% to more than 50% when compared with otherwise comparable conventional configurations. The performance prediction range covered wing aspect ratios from 4 to 10. A prime reason for the L/D improvements included profiting from a lift contribution from the horizontal stabilizer the two sections of which lie in the wing-tip upwash flows thereby offsetting, for a prescribed aircraft gross weight, some of the lift otherwise required from the wing and yet leaving a sufficient lift coefficient margin available for pitch control. Also the position of the vertical stabilizers, downwind of the wing, attached to the horizontal stabilizer support booms involved the generation of an aerodynamic lift type force, acting in the horizontal plane, which helps to offset the skin friction and induced drag of the twin vertical surfaces. The force acting on the vertical stabilizers is a consequence of these surfaces lying, when mounted above the booms, in an inwash flow generated by the combined action of the wing-tip flow field and the mainplane downwash. Lastly, due to the directions of the flows impinging upon their surfaces, the stabilizers also generate a thrust component helping to further cancel their drags.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.192
Teacher spread0.182 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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