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Record W7019113729

Evaluation of the impact of morphing horizontal tail design of the UAS-S45 performances

2019· other· en· W7019113729 on OpenAlexaff

Bibliographic record

VenueEspace ÉTS (ETS) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsÉcole de Technologie SupérieureHôpital Notre-Dame
Fundersnot available
KeywordsMorphingThrustTrimAngle of attackAerodynamicsWingAerospaceNACA airfoil
DOInot available

Abstract

fetched live from OpenAlex

owadays, increasingly sensitive to the global warming, the aerospace industry is committed to reduce its toxic gas emissions.To take a part in this global effort, a morphing study on the horizontal tail of an Unmanned Aerial System (UAS) is here presented.This type of morphing consists in changing the shape of the horizontal tail wing during the flight in order to improve aircraft aerodynamical characteristics.The geometry of the horizontal tail was changed as function of 3 parameters: the dihedral (from -80 degrees to +80 degrees), the sweep (from -80 degrees to +80 degrees) or the twist angles (from -50 degrees to 50 degrees).To measure the impact of these types of changes on the horizontal tail, an aerodynamic study was performed using the Vortex-Lattice Method (VLM) implemented in OpenVSP software (distributed by NASA).The methodology consists, in the first place, to develop a reference model that can reproduce the aerodynamic behavior of the reference aircraft with its horizontal tail.Then, morphing models were developed based on the reference model, in which different dihedral, sweep or twist angles were changed for its horizontal tail.Finally, a model able to trim the UAS-S45 for static cruise conditions was used to compute the thrust force required to balance the aircraft for each case (original and morphing).A gain of 6% of thrust has been obtained when the aircraft was trimmed using twist angle instead of elevators deflection.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.305
Teacher spread0.268 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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