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Record W2335327437 · doi:10.2514/6.2014-0382

Fully-coupled 6 DoF Model for Unmanned Version of the SA160 General Aviation Aircraft

2014· article· en· W2335327437 on OpenAlexaff
Mikaël Cardinal, Patrice Masson

Bibliographic record

VenueAIAA Atmospheric Flight Mechanics Conference · 2014
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAeronauticsAerospace engineeringAviationComputer scienceGeneral aviationRemotely operated underwater vehicleSystems engineeringEngineeringArtificial intelligenceMobile robotRobot

Abstract

fetched live from OpenAlex

With the growing use of Unmanned Aerial Vehicle (UAV) in civilian airspace, design and development of this type of aircraft requires a thorough study of its dynamics in order to demonstrate airworthiness of such a system. This paper describes the first step in the development of an unmanned version of the SA160 aircraft by presenting a fully-coupled 6 DoF model of the SA160 aircraft. As the aerodynamic parameters are evaluated with inflight data using the SIDPAC (System Identification Program for AirCraft) software, the problem addressed in this paper is the validation of a predictive model for the dynamic response of the SA160 based on identified parameters. In this work, a state-space representation is used for the dynamic modeling of the SA160 single-engine aircraft. The a priori unknown aerodynamic coefficients are first estimated using a Digital Datcom (U.S. Air Force Digital Data Compendium) model. These coefficients are used as initial estimates for the output-error method. The effect of the rotating propeller on the aircraft dynamics is included in order to account for aerodynamic and inertial coupling. The dynamic model is based on the small-disturbance theory, so that aircraft motion is simulated around equilibrium flight conditions. Furthermore, the dynamic model elaborated in this paper is based on dimensionless linearized equations. The main goal is to demonstrate that the statespace model parameters identified through flight test program provide reliable and accurate dynamic model. System identification techniques are then used with in-flight data from an instrumented aircraft. Instrumentation includes an air data boom and an inertial measurement unit fitted to the SA160 in order to get a full set of in-flight aerodynamic and 6 DoF dynamic data. The flight control surfaces deflection is also measured with linear position measurements sensors. The inputs applied to flight control surfaces (elevator, ailerons and rudder) are designed such as to excite the aircraft in order to provide sufficiently rich data quality for modeling. Results from the model with identified SA160 aerodynamic parameters are compared to flight test data. Different sets of longitudinal, lateral/directional and fully-coupled maneuvers are performed for specific flight conditions. In the system identification process, aerodynamic parameters identified from longitudinal and lateral/directional dynamics are used as inputs for the output-error method applied for the fully-coupled case, and the amount of aerodynamic coupling is addressed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2014
Admission routes1
Has abstractyes

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