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Record W2215754428 · doi:10.5589/q12-012

Development of a flight dynamics model of a small unmanned airship

2012· article· en· W2215754428 on OpenAlexvenueno aff
Maria Acanfora, Agostino De Marco, Leonardo Lecce

Bibliographic record

VenueCanadian aeronautics and space journal · 2012
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsnot available
Fundersnot available
KeywordsRudderFlight dynamicsAerospace engineeringMATLABEngineeringControl theory (sociology)Controller (irrigation)Control engineeringTrimState spaceLimit (mathematics)AeronauticsComputer scienceControl (management)Aerodynamics

Abstract

fetched live from OpenAlex

The scientific community has a renewed interest in airships owing to their potential applications in various tasks. The small unmanned airships are often used for inspection and environmental monitoring missions at low altitude. In this paper we introduce a study of two possible tail configurations of the unmanned airship AIUX15, arranged without ballast and ballonets and provided with an electric engine. We developed Matlab/Simulink models to simulate the dynamic behavior of two airship configurations. One of the goals of this research was to conduct an analysis of the feedback control laws for the 6-degrees-of-freedom model, which is linearized around the operational trim conditions. The controller gains were determined according to the pole-placement method. The closed-loop flight control was achieved by means of the state–space approach, to limit the oscillatory rolling motions induced by the rudder deflections.

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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.017
GPT teacher head0.174
Teacher spread0.157 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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