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Record W4235373950 · doi:10.32920/ryerson.14657772

Using Presagis simulation software to model UAV aircraft in a humanitarian mission configuration

2021· preprint· en· W4235373950 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAerospace Engineering and Control Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPayload (computing)Aerospace engineeringSoftwareInertiaGraphicsFlight testAirplaneComputer scienceStability (learning theory)Longitudinal static stabilitySimulationEngineeringComputer graphics (images)PhysicsAerodynamicsOperating system

Abstract

fetched live from OpenAlex

Aircraft simulation software was used together to simulate a humanitarian variant of the MQ-9 Reaper drone as well as its longitudinal stability response upon dropping an aid payload. This project derives stability derivatives from the MQ-9 dimensions using the mass moments of inertia and approximate air- foil shape using Athena Vortex Lattice (AVL) code. The stability derivatives, aircraft properties, weights and control systems were modelled with Presagis FlightSim 14 to approximate the MQ-9 flight model. A graphics model was also built using Presagis Creator and the flight model and graphics model were unified into a virtual environment. Its longitudinal short period and phugoid responses as well as the lateral Dutch mode after dropping a 200 kg payload was recorded and analysed. The older Ryan Navion was also modelled using the same method which was used to model the MQ-9. The same dynamic responses were compared to real Navion flight test and calculated data in order to validate the aforementioned modelling method.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.852
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.041
GPT teacher head0.273
Teacher spread0.231 · 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