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Record W4255180489 · doi:10.32920/ryerson.14635701

Design and Analysis of a Rocket-Deployed Flying-Wing UAV

2021· preprint· en· W4255180489 on OpenAlexaffabout
Yukei Oyama

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPayload (computing)WingspanAerospace engineeringTakeoffWingWing loadingRocket (weapon)Flight control surfacesEngineeringAerodynamicsAirfoilPower (physics)Volume (thermodynamics)Automotive engineeringAeronauticsMarine engineeringAngle of attackComputer science

Abstract

fetched live from OpenAlex

This undergraduate paper demonstrates the design, analysis, and manufacturing of a rocket deployable electric powered experimental unmanned aerial vehicle. The design process begins with defining the volume and dimensions of the allocated payload space for the UAV in the rocket. These dimensions are given by the aerostructures sub team in the Ryerson Rocketry Club. The dimensions given were used to determine the best configuration for the mission. The wing loading, power loading and endurance of the UAV are obtained from the constrained payload volume in the rocket and the avionics system of the of the UAV. The wing area, UAV weight and power requirements were calculated based on the previously determined values. The power requirement determines the motor size and propeller configuration. Aerodynamics, stability, and control were based the selected airfoil and obtained wing area. After completing the design, foam, additive manufacturing, and composite layups were used to create prototypes of the UAV. These prototypes were used to iterate the aircraft and address any immediate changes. The chosen design is a foldable flying wing, once deployed from the rocket has a wingspan of 70 inches, an aspect ratio of 13.35 and a surface area of 367 in2 . A prototype was created to prove the design feasibility of the UAV. The prototype proved to function as planned, capable of gliding, powered flight, and takeoff.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.054
GPT teacher head0.292
Teacher spread0.238 · 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 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
Published2021
Admission routes2
Has abstractyes

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