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Record W4384200709 · doi:10.1504/ijmpt.2023.132189

Optimisation of low-weight cargo UAV with real-time controller by CAD design, FEM simulation and dynamic modelling

2023· article· en· W4384200709 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

VenueInternational Journal of Materials and Product Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Control Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCADFinite element methodController (irrigation)EngineeringComputer scienceSimulationEngineering drawingStructural engineering

Abstract

fetched live from OpenAlex

In this article, we present a novel approach to designing and optimising unmanned aerial vehicles (UAVs) to carry low-weight cargo. Various computational design techniques are involved, including the computer-aided design (CAD) of the aircraft's mechanical components and the simulation of its structural and material properties by finite elements methods (FEMs). Mathematical models were also used to describe and improve the rotor-dynamic stability, control, and weight-carrying capacity of the UAV. Based on these, an all-aluminum UAV with a real-time controller was prototyped and test-flown severally with payloads of different weights. Results show that our UAV system is optimal and aerodynamically efficient for low-weight cargo deployment. Additional testing demonstrates the energy-efficiency and suitability of our UAV for logistical, remote sensing, and agricultural applications.

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 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: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.341

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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