MétaCan
Menu
Back to cohort

Structural and CFD Analysis of Unmanned Aerial Vehicle by using COMSOL Multiphysics

2021· article· en· W3198712525 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

VenueINCAS BULLETIN · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMultiphysicsComputational fluid dynamicsDragAerospace engineeringFinite element methodDrag coefficientMarine engineeringTurbulenceSize effect on structural strengthMechanical engineeringSoftwareDroneAutomotive engineeringComputer scienceEngineeringStructural engineeringMechanicsPhysics

Abstract

fetched live from OpenAlex

The purpose of this article is to reduce the structural weight and drag of an unmanned aerial vehicle (UAV) or drone while increasing its endurance. To achieve a high strength to weight ratio, Finite Element Analysis is used to study the structural strength characteristics of UAV frames. A computational fluid dynamic analysis (CFD) is performed for different angles of attack and vehicle speeds to estimate the drag coefficient using the k-e turbulence model. The analysis results show that the designed UAV vehicle has excellent performance characteristics and stability at 5° AoA and 3 m/sec. This article outlines the overall design of the unmanned aerial vehicle, which was created using the CATIA V6 platform. COMSOL 5.6 software is used for structural and CFD analysis.

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 score0.166
Threshold uncertainty score0.343

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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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