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Record W4392910983 · doi:10.32920/25412575.v1

The Effect of Rotors on the Aerodynamic Loads of a Quadrotor Fuselage

2024· preprint· en· W4392910983 on OpenAlexaff
Shengxi Li

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAerospace Engineering and Control Systems
Canadian institutionsToronto Metropolitan UniversityUniversity of Alberta
Fundersnot available
KeywordsFuselageQuadcopterAerodynamicsAerospace engineeringLift coefficientRotor (electric)Lift (data mining)AirfoilComputationLift-to-drag ratioEngineeringStructural engineeringControl theory (sociology)Computer scienceMechanicsPhysicsMechanical engineeringControl (management)

Abstract

fetched live from OpenAlex

Understanding the complex interactions between rotors and the fuselage is essential for designing the flight control system for the Unmanned Aerial Vehicle. This paper investigates the effect of rotors on the aerodynamic load of a quadcopter fuselage based on computational fluid dynamics simulations. Fuselage and the whole drone’s aerodynamics loads are studied along with the steady and transient-state simulation. Comparison between the plain fuselage and whole quadcopter was based on the computation results from different operating airspeeds and tilt angles. Drag coefficient was merely influenced, and in contrast, the rotors’ motion influenced the lift coefficient. Reasons for the impact of the rotor on the fuselage’s aerodynamic loads are discussed. Future work improvement and concluding of the analysis are presented.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.002
GPT teacher head0.181
Teacher spread0.178 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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