MétaCan
Menu
Back to cohort
Record W2313770576 · doi:10.2514/6.2012-5012

A Multi-Scale Simulation Methodology for the Samarai Monocopter ?UAV

2012· article· en· W2313770576 on OpenAlexaff
Borna Obradovic, Gregory Ho, Rick Barto, Kingsley Fregene, David Sharp

Bibliographic record

VenueAIAA Modeling and Simulation Technologies Conference · 2012
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsComputer scienceScale (ratio)GeographyCartography

Abstract

fetched live from OpenAlex

The Samarai UAV is a biologically-inspired monocopter, similar in its ight characteristics to the maple seed. It is essentially a single, asymmetric helicopter blade, powered by a tip propeller. Highdelity simulation of such a vehicle is challenging, but of great importance to e cient design. A multi-scale simulation methodology is presented, utilizing Computational Fluid Dynamics (CFD)-based predictions of the aerodynamic behavior, coupled to the more CPU-e cient blade element and free-wake vortex ring models. The resulting simulation is suitable for engineering analysis and design of monocopters, but nevertheless captures the relevant physical phenomena at low Reynolds number ight regime.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.176
GPT teacher head0.332
Teacher spread0.156 · 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
GenreMethods

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

Citations15
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAIAA Modeling and Simulation Technologies ConferenceSame topicBiomimetic flight and propulsion mechanismsFrench-language works237,207