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Record W2252685941 · doi:10.1260/1756-8293.7.4.419

Resonant Cantilever Wings for Monolithic MAVs

2015· article· en· W2252685941 on OpenAlexaff
Matthew D. Minnick, A. D. Goussev, R. N. Kleiman

Bibliographic record

VenueInternational Journal of Micro Air Vehicles · 2015
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCantileverFlappingWingPower (physics)Mode (computer interface)Figure of meritAcousticsRangingFabricationNormal modePhysicsAerospace engineeringEngineeringOpticsComputer scienceVibration

Abstract

fetched live from OpenAlex

In this paper we present the concept, fabrication, and testing of resonant cantilever wings for monolithic micro aerial vehicles (MAVs). Combining new analytical and computational fluid dynamic work to determine the resonant mode, forces, and power of resonating curved cantilevers, we present a framework to calculate and optimize robot designs for certain figures of merit (i.e., greatest excess power, smallest size, and fastest time for a swarm to search a volume). The optimization results reveal promising designs on scales ranging from fruit flies to dragonflies with the optimal MAV having a maximum continuous travel speed of 2 m/s, 10 mm wing length, and 9 mg total mass. We then fabricate curved cantilever wings to test the theoretical model, which confirm the resonant frequency, resonant mode shape, power dissipated, and net force generated. This work is the first demonstration of asymmetric force from a symmetric flapping cycle and of the feasibility of curved cantilever wings for completely monolithic MAVs.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.338

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 head0.024
GPT teacher head0.250
Teacher spread0.226 · 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 designBench or experimental
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

Citations1
Published2015
Admission routes1
Has abstractyes

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