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MECHANISM DESIGN, DYNAMICS MODELLING AND EXPERIMENTS OF BIONIC UNDULATING FINS

2016· article· en· W2319558462 on OpenAlexvenueno aff
Han Zhou, Lincheng Shen, Dong Yin

Bibliographic record

VenueInternational Journal of Robotics and Automation · 2016
Typearticle
Languageen
FieldEngineering
TopicMechanics and Biomechanics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMechanism (biology)Dynamics (music)Computer scienceMechanicsPhysicsAcoustics

Abstract

fetched live from OpenAlex

The undulating fin propulsion is inspired by fish using Median and/or Paired Fin (MPF) mode. This mode provides advantages of the vector thrust production and small disturbance to the ambient flow field, and also it could be applied on underwater robots conveniently. Two bionic undulating fins are designed to imitate the structure and function of undulating fin of aquatic animals, which are fixed-waveform mode and independently-driven mode. The active deformation of bionic undulating fins is described by a kinematic model. Base on the kinematic model, a simplified computational model is derived theoretically to analyse the dynamics of the bionic propulsor. The dynamic model considers six components of forces and moments. The dynamics performance related to the geometric parameters, undulating parameters as well as the carrier velocity are further discussed through simulation. Furthermore, the above analytic method is verified through the thrust/moment and velocity test using the bionic propulsor.

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: Bench or experimental · Consensus signal: none
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 science0.0010.000
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 head0.027
GPT teacher head0.242
Teacher spread0.216 · 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 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

Citations7
Published2016
Admission routes1
Has abstractyes

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