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MECHANISM DESIGN, DYNAMICS MODELLING AND EXPERIMENTS OF BIONIC UNDULATING FINS

2016· article· en· W2319558462 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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: none
Teacher disagreement score0.839
Threshold uncertainty score0.195

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 head0.027
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