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Modeling and Hydrodynamic Characteristics of Bionic Undulate Fin Propeller Driven by Hydraulic

2010· article· en· W1824166764 on OpenAlexvenueno aff
Haijun Xu, Cun-yun Pan, Xiaojun Xu, Han Zhou

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2010
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsPropellerKinematicsFinEngineeringMechanism (biology)Hydraulic machineryProcess (computing)Marine engineeringHydraulic cylinderComputational fluid dynamicsMechanical engineeringMechanicsComputer scienceAerospace engineeringPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

The bionic undulating propeller driven by hydraulic system has different structure, kinematic and dynamic characteristics than that of the common bionic undulating propellers driven by other sources. This paper highlights firstly the structure and driving mechanism of bionic undulating propeller with a hydraulic system, and then setup its kinematic model, based on ruled-surface equation. Changing rules of dynamic mesh for bionic fin is designed based on kinetic model. Later on, the changing courses of hydrodynamic force caused by the bionic undulating fin are calculated and studied with the CFD (Computational Fluid Dynamics) method, as well as the changing characteristics of the fluid pressure field. The analysis showed that while driven by hydraulic system, the bionic propeller could produce full-baseline undulating motion, and has flexible start-up process, as well as doubled-frequency character. The bionic undulating fin driven by hydraulic system puts up flexible characters on both kinematic and dynamics. Key Words: Hydraulic Driven; Undulating Fin; Bionic Propeller; Dynamic Mesh; Hydrodynamics

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: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 head0.004
GPT teacher head0.243
Teacher spread0.239 · 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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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