journal article·2022-03-01Open access

The hydrodynamic effects of undulating patterns on propulsion and braking performances of long-based fin

Qiang-Qiang Hu, Yong-Liang Yu

AIP Advances· Volume 12· Issue 3

Abstract

Bio-inspired long-based undulating fin propulsion is commonly employed in biological autonomous underwater vehicles (BAUVs), while the hydrodynamic characteristics of various undulating patterns are different. To investigate what kind of undulating pattern has outstanding propulsion or braking performance for BAUVs in directional maneuvers, undulations with four basic undulating patterns are numerically examined under the Open-source Field Operation And Manipulation environment at the Reynolds number of 5 × 102, 5 × 103, and 5 × 104, corresponding to viscous, transitional, and inertial flow regimes, respectively. The study is conducted at various non-dimensional phase speeds c (0.5–2.0, normalized by incoming flow speed) at a constant maximum amplitude of 0.08 and a wavelength of 0.5 (both are normalized by the fin cord length) to imitate the long-based fin. The numerical results indicate that the undulating fin motion with the amplitude envelope gradually increasing from the anterior part to the posterior (conical sinusoidal wave) part may be preferable for thrust generation; undulating with the amplitude envelope increasing from the anterior part to the mid part and decreasing toward the posterior (fusiform sinusoidal wave) presents the superior braking performance when the phase speed is low enough. Moreover, the influence of undulating patterns on the wake structure is analyzed. Through further comparative analysis for propulsion and braking performances, the results obtained here may have instructional significance to the propulsion mechanism in bionic design.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Biomimetic flight and propulsion mechanisms · Lattice Boltzmann Simulation Studies · Micro and Nano Robotics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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