Experimental Visualization of the Near-Boundary Hydrodynamics About Fish-Like Swimming Bodies

Abstract

Experimental investigation into the near boundary flow about fish-like swimming bodies is performed up to Reynolds number 106 using laser Doppler velocimetry (LDV) and particle imaging techniques. The turbulence in the boundary layer of a waving mat and swimming robotic fish were investigated. The study of the MIT RoboTuna and a two-dimensional representation of the backbone of the robotic swimming fish was performed to further assess the implications of such motion on drag reduction. Particle Image Velocimetry (PIV) experiments with the MIT RoboTuna indicate a laminarisation of the near boundary flow for swimming cases compared with non-swimming cases along the robot body. LDV and PW experiments were performed. LDV results show the reduction of turbulence intensity, near the waving boundary, for increasing phase speed ratio Cp/U up to 1.2 m/s after which the intensities begin to increase again through Cp/U =2.0, where numerical simulations by Zhang (2000) showed separation reappearing on the back of the crests. Velocity profiles show an acceleration of the fluid beyond the inflow speed at the crest region increases with increased phase speed and no separation was present in the trough for the moving wall.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2001
Accession Number
ADA405879

Entities

People

  • Alexandra H Techet

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Space

DTIC Thesaurus Topics

  • Boundary Layer
  • Cameras
  • Computational Fluid Dynamics
  • Flow Visualization
  • Fluid Dynamics
  • Fluid Flow
  • Hydrodynamics
  • Lasers
  • Measurement
  • Mechanical Phenomena
  • Mechanical Properties
  • Mechanics
  • Photographs
  • Physics Laboratories
  • Reynolds Number
  • Turbulent Flow
  • Turbulent Mixing

Fields of Study

  • Physics

Readers

  • Aquatic Ecology
  • Fluid Mechanics and Fluid Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Autonomy
  • Directed Energy