A Numerical Study of Cavitation Inception in Complex Flow Fields

Abstract

Prediction of cavitation inception on Navy propulsors is a very challenging task that has preoccupied propulsor designers for many years. This is in fact true for predictions using scaled experimental tests as well as for predictions based on analytical/numerical modeling. Over the past few years very significant progress has been accomplished by the community in terms of both experimental measurements and numerical techniques development and their application to the problem. Novel sophisticated velocity flow field measurement techniques and their efficient practical application to propulsor studies both at Navy research centers and at other Navy funded laboratories has enabled impressive measurements of the complex flow field in details never observed before. These observations quantified mainly the space variations of the flow field using some time and space averaging. Some effort, but so far less impressive, has also illustrated the time unsteady nature of the challenging phenomena. However, additional efforts are necessary but require tremendous capabilities in data storage and analysis to provide us with time fluctuations of pressures. In parallel impressive progress in computational techniques and in computer power has enabled more and more complex and large simulations. These have included Reynolds Averaged Navier Stokes (RANS) simulation, Large Eddy Simulations (LES) and Direct Numerical Simulations (DNS) applied to larger and larger problems.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2007
Accession Number
ADA475087

Entities

People

  • Chao-tsung Hsiao
  • Georges L. Chahine
  • Jin-Keun Choi

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Acoustic Signals
  • Cameras
  • Computational Fluid Dynamics
  • Computational Science
  • Engineers
  • Equations Of Motion
  • Flow Fields
  • Flow Visualization
  • Fluid Flow
  • Hydrodynamics
  • Mechanics
  • Photographs
  • Photography
  • Pressure Measurement
  • Three Dimensional
  • Turbulent Mixing
  • Two Dimensional

Readers

  • Computational Modeling and Simulation
  • Fluid Mechanics and Fluid Dynamics.
  • Neural Network Machine Learning.

Technology Areas

  • Space
  • Space - Hall-Effect Thruster