Skin-Friction Measurements on a Model Submarine

Abstract

Experimental skin-friction and pressure-coe cient data for a generic scale-model submarine, tested in the Low-Speed Wind Tunnel at DSTO, are presented. The e ect on skin-friction and pressure coe cients due to di erent sizes and types of boundary layer tripping devices, including the case of no tripping device, was investigated for the Reynolds number range of 3:58 106 to 6:27 106, where the Reynolds number is based on model length. Skin friction was measured using the Preston-tube method which is a technique applicable to turbulent boundary layers only. For the laminar and transition regions the Preston tube only provided qualitative results. The results demonstrate the importance of correctly tripping the boundary layer and provide a guide on determining the size and type of tripping device required to achieve a correctly stimulated turbulent boundary layer for a given tunnel free-stream velocity. While the results are speci c to the model geometry tested and for the given trip location, the methodology is applicable to other general model geometries and trip locations. This report does not address the di cult problem of where to place the trip.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2013
Accession Number
ADA599282

Entities

People

  • A. Valiyff
  • L. P. Erm
  • M. B. Jones
  • S. M. Henbest

Organizations

  • Defence Science and Technology Group

Tags

Communities of Interest

  • Advanced Electronics
  • Air Platforms
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Boundary Layer
  • Boundary Layer Transition
  • Computational Fluid Dynamics
  • Computer Programs
  • Fluid Dynamics
  • Fluid Flow
  • Fluid Mechanics
  • Free Stream
  • Geometry
  • Hydrodynamics
  • Laminar Boundary Layer
  • Measurement
  • Pressure Gradients
  • Pressure Measurement
  • Test Facilities
  • Turbulent Flow
  • Wind Tunnels

Fields of Study

  • Physics

Readers

  • Distributed Systems and Data Platform Development
  • Electrical Engineering
  • Fluid Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference