Investigation of Superradiant LDV Markers and Three-Component Velocity Mapping.

Abstract

The major progress in the first year of AROSR support has been made in the area of laser interaction with micron-size droplets flowing in a linear stream. Motivated by our interest in finding bright fluorescent markers for LDV instruments, we have continued the study of lasing droplets. Magnified photographs of lasing droplets reveal that the laser radiation is confined just within the liquid-air interface, whether the actual droplet shape is spherical or spheroidal. The possibility of providing chemical species identification of liquid droplets from the nonlinear optical spectra of droplets led us to investigate the stimulated Raman scattering, coherent anti-stokes Raman scattering, and coherent Raman mixing processes within the droplets. Progress in these areas is described. Considerable effort was devoted to developing an optical technique which has the potential of providing three-component velocity mapping in three dimensions. Double-pulse holographic experiments are currently under way with seeded particles in a cold flow. the observed particle tracks are being analyzed by a novel application of the Hough transform which is well known in the pattern recognition field. Progress in this area is also discussed.

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Document Details

Document Type
Technical Report
Publication Date
Jan 21, 1986
Accession Number
ADA173066

Entities

People

  • Richard K. Chang

Organizations

  • Yale University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Cameras
  • Classification
  • Creep
  • Elastic Scattering
  • Identification
  • Images
  • Laser Spectroscopy
  • Lasers
  • Light Sources
  • Particles
  • Pattern Recognition
  • Photographs
  • Raman Scattering
  • Recognition
  • Scattering
  • Spectra
  • Wave Mixing

Fields of Study

  • Physics

Readers

  • Aerosol Science/Aerosol Physics
  • Fluid Mechanics and Fluid Dynamics.
  • Optical Physics and Photonics.

Technology Areas

  • AI & ML
  • Directed Energy