Hartmann Sensor and Dynamic Tomographical Analysis of Organized Structure in Flow Fields,

Abstract

In the Aero-optics research program currently underway at the Phillips Laboratory, we are examining the relationship between the organized structure in the near field region of an axisymmetric jet and the degradation of an optical field that propagates through the jet shear layer. We are developing an experimental capability in which dynamic tomographical analysis enables us to reconstruct a time series of flow field images while simultaneously recording the optical phase incurred along many paths through the flow. From such simultaneous information we can infer relevant flow structures. In this paper we present an analysis of the one dimensional Hartmann sensing technique with which we intend to make time resolved tomographic measurements. We discuss the sensitivity and noise characteristics of the sensor and present novel, high resolution, measurements of flow field structure that have been obtained from Hartmann sensor measurements. Additionally, computed tomographic reconstructions of flow field cross sections are created using information from the Hartmann sensor measurements. We employ these simulations to analyze the expected quality of time-resolved tomographic reconstructions and to create an optimized design of an experimental dynamic tomographic system based on Hartmann sensing.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1994
Accession Number
ADA295049

Entities

People

  • E. Chen
  • J. Wissler
  • K. Bishop
  • L. Mcmackin
  • N. Clark

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Acquisition
  • Boundary Layer
  • Convection
  • Data Acquisition
  • Detectors
  • Flow
  • Flow Fields
  • Geometry
  • High Resolution
  • Inverse Problems
  • Measurement
  • Orientation (Direction)
  • Sensitivity
  • Simulations
  • Three Dimensional
  • Turbulent Mixing
  • Two Dimensional

Fields of Study

  • Physics

Readers

  • Acoustics.
  • Computer Vision.
  • Fluid Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference