On the Application and Interpretation of Coherent Motion Detectors

Abstract

Coherent motions are understood to play an important role in enhancing the momentum transport of turbulent wall bounded shear flows, but determining whether or not a coherent motion is present in a given volume of space at a particular time is still a difficult task. Although most detection schemes were developed for use within a specific region above the wall, most are ultimately used throughout the entire boundary layer. Three different probe based algorithms and three different visual detection schemes are applied to combined flow visualization and spanwise vorticity probe data taken in the near wall region and at y/delta of 0.8. Several new performance parameters have been developed, and they are calculated along with most of the commonly used evaluation parameters as functions of detection threshold and Reynolds number or y+. The one to one correspondence between probe based detections and visual detections is also evaluated using two parameters already in common use and a new parameter, P(T,t), which is based on the number of event overlaps as a function of time during an event inner, outer, and mixed variables are used whenever appropriate to scale all results, but only two Reynolds number independent curves are found which describe the outer region response of any detection algorithm as a function of threshold.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1991
Accession Number
ADA356739

Entities

People

  • Charles Paul Gendrich

Organizations

  • Michigan State University

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Boundary Layer
  • Cameras
  • Computer Programs
  • Data Acquisition
  • Data Sets
  • Detectors
  • Flow Visualization
  • Fluid Dynamics
  • Image Processing
  • Measurement
  • Motion Detectors
  • Probes
  • Processing Equipment
  • Reynolds Number
  • Turbulent Boundary Layer
  • Two Dimensional

Fields of Study

  • Physics

Readers

  • Fluid Mechanics and Fluid Dynamics.
  • Neural Network Machine Learning.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • Space
  • Space - Hall-Effect Thruster
  • Space - Space Objects