Direct Measurement of Particle Behavior in the Particle-Lagrangian Reference Frame of a Turbulent Flow - PowerPoint PPT Presentation

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Direct Measurement of Particle Behavior in the Particle-Lagrangian Reference Frame of a Turbulent Flow

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Direct Measurement of Particle Behavior in the Particle-Lagrangian Reference Frame of a Turbulent Flow James A. Bickford – PowerPoint PPT presentation

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Title: Direct Measurement of Particle Behavior in the Particle-Lagrangian Reference Frame of a Turbulent Flow


1
Direct Measurement of Particle Behavior in the
Particle-Lagrangian Reference Frame of a
Turbulent Flow
  • James A. Bickford
  • M.S.M.E. Defense
  • 10 August 1999
  • Advisor Chris Rogers (Tufts University)
  • Committee Members Vincent Manno (Tufts
    University)
  • Martin Maxey (Brown University)

2
Outline
  • Overview and Applications
  • Quasi-numerical Simulation
  • QNS Method
  • Velocity autocorrelations, spectra
  • Integral scales
  • u
  • Anomalous drift
  • Digital Particle Image Velocimetry
  • DPIV Method
  • Kolmogorov estimates
  • Effects of preferential concentration

3
Particles and Turbulence
  • Turbulent Fluid Fluctuations
  • Occur on a range of length and time scales
  • Suspended particles respond to these scales

4
Applications
  • Engine combustion, radiation and pollution
    control, volcanic erruptions
  • Aeolian Martian processes
  • Formation of planetary bodies and large scale
    structure of the universe

5
Three-tiered research approach
  • Tactical approach uses separate but complimentary
    methods
  • Microgravity flight experiments
  • Direct numerical simulations
  • Quasi-numerical simulations

6
Quasi-Numerical Overview
  • Technique
  • Hybrid numerical-experimental
  • Two-axis traverse emulates a virtual particle in
    a water flow
  • Measures turbulence statistics in the particles
    reference frame
  • Variable Parameters
  • Particle time constant
  • (size)
  • Drift velocity
  • (gravity)
  • Reynolds number
  • (turbulence intensity)
  • Data Acquisition Methods
  • Laser Doppler Velocimetry
  • Digital Particle Image Velocimetry

7
QNS Methodology
Read Fluid Velocity
Repeat gtgt Tk
Update Traverse Velocity
8
Particle Response to Turbulence
Velocity
Particle Velocity
Fluid Velocity (along particle path)
Velocity
Velocity
Time
Time
9
Movie - QNS in action
10
Effect of Gravity on Velocity Autocorrelations
  • Gravity
  • Decreases Correlation times
  • crossing trajectories effect
  • Increases relative particle energy at higher
    frequencies
  • Little effect on fluid spectra

Rii / u2
Time
11
Effect of Particle Inertia on Velocity
Autocorrelations
  • Particle Inertia
  • Increases particle correlation times
  • Almost no effect on fluid correlations or
    spectra
  • Decreases relative energy at higher frequencies

Rii / u2
Time
12
Effect of Gravity on Integral Scales
  • Gravity
  • Fluid Scales Decrease
  • Streamwise
  • Streamnormal (more)
  • Particle Scales
  • Possible decrease (tiny)
  • p-L fluid scales
  • ME pL _at_ Sg 1

T2p-L / T2me
Sg
13
Effect of Particle Inertia on Integral Scales
  • Inertia
  • General increase in fluid and particle integral
    scales
  • Possible local peaks
  • Tf 1 (particle)
  • Tf 0.7 (fluid)
  • SW more prominent

T1p-L / T1me
Stme
14
Anomalous Drift Velocities
(Measured Drift - Imposed Drift) / U
Stme
15
U Dependence on Gravity and Particle Inertia
Streamwise
Streamnormal
Uipl / Uime
Stme
Stme
16
Mechanisms Dictating Particle Behavior
  • Looking beyond single point statistics
  • Vorticity as a governing force for particle
    motion
  • Preferential concentration

17
Digital Particle Image Velocimetry
  • Four computers used during simultaneous QNS
  • Master control
  • Traverse control (DSP)
  • Frame grabber
  • Laser and camera pulse control
  • 750 mW pulsed diode laser illuminates a 2-D plane
    of the flow
  • Dichroic filter allows camera and LDV regions to
    coincide
  • Kodak ES-1 camera grabs 1008x1018 pixel images at
    30 Hz

18
Image Correlations
  • Images broken into sections (interrogation
    windows)
  • Sub-images cross-correlated to produce vector
    field

19
Bad Vector Identification
  • Bad correlations (lighting, dirt, 3-D effects)
  • Bad vectors are identified by comparing the
    velocity of a given vector to its surrounding
    neighbors.

? 2 (good)
? 2 (good)
? 8 (bad)
? 6 (bad)
20
Tagged Vector Replacement
  • Average with surrounding vectors
  • iterate to fix coincident vectors
  • inaccurate velocities
  • reduced resolution
  • Replace with higher order interpolated value
  • more accurate interpolation
  • same reduced resolution
  • Use secondary correlation peaks
  • no loss of resolution or accuracy

21
Estimation of the Kolmogorov Fluid Time Scale
  • Kolmogorov Fluid Time Scale
  • Results

22
Effect of Preferential Concentration on Particle
Path
23
Conclusion
  • Gravity and Inertia
  • Affect particle trajectory which in turns affects
  • Integral scales
  • Measured u
  • Measured vorticity
  • Observed Anomalies
  • Drift
  • Integral scale dependence

24
Acknowledgements
  • Committee Members
  • Chris Rogers
  • Vincent Manno
  • Martin Maxey
  • Staff
  • Jim Hoffmann, Vinny Maraglia
  • Audrey-Beth Stein, Joan Kern
  • TUFTL
  • Becca Macmaster, AJ Bettencourt
  • Dave McAndrew, Dan Groszmann, Scott Coppen, Jon
    Coppeta, Merre Portsmore

25
Ainley Bickford Rii Comparisons
  • Fluid Velocity Autocorrelation
  • Particle Velocity Autocorrelation
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