Flow VisualizationTechniques
Experimental Methods inEnergy and Environment
Miguel Rosa Oliveira PanãoIST 2003
Courtesy of A.S.Moita
WHY VISUALIZE A FLOW ?
Definition:“Flow visualization is the art and science of
obtaining a clear image of a physical flow field and the ability to capture it on sketch,
photograph, or other video storage device for display or further processing.”
P. Freymuth, Flow visualization in fluid mechanics, Rev. Sci. Instrum., 64(1), Jan, 1993
Obtain clear image of flow field
Ability to
capture image
Display and
further process
Why ...“Flow visualization aims at the discovery,
description and parametric investigation of new flow phenomena and at the educational
presentation of established ones.”
P. Freymuth, Flow visualization in fluid mechanics, Rev. Sci. Instrum., 64(1), Jan, 1993
HOW TO OBTAIN A CLEAR IMAGEOF THE FLOW FIELD ?
Tracers
HydrogenBubbles Tufts
Smoke
Dyes
Optical method
s
particles
Refractive index
Polarization density
EXAMPLES
Hydrogen Bubbles
Particles
K. Kerenyi, S. Stein and J. S. Jones, Advanced flow visualization techniques for the Federal Highway Administration Hydraulics Research Laboratory, ASCE 2001
Smoke
Courtesy of Sergei I. Shtork, PhD
EXAMPLES
Dyes
Tufts
HOW TO OBTAIN A CLEAR IMAGEOF THE FLOW FIELD ?
HydrogenBubbles Tufts
Smoke
Dyes
Refractive index
Optical method
s
particles
Polarization density
Tracers
HOW TO MAKE SURE TRACERS FOLLOW THE FLOW ?
Tracer Response Time
Equation of Motion for a spherical tracer
vuvu4D
C21
dtdv
m c
2
D
For low Re numbers(Stokes flow) the tracer response time is
c
2p
p 18
D
If F is a time characteristic ofthe flow field example
Flow through a Venturi
U
DT
UDT
F
With Stp as the Stokes number
F
ppSt
Stp << 1
Tracer follows the
flowC. Crowe, M. Sommerfeld and Y. Tsuji, Miltiphase Flows with Droplets and Particles, pp. 22, CRC Press, 1998
HOW TO CAPTURE AN IMAGE ?
LightSourc
e
TestSectio
n
ImageRecord
er
LaserLightSheet
Cilindrical lens
Plano-convexLens
Light dispersed by particles through Mie Scattering
LASER
Shadowgraphy
Magnifying lensVisualizes the second spatial derivative field of the refractive index
Spot LightIllumination
The light intensity is proportional to L-3
Background
Continuousor
Pulsed
HOW TO CAPTURE AN IMAGE ?
LightSourc
e
TestSectio
n
ImageRecord
er
FLOW FIELD
Spray impact onto a solid surface with Cross-flow
Single droplet impact onto a solid surface
HOW TO CAPTURE AN IMAGE ?
LightSourc
e
TestSectio
n
ImageRecord
er
Images are recorded with a camera and its type is chosen depending on the flow characteristics.
Digital or Film Camera
High speed
CCD camera
Important Features:• Resolution, (n x n) pixel• Exposure time• Aperture• Frame rate (for HS cam.)
HOW TO CAPTURE AN IMAGE ?
LightSourc
e
TestSectio
n
ImageRecord
er
Resolution = L/pixel
PIXEL
L
Exposure Time
short long
Aperture
opening
closing
Frame Rate
U
D = 3 mmU = 3 m/sTc = 0.001 sFR = 2(1/Tc) = 2000FPSNyquist
IMPORTANTSpace-time
scale factors ofFlow field
L – Characteristic length scale
Why ...“Flow visualization aims at the discovery,
description and parametric investigation of new flow phenomena and at the educational
presentation of established ones.”
P. Freymuth, Flow visualization in fluid mechanics, Rev. Sci. Instrum., 64(1), Jan, 1993
The visualization of a flow field allows to identify the large and small structures existing in a flow and futher to compare with local probe measurements, or CFD.
Identify Flow Structures
Spray impact on solid surface with cross-flow
• Upstream wall-jet vortex;• Droplet cloud over the surface after impact;• Turbulent boundary layer;• Fuel vaporization upon impact.M.Panão and A. Moreira, Visualization and Analysis of Spray Impingement Under Cross-Flow Conditions, SAE Technical Paper 2002-01-2664, 2000
Quantify Deformation Process
Spread and finger formation
d(t)
h(t)
Dimensionless time0
0*
DUt
t
Spread Factor
0
**
Dtd
t
0
0.5
1
1.5
2
2.5
3
3.5
4
0 5 10 15
t*
U0=0.22m/ sU0=0.44m/ s
U0=2.47m/ s
0
0.5
1
1.5
2
2.5
3
3.5
4
0 5 10 15
t*
U0=0.22m/ sU0=0.44m/ s
U0=2.47m/ s
U0=0.22m/ sU0=0.44m/ s
U0=2.47m/ s
(t*
)
A. S. Moita and A. Moreira,The Deformation of Single Droplets Impacting onto a Flat Surface, SAE 2002 Transactions Journal of Fuels and Lubricants 1477 – 1489, 2002.
Flow visualization compared with local probe measurementsLDA measurements Velocity and Vorticity
fields
x/d
y/d
-0.5 0 0.5-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
2.42.091.781.471.160.850.540.23-0.08-0.39-0.7
x'
x'
U/U03 U0
x/d
y/d
-0.5 0 0.5-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8543210-1-2-3-4-5-6-7-8
zd/4U0
1. Anacleto P.M., Fernandes E.C., Heitor M.V., Shtork S.I. Characteristics of precessing vortex core in the LPP combustor model. Abs. to the Int. Conf. On Stability and Turbulence of Homogeneous and Heterogeneous Flows, Novosibirsk, April, 25 - 27, 2001, Vol. 8, Kozlov V.V. (Ed.), Institute of Theoretical and Applied Mechanics SB RAS, Novosibirsk, 2001, pp. 14-15.
2. Anacleto P.M., Fernandes E.C., Heitor M.V., Shtork S.I. Characteristics of precessing vortex core in the LPP combustor model. Proc. Second International Symposium on Turbulence and Shear Flow Phenomena, June 27-29, 2001, Stockholm, Sweden. Lindborg E. et al. (Eds.), KTH, Stockholm, 2001, Vol. 1, pp. 133-138.
3. Cala C.E., Fernandes E.C., Heitor M.V., Shtork S.I. Characterization of unsteady swirling flow based on phase averaging of pressure and LDA probe signals. Presented at the 5th Euromech Fluid Mechanics Conference, EFMC-5, 24-28 August, 2003, Toulouse, France.
Q air
PVC
Q air
PVC
Extracting quantitative information by image processingLeonardo’s Vision of Flow in the Aortic
Track
Flow visualization using particles
Digital Particle Image Velocimetry
M. Gharib, D. Kremers, M.M. Koochesfahani and M. Kemp, Leonardo’s Vision of flow visualization, Exp. Fluids, vol.33, pp. 219-223, 2002
Extracting quantitative information by image processingTemperture distribution using a
thermographic camera
Temperature map of an aluminum plate, heated by an electric resistance, to show the uniformity degree of the heating
process.
Courtesy of Humberto Loureiro
139,0°C
224,3°C
140
160
180
200
220
Extracting quantitative information by image processingMeasurements of NO-molecule excitation
LIF images for different ammonia seeding concentrations
N. Sullivan, A. D. Jensen, P. Glarborg, M. S. Day, J. F. Grcar, J. B. Bell, C. J. Pope, and R. J. Kee, Ammonia Conversion and NOx Formation in Laminar Coflowing Nonpremixed Methane-Air Flame", Combustion and Flame 131(3), pp. 285-298, 2002.
Extracting quantitative information by image processingMeasurements of radical concentrations
with tomography
“Steady” “Unsteady”
Burner AUj=15.6 m/sUp=3.5 m/s = 6
UpUj
Courtesy of Prof. Edgar Fernandes
Extracting quantitative information by image processingMeasurements of radical concentrations
with tomography0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
r / R
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
z/R
Up
Uj
air
entr
ain
me
nt
UHC
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
r / R
0.0
0.2
0.4
0.6
0.8
1.0
z/R
2
4
6
8
10
12
14
16
UpUj UpUj
Radical <C2>*
Courtesy of Prof. Edgar Fernandes
Flow visualization compared with CFD
Multipoint Fuel Systems
C.X. Bai, H. Rusche and A.D. Gosman, Modeling of gasoline spray impingement, Atomization and Sprays, vol. 12, pp. 1-27, 2002
Turbulent Premixed Flames
WHEN IS SEEING BELIEVING ?
• Image out of focus• Incorrect light intensity• Incorrect imaging angle• Low SNR (Signal to Noise Ratio)• Too large exposure time• Improper synchronization system
Some problems associated with image processing
Courtesy of Leonardo Da Vinci
BG
HOW TO PROCESS AN IMAGE ?
An image is nothing more than a matrix.
What is an image?
3 x 3
3 x 3 x
3
Intensity Image Color Image
R
= R + G + B,in this case
There are two levels:1. the Graylevel
and;2. the RGB level.
0 1 or 255
Each pixel has a value between 0 and 1.
Image Processing Toolbox, MATLAB
Example:
HOW TO PROCESS AN IMAGE ?
Calibration Process
• To have a scale factor (Length/pixel).• To explore the major effects influencing measurement accuracy.
In Focus Out of Focus
Example: Illuminated hole in back lighting.
0
50
100
150
200
250
Gra
y L
evel
In Carvalho (1995), the following parameters were evaluated: Background Gray Level (BGL), r.m.s., standard deviation, SNR, average gray level along the hole.
The analysis was applied to the measurement of liquid film breakup lengths and the following empirical expression was derived:
f – aperturet – exposure timeG – electronic gain
9.023 G9.0exptf8.2102.57SNRBGL
I. Carvalho, Atomização de Líquidos em escoamentos Turbulentos com e sem Recirculação, PhD Thesis, 1995
halo
HOW TO PROCESS AN IMAGE ?
Particle Identification – boundary detection
• The general procedure is to separate the particle and the background.• This is done though boundary detection algorithms.
Gray Level Threshold
Portions with the gray level lower than a threshold value are counted as particles.
Methods
LBOM
LBLT
GGGG
C
Threshold Value
Gray Level Histogram
Depends on calibration
processB
ackg
rou
nd
Particles
HOW TO PROCESS AN IMAGE ?
Particle Identification – boundary detection
• The general procedure is to separate the particle and the background.• This is done though boundary detection algorithms.
Gray Level Gradient
Based on the assumption that the gray-level variation is the steepest at the particle boundaries.
Appropriate
threshold
S. Y. Lee and Y. D. Kim, Sizing of Sprays Particles using Image Processing Technique, 9th ICLASS, 2003
SUMMARY“Flow visualization has a long history.”
P. Freymuth, Flow visualization in fluid mechanics, Rev. Sci. Instrum., 64(1), Jan, 1993
• Flow visualization consists of:
obtaining a clear image; capture the image; process the image. • There are several techniques for flow
visualization: Laser light sheet; Shadowgraphy, Schlieren, interferometry; Flash or spot illumination.
• The image processing can be made to provide accurate information of the flow. The first steps and techniques are:
o Calibration; o Boundary detection algorithms;o Techniques: Particle Image Velocimetry (PIV); tomography; Particle Tracking Velocimetry (PTV); LIF; thermography; Exciplex ...
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