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Flow Visualization Techniques Experimental Methods in Energy and Environment Miguel Rosa Oliveira Panão IST 2003 Courtesy of A.S.Moita.

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Presentation on theme: "Flow Visualization Techniques Experimental Methods in Energy and Environment Miguel Rosa Oliveira Panão IST 2003 Courtesy of A.S.Moita."— Presentation transcript:

1 Flow Visualization Techniques Experimental Methods in Energy and Environment Miguel Rosa Oliveira Panão IST 2003 Courtesy of A.S.Moita

2 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

3 HOW TO OBTAIN A CLEAR IMAGE OF THE FLOW FIELD ? Tracers Hydrogen Bubbles Tufts Smoke Dyes Optical methods particles Refractive index Polarization density

4 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

5 EXAMPLES Dyes Tufts

6 HOW TO OBTAIN A CLEAR IMAGE OF THE FLOW FIELD ? Hydrogen Bubbles Tufts Smoke Dyes Refractive index Optical methods particles Polarization density Tracers

7 HOW TO MAKE SURE TRACERS FOLLOW THE FLOW ? Tracer Response Time Equation of Motion for a spherical tracer For low Re numbers (Stokes flow) the tracer response time is If F is a time characteristic of the flow field example Flow through a Venturi U DTDT With St p as the Stokes number St p << 1 Tracer follows the flow C. Crowe, M. Sommerfeld and Y. Tsuji, Miltiphase Flows with Droplets and Particles, pp. 22, CRC Press, 1998

8 HOW TO CAPTURE AN IMAGE ? Light Source Test Section Image Recorder Laser Light Sheet Cilindrical lens Plano-convex Lens Light dispersed by particles through Mie Scattering LASER Shadowgraphy Magnifying lens Visualizes the second spatial derivative field of the refractive index Spot Light Illumination The light intensity is proportional to L -3 Background Continuous or Pulsed

9 HOW TO CAPTURE AN IMAGE ? Light Source Test Section Image Recorder FLOW FIELD Spray impact onto a solid surface with Cross-flow Single droplet impact onto a solid surface

10 HOW TO CAPTURE AN IMAGE ? Light Source Test Section Image Recorder 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.)

11 HOW TO CAPTURE AN IMAGE ? Light Source Test Section Image Recorder Resolution = L/pixel PIXEL L Exposure Time shortlong Aperture openingclosing Frame Rate D = 3 mm U = 3 m/s T c = s FR = 2(1/T c ) = 2000FPS Nyquist IMPORTANT Space-time scale factors of Flow field L – Characteristic length scale

12 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 , 2000

13 Quantify Deformation Process Spread and finger formation Dimensionless time Spread Factor (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.

14 Flow visualization compared with local probe measurements LDA measurements Velocity and Vorticity fields 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, , 2001, Vol. 8, Kozlov V.V. (Ed.), Institute of Theoretical and Applied Mechanics SB RAS, Novosibirsk, 2001, pp 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 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, August, 2003, Toulouse, France.

15 Extracting quantitative information by image processing Leonardos 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, Leonardos Vision of flow visualization, Exp. Fluids, vol.33, pp , 2002

16 Extracting quantitative information by image processing Temperture 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

17 Extracting quantitative information by image processing Measurements 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 , 2002.

18 Extracting quantitative information by image processing Measurements of radical concentrations with tomography Steady Unsteady Burner A U j =15.6 m/s U p =3.5 m/s = 6 UpUp UjUj Courtesy of Prof. Edgar Fernandes

19 Extracting quantitative information by image processing Measurements of radical concentrations with tomography UpUp UjUj air entrain ment UHC Radical * Courtesy of Prof. Edgar Fernandes

20 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

21 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

22 B G 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 ImageColor 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:

23 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. 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 – aperture t – exposure time G – electronic gain I. Carvalho, Atomização de Líquidos em escoamentos Turbulentos com e sem Recirculação, PhD Thesis, 1995 halo

24 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 Threshold Value Gray Level Histogram Depends on calibration process Background Particles

25 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

26 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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