2-D PIV Experiment by lifemate


     Team 2

                ALONSO, Diego
                 KUMAR, Kartik
                VILLAR, Jorge J.
                    RIVAS, Ana
              BRESSON, Raphael
              BOSIGER, Matthieu
The aim of this experiment is to investigate the two dimensional velocity flow field in air
seeded by fog particles.

Two dimensional PIV is a measurement technique with many applications. One of the
primary uses is in flow field visualisation. Additionally 2-D PIV provides accurate velocity
measurements in such flows. The physical principles behind this technique rely upon the
illumination and capture of seeding particles that trace out the flow field. By capturing images
in close succession and by using correlation techniques, it is possible to analyse the
displacement of the seeding particles; hence describing the motion of these particles in the
flow field.

To illuminate the seeding particles a high intensity and frequency laser is used. This laser
produces a light sheet which illuminates the seeding particles within a finite volume. A CCD
camera is used to capture the position of the seeding particles at different instances. For this
experiment commercial software from Dantec Measurement Technology is used to analyse
and visualise the flow field electronically. The seeding particles used are produced by a fog
generator. The phases involved in the entire process can be summarised as follows:
calibration, measurement, and analysis. Further details of the steps involved in these phases
are provided during the rest of this report.

Experimental Setup



                          Light                          Fog

                                                                  CCD Camera

                                  Figure 1 – 2D PIV Experimental Setup
Figure 1 illustrates the basic elements in the 2-D PIV experiment. The CCD Camera used is a
1 megapixel Dantec HiSence. The lens and filters used are from Nikon. The laser used
produces a high intensity green light sheet. In order to visualise the flow field using this light
sheet a green filter is used on the CCD camera to ensure that optical wavelengths in excess of
approximately 540 nm are allowed to pass through. This enables a large portion of
background noise caused by other light sources, for instance, to be minimised. The laser light
sheet is created by combining infrared light from two cavities using a beam combiner and
passing the bundle through a harmonic generator.

The seeding particles used are produced by a fog generator. The fog passes through a fan and
a box with a circular outlet. The camera and laser are positioned such that the flow exiting this
box is measured at close proximity. The data captured by the CCD camera is transferred to a
computer where the images are post-processed using Dantec software. The correlation
technique used is cross-correlation which makes use of the signal obtained from two images
taken with a short time delay.

The method used for this experiment is outlined in the following steps:

   1. Calibration of the experimental setup. Calibration is necessary to ensure that the
      images obtained are focussed and the scale factor necessary for processing of the
      images is obtained. Calibration can be done in a number of ways. The two ways
      considered during this experiment are: use of a calibration target and real-time
      analysis of the correlation vectors in the flow field analysed by the computer.

       The first way makes use of a calibration target, given in Figure 2.

                              Figure 2 – Captured image of the calibration target
   This calibration target is placed parallel to the light sheet and approximately in the
   middle. The calibration target consists or a grid of dots with a bigger central dot
   surrounded by four smaller dots. The distance between two dots on this calibration
   target is given as 5 mm. Images are captured of the calibration target when placed in
   the light sheet. These images are analysis by the computer and the scale factor is
   deduced by comparing the apparent distance between the dots provided by the
   perspective of the CCD camera and the actual distance of 5 mm. Before the images are
   captured, the camera is focussed such that all the dots appear sharp. This is confirmed
   by analysing the histogram of the greyscale image and ensuring that the signal peaks
   present are only black and white.

   The second calibration method that can be employed is real-time analysis of the
   correlation vectors in the direction flow field processed by the computer. Typically
   noise signals are represented by chaotic and large vectors in this direction field. By
   focussing the camera, the volume of these vectors in the direction field can be
   minimised. When perfectly focussed theoretically there should not be any of these
   noise correlation vectors present. This however in practice is difficult to ensure when
   working under tight time constraints so a level of tolerance is adopted.

2. Measurement of the flow field. Once calibration of the setup is achieved, measurement
   can commence. The laser is switched on the time delay between pulses is set. The fog
   generator is switched on and the flow field expelled from the box is captured by the
   CCD camera. The settings used by the Dantec software are provided in the results.
   The computer ensures that the laser pulse and the camera capture are synchronised.
   The delay between successive captures is automatically controlled by the software.
   The images obtained are recorded on the computer system, ready for post-processing.

3. The final step necessary is post-processing. During this phase, the images captured
   during the measurement phase are analysed. Each pair of images taken are processed
   using cross-correlation. Using cross-correlation the images are broken down into
   smaller and smaller interrogation areas until the specified size is achieved (for
   example 32x32 px). Each interrogation area is processed using statistical correlation
   techniques whereby the displacement of the seeding particles is analysed and
   compared. This allows for the visualisation of the flow field. The resulting direction
   vector field is provided in the results.

   Once the cross-correlation vectors are obtained, the next step necessary is the use of
   various filters to eliminate the “bad” vectors. These vectors are typically anomalies in
   the flow field resulting from low seeding particle concentration and general signal
   noise. The filters used are: peak filter, range filter, and moving average filter. The peak
   filter eliminates vectors by considering the ratio between the signal peak and the noise
   peaks. If this ratio drops below a certain value, then the vector is eliminated. The ratio
   used is given in the results. The range filter ensures that the magnitude of the vectors
   remains within a set range determined by qualitative analysis of the “good” vectors.
   The moving average filter filters out “bad” vectors by taking a sample set of vectors
   around one vector and calculating the mean. If the discrepancy between the actual
   vector and the average calculated by considering the surrounding vectors is greater
   than a given ratio, the central vector is either replaced by the mean or completely
   eliminated. Use of these three filters allows for a great deal of noise to be eliminated
        from the flow field, however in practice this is never perfect and a certain number of
        “bad” vectors persist. If necessary, these vectors can be removed manually, however
        this is a laborious task.

4. The above three phases are carried out for two different velocities of the fog generated.

The settings used in the Dantec software to analyse the captured images for the higher and
lower velocity are given in Table 1 and Table 2.

Table 1 – Dantec software settings for lower velocity
         Setting                                               Value
         Interrogation area                                    32x32 px
         Overlap                                               50%
         Peak Filter Signal-to-noise ratio                     1.1
         Moving Average Filter Acceptance Factor               0.1
         Moving Average Filter Averaging Area                  3x3 px
         Range Filter Length Interval                          0 – 0.9 ms-1

Table 1 – Dantec software settings for higher velocity
         Setting                                               Value
         Interrogation area                                    64x64 px
         Overlap                                               50%
         Peak Filter Signal-to-noise ratio                     2
         Moving Average Filter Acceptance Factor               0.01
         Moving Average Filter Averaging Area                  3x3 px

A sample of the results obtained is provided below:

Lower Velocity

         Figure 3 – Velocity vector field superimposed on image of seeding particles for lower velocity
Figure 4 – Velocity vector field superimposed on a scalar map for lower velocity

     Figure 5 – Rejected vectors (red) using peak filter for lower velocity
                   Figure 6 – Streamlines superimposed on a scalar map for lower velocity

Higher Velocity:

                          Figure 7 – Image of seeding particles for higher velocity
             Figure 8 – Velocity vector field superimposed on a scalar map for higher velocity

A number of conclusions can be drawn from the results obtained from the 2D PIV
experiment. For the lower velocity it can be seen that the flow field is a fairly constant and
regular stream. It can be seen through superposition that the vector field matches the locations
of the particles in the images captured by the CCD camera. Further it can be seen from the
results obtained from the peak filter that a substantial number of vectors were discarded based
on the settings chosen. This filtering allows for the anomalous correlation vectors to be
removed, and the range and moving average filters perform this function using different
principles. Application of these three vectors allows for a great number of erroneous results to
be filtered out; however this is not completely successful. To rid the final results of all the
anomalous correlation vectors additional filters and/or manual removal is required. This in
practice is a very laborious task.

For the higher velocity it is clear that the flow has more energy; hence the flow field is more
chaotic. Vorticity amongst other factors plays an important role in shaping the flow field. It is
also clear from the settings used that use of a peak filter signal-to-noise ratio of 2 results in
rejection of a very large number of correlation vectors, including many “good” vectors.
Therefore when using such filters to post-process images it is crucial that a judicious choice is
made when setting such parameters.

For confirmation of the results obtained further experiments are necessary.

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