Particle image velocimetry (PIV) - Revision history

Kordula NTNU at 07:02, 30 September 2020

2020-09-30T07:02:19Z

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2020-06-01T12:40:54Z

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2020-06-01T12:34:43Z

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2020-06-01T12:24:58Z

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Bendikhansen at 13:18, 5 April 2020

2020-04-05T13:18:50Z

Bendikhansen: Text replacement - "=Mitigation measures where MTD might be applied=" to "=Relevant mitigation measures and test cases="

2020-01-22T08:33:14Z

Text replacement - "=Mitigation measures where MTD might be applied=" to "=Relevant mitigation measures and test cases="

Bendikhansen at 12:51, 20 January 2020

2020-01-20T12:51:19Z

Bendikhansen at 12:46, 20 January 2020

2020-01-20T12:46:29Z

Bendikhansen at 12:45, 20 January 2020

2020-01-20T12:45:38Z

Bendikhansen at 12:41, 20 January 2020

2020-01-20T12:41:28Z

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-Developed by:
+Developed by: -
-Date:
+Date: -
 Type: [[:Category:Devices|Device]], [[:Category:Methods|Method]]
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 ==Particle Image Velocimetry (PIV)==
-The particle image velocimetry (PIV), is an optical technique to measure the velocity of a fluid by measuring the velocity of the movement of particles transported in this fluid assuming that the particles are transported with the fluid without losses or disturbances.
+The particle image velocimetry (PIV), is an optical technique to measure the velocity of a fluid by measuring the velocity of the movement of particles transported in this fluid, assuming that the particles are transported with the fluid without losses or disturbances.
 The main devices used for this technique are:
-*A source of light (for PIV usually a laser, however strong LED lights become more popular)
+* A source of light (for PIV usually a laser, however strong LED lights become more popular)
-*At least one camera (in case of a 3D or volume measurement up to at least 4 cameras)
+* At least one camera (in case of a 3D or volume measurement up to at least 4 cameras)
-*Synchronizer to be able to automatically synchronize the camera (times when the pictures are taken) with the laser, otherwise, the pictures would not be properly illuminated
+* Synchronizer to be able to automatically synchronize the camera (times when the pictures are taken) with the laser, otherwise, the pictures would not be properly illuminated
-*Software for evaluation of the results
+* Software for evaluation of the results
-*Mostly also tracer particles as seeding
+* Mostly also tracer particles as seeding
-A camera takes pictures of the moving particles in predefined very short time intervals t. Based on the known t the flow velocity can be calculated using special algorithms to evaluate the movement of the seeding points visible in the picture. The evaluation of the pictures uses an Eulerian’ approach. To be able to evaluate the distances on the pictures the system needs to be calibrated. As both, the time between the two pictures as well as the distance one particle has moved comparing these pictures the particle and hence the fluid velocity can be calculated.
+PIV is an instantaneous method to measure the flow. The main set-up usually includes a laser as a source of light (see Figure 1). With this laser, a so-called laser sheet, a plain area within the fluid, which is enlightened with the laser, is created. All particles transported with the fluid are supposed to reflect the light and become visible like that. This effect can be supported by using special tracer particles (which are quite costly and not necessarily environmentally friendly, so this is only recommended for lab use).
-To be able to achieve good results it is necessary to mind certain rules such as that one particle on the resulting picture should have the size of approx.. 2-3 pixel etc. It is highly recommended to get a detailed view into the literature (i.a. Raffel et al 2007) before starting with a measurement.
+A camera takes pictures of the moving particles in predefined, very short time intervals t. Based on the known t the flow velocity can be calculated using special algorithms to evaluate the movement of the seeding particles visible in the picture. The evaluation of the pictures uses an Eulerian’ approach. To be able to evaluate the shifts on the pictures the system needs to be calibrated. Using the information about the time shift t between two pictures and the calibrated length scale, comparing the shift of the particles on the pictures allow the calculation of the particle movement and hence the fluid velocity.
-The PIV can be bought as a complete system, usually including the software. Different companies such as ILA Laser applications, Dantec or LaVision offer this possibility including support. Some research institutions and universities also develop their own codes for post-processing depending on their needs or build the laser or camera system on their own.
+To be able to achieve good results it is necessary to keep in mind certain rules such as that one particle on the resulting picture should have the size of approx. 2-3 pixel etc. It is highly recommended to get a detailed view into the literature (i.a. Raffel et al 2007) before starting with a measurement.
 ==Large Scale PIV (LS-PIV) or Airborne Image Velocimetry (AIV)==
-Based on the idea of PIV the so-called Large Scale PIV (LS-PIV) or Airborne Image Velocimetry (AIV) was developed. As “Large Scale” with the increasing technical possibilities became a quite wide meaning the term AIV is more correct nowadays and used hereinafter.
+Based on the idea of PIV the so-called Large Scale PIV (LS-PIV) or Airborne Image Velocimetry (AIV) was developed. As “Large Scale” with the increasing technical possibilities became a quite wide meaning, the term AIV is more correct nowadays and used hereinafter.
-The measurement and evaluation technique is basically the same set-up and underlying theory as PIV and adapts this for use on large scale mainly in the field. As mentioned above the term large scale is not defined, generally, it means more than lab size with about max. 1.5m x 1.5m for very good conditions. Such as a PIV the AIV is an optical system used to measure flow velocities, other than the PIV mainly surface velocities in 2D.
+The measurement and evaluation technique is basically the same set-up and underlying theory as PIV and adapts this for use on large scale mainly in the field. As mentioned above the term large scale is not defined, generally it means more than lab size with about max. 1.5m x 1.5m for very good conditions. Such as a PIV the AIV is an optical system used to measure flow velocities, other than the PIV mainly surface velocities in 2D.
 Instead of Laser as a source of light, the AIV uses the sun. This might become a problem for the evaluation of the pictures when direct sun is present, as reflections will affect the quality of the results.
 =Application=
-For the application, the study area needs to be well known and targets need to be distributed in the area to ensure a defined distance for calibration on the pictures. As due to the huge amount of seeding needed for one run, not too many tests can be run and it is highly recommended to prepare the experiment properly.
+For the application, the study area needs to be well known and targets need to be distributed in the area to ensure a defined distance for calibration on the pictures. As due to the huge amount of seeding needed for one run, not too many tests can be done and it is highly recommended to prepare the experiment properly.
-*Set up of targets (either own targets such as wooden crosses or coded targets can be used). The coded targets are especially of interest in case the measurement shall be combined with a Structure from Motion measurement
+After the data collection in the field, the more time-consuming part is the data evaluation. Therefor different software can be used. Usually all variations of commercial products such as the PIV software of DanTec, ILA or LaVision. Further free software such as PIVlab can be used.
-After the data collection in the field, the more time consuming part is the data evaluation. Therefore different software can be used. Usually all variations of commercial products such as the PIV software of DanTec, ILA or LaVision. Further free software such as PIVlab can be used.
+Depending on the software different levels of own effort are necessary when it comes to rectification of the pictures (Detert et al. 2015; Patalano et al 2017). The software is, when using PIVlab, MATLAB based and the basic idea is the same as for a standard PIV.
-Depending on the software different levels of own effort are necessary when it comes to rectification of the pictures (Detert et al. 2015; Patalano et al 2017). The software is, when using PIVlab, MATLAB based and the basic idea is the same as for a standard PIV.
+Note that the results generated are surface velocities in the measured areas (Figure 3).
 =Relevant mitigation measures and test cases=

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 *Patalano A, Garcia CM, Rodriguez A (2017) Rectification of image velocity results (river): a simple and user-friendly toolbox for large scale water surface particle image velocimetry (piv) and particle tracking velocimetry (ptv). Comput Geosci 109:323–330 http://dx.doi.org/10.1016/j.cageo.2017.07.009
 *Pagano, C., Tauro, F., Porfiri, M., & Grimaldi, S. (2014). Development and testing of an unmanned aerial vehicle for large scale particle image velocimetry. Proceedings of ASEM (2014) Dynamic Systems and Control Conference, San Antonio,USA, DSCC2014-5838.
 *Thielicke, W., & Stamhuis, E. J. (2014). PIVlab: Time-resolved digital Particle Image Velocimetry tool for MATLAB (version: 1.35). http://dx.doi.org/10.6084/m9.figshare.1092508.
 *Weitbrecht, V., Kühn, G., & Jirka, G. H. (2002). Large-scale PIV-measurements at the surface of shallow water flows. Flow Measurement and Instrumentation, 13, 237–245.

@@ Line 72: / Line 72: @@
 *Gunawan, B., Sun, X., Sterling, M., Shiono, K., Tsubaki, R., Rameshwaran, P., Knight, D.W., Chandler, J.H., Tang, X., Fujita, I., (2012). The application of LS-PIV to a small irregular river for inbank and overbank flows. Flow Measurement and Instrumentation 24, 1–12. https://doi.org/10.1016/j.flowmeasinst.2012.02.001
 *Le Coz, J., Hauet, A., Pierrefeu, G., Dramais, G., Camenen, B., 2010. Performance of image-based velocimetry (LSPIV) applied to flash-flood discharge measurements in Mediterranean rivers. Journal of Hydrology 394, 42–52. https://doi.org/10.1016/j.jhydrol.2010.05.049
-*Muste, M., Fujita, I., & Hauet, A. (2008). Large-scale particle image velocimetry for measurements in riverine environments. Special Issue on Hydrologic Measurements, Water Resources Research, 44, doi:10.1029/2008WR006950.
+*Muste, M., Fujita, I., & Hauet, A. (2008). Large-scale particle image velocimetry for measurements in riverine environments. Special Issue on Hydrologic Measurements, Water Resources Research, 44, https://doi.org/10.1029/2008WR006950.
 *Muste, M., Hauet, A., Fujita, I., Legout, C., Ho, H.-C., (2014). Capabilities of Large-scale Particle Image Velocimetry to characterize shallow free-surface flows. Advances in Water Resources 70, 160–171. https://doi.org/10.1016/j.advwatres.2014.04.004
-*Patalano A, Garcia CM, Rodriguez A (2017) Rectification of image velocity results (river): a simple and user-friendly toolbox for large scale water surface particle image velocimetry (piv) and particle tracking velocimetry (ptv). Comput Geosci 109:323–330
+*Patalano A, Garcia CM, Rodriguez A (2017) Rectification of image velocity results (river): a simple and user-friendly toolbox for large scale water surface particle image velocimetry (piv) and particle tracking velocimetry (ptv). Comput Geosci 109:323–330 http://dx.doi.org/10.1016/j.cageo.2017.07.009
 *Pagano, C., Tauro, F., Porfiri, M., & Grimaldi, S. (2014). Development and testing of an unmanned aerial vehicle for large scale particle image velocimetry. Proceedings of ASEM (2014) Dynamic Systems and Control Conference, San Antonio,USA, DSCC2014-5838.
 *Thielicke, W., & Stamhuis, E. J. (2014). PIVlab: Time-resolved digital Particle Image Velocimetry tool for MATLAB (version: 1.35). http://dx.doi.org/10.6084/m9.figshare.1092508.

@@ Line 70: / Line 70: @@
 *Fujita, I., & Hino, T. (2003). Unseeded and seeded PIV measurementsof river flows video from a helicopter. Journal of Visualization, 6, 245–252.
 *Fujita, I., & Kunita, Y. (2011). Application of aerial LSPIV to the 2002 flood of the Yodo River using a helicopter mounted high density video camera. Journal of Hydroenvironmental Research, 5, 323–331.
-*Gunawan, B., Sun, X., Sterling, M., Shiono, K., Tsubaki, R., Rameshwaran, P., Knight, D.W., Chandler, J.H., Tang, X., Fujita, I., 2012. The application of LS-PIV to a small irregular river for inbank and overbank flows. Flow Measurement and Instrumentation 24, 1–12. https://doi.org/10.1016/j.flowmeasinst.2012.02.001
+*Gunawan, B., Sun, X., Sterling, M., Shiono, K., Tsubaki, R., Rameshwaran, P., Knight, D.W., Chandler, J.H., Tang, X., Fujita, I., (2012). The application of LS-PIV to a small irregular river for inbank and overbank flows. Flow Measurement and Instrumentation 24, 1–12. https://doi.org/10.1016/j.flowmeasinst.2012.02.001
 *Le Coz, J., Hauet, A., Pierrefeu, G., Dramais, G., Camenen, B., 2010. Performance of image-based velocimetry (LSPIV) applied to flash-flood discharge measurements in Mediterranean rivers. Journal of Hydrology 394, 42–52. https://doi.org/10.1016/j.jhydrol.2010.05.049
 *Muste, M., Fujita, I., & Hauet, A. (2008). Large-scale particle image velocimetry for measurements in riverine environments. Special Issue on Hydrologic Measurements, Water Resources Research, 44, doi:10.1029/2008WR006950.
-*Muste, M., Hauet, A., Fujita, I., Legout, C., Ho, H.-C., 2014. Capabilities of Large-scale Particle Image Velocimetry to characterize shallow free-surface flows. Advances in Water Resources 70, 160–171. https://doi.org/10.1016/j.advwatres.2014.04.004
+*Muste, M., Hauet, A., Fujita, I., Legout, C., Ho, H.-C., (2014). Capabilities of Large-scale Particle Image Velocimetry to characterize shallow free-surface flows. Advances in Water Resources 70, 160–171. https://doi.org/10.1016/j.advwatres.2014.04.004
-*Pagano, C., Tauro, F., Porfiri, M., & Grimaldi, S. (2014). Development and testing of an unmanned aerial vehicle for large scale particle image velocimetry. Proceedings of ASEM 2014 Dynamic Systems and Control Conference, San Antonio,USA, DSCC2014-5838.
+*Patalano A, Garcia CM, Rodriguez A (2017) Rectification of image velocity results (river): a simple and user-friendly toolbox for large scale water surface particle image velocimetry (piv) and particle tracking velocimetry (ptv). Comput Geosci 109:323–330
 *Thielicke, W., & Stamhuis, E. J. (2014). PIVlab: Time-resolved digital Particle Image Velocimetry tool for MATLAB (version: 1.35). http://dx.doi.org/10.6084/m9.figshare.1092508.
 *Weitbrecht, V., Kühn, G., & Jirka, G. H. (2002). Large-scale PIV-measurements at the surface of shallow water flows. Flow Measurement and Instrumentation, 13, 237–245.

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 =Quick summary=
-[[file:piv_2d_system.png|thumb|500px|Figure 1: Set-up of a standard 2D PIV system (Raffel et al 2007).]]
+[[file:piv_2d_system.png|thumb|250px|Figure 1: Set-up of a standard 2D PIV system (Raffel et al 2007).]]
-[[file:piv_large_scale.png|thumb|500px|Figure 2: LSPIV measurement sequence: (a) imaging the area to be measured (white patterns indicate the natural or added tracers used for visualization of the free surface), (b) the distorted raw image, and (c) the undistorted image with the estimated velocity velocity vectors overlaid on the image (Muste et al. 2008).]]
+[[file:piv_large_scale.png|thumb|250px|Figure 2: LSPIV measurement sequence: (a) imaging the area to be measured (white patterns indicate the natural or added tracers used for visualization of the free surface), (b) the distorted raw image, and (c) the undistorted image with the estimated velocity velocity vectors overlaid on the image (Muste et al. 2008).]]
-[[file:piv_airborne.png|thumb|500px|Figure 3: Swiss grid georeferenced surface velocity field and streamlines measured by AIV, including areas of noisy velocity data. (Note that according to Le Coz et al. (2010), depth averaged velocities can be estimated by multiplying the surface velocity by factors 0.79-0.89, with a central value close to 0.85); (Detert et al. 2019).]]
+[[file:piv_airborne.png|thumb|250px|Figure 3: Swiss grid georeferenced surface velocity field and streamlines measured by AIV, including areas of noisy velocity data. (Note that according to Le Coz et al. (2010), depth averaged velocities can be estimated by multiplying the surface velocity by factors 0.79-0.89, with a central value close to 0.85); (Detert et al. 2019).]]

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 The results generated are surface velocities in the measured areas (Figure 3).
-=Mitigation measures where MTD might be applied=
+=Relevant mitigation measures and test cases=
 {{Suitable measures for Particle image velocimetry (PIV)}}

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 =Mitigation measures that MTD is useful for=
-{{Suitable measures for Particle image velocimetry (PIV)}}
+{| class="wikitable"
 =Relevant literature=

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 =Other information=
 Costs depend mainly on the staff needed for conducting the experiment and in case a commercial software is used for evaluation on the license costs.
 =Relevant literature=