Particle Image Velocimetry (PIV)

von Karman vortex street measured with time-resolved PIV

Particle Image Velocimetry (PIV) is a non-intrusive laser optical measurement technique for research and diagnostics into flow, turbulence, microfluidics, spray atomization, and combustion processes.

Dantec Dynamics offers a range of PIV solutions to suit a variety of research needs. Basic systems utilize a single camera to measure two velocity components in a plane. More advanced systems utilize multiple cameras to measure three velocity components either in a plane or in a volume.

High speed systems are available to study vortices. Furthermore, advanced features like uncertainty estimation and propagation that tell about the measurement quality, advanced post-processing routing for vortex detections, combined PIV / LIF / Shadow measurement, and pressure computations help to get most out of your data.


Advanced Analysis and Data Visualization

Our imaging software DynamicStudio comes with advanced analysis and visualization capabilities.
You can create quality 2D and 3D graphics and animations from your measured data.
For traceability and comfort, no exporting and re-importing of data is necessary; all data is retained in DynamicStudio.

Advanced analysis features and Post Processing routines in DynamicStudio offer lots of features that simplify the life of fluid mechanic experimentalists.

Pressure field computed from PIV velocity map
(in collaboration with von Karman Institute, Brussels, Belgium)
Dynamic masking examples. Full HD resolution

Examples

  • Adaptive PIV for easy, precise and fast planar PIV data analysis
  • Uncertainty estimation and propagation compute the uncertainty of each vector and can propagate it into velocity derivatives
  • Analysis sequences to build batch processing libraries
  • Pressure from PIV Add-on derives pressures from velocity fields
  • Easy visualization tools
  • Dynamic Masking Add-on to mask out moving objects

Modal Decomposition

Modal decompositions are a great tool to automatically identify and separate different features & phenomena in a flow. They can be used for many useful things:

  • They can give us a better understanding by separating flows into their different modes.
  • One can reconstruct the flow using just the dominant modes with major flow features and thereby filter away measurement noise and outliers, typically described by the higher order modes.
  • Modal decompositions can be used for data reduction by storing just the dominant modes with the highest energy content and later reconstruct the flow from the stored data.

Modal decompositions can also be used for stability analysis when Oscillating Pattern Decomposition, identify cyclic patterns in the flow along with frequencies and growth or decay rates. DynamicStudio features four different Modal decompositions, each doing different things:

  • Proper Orthogonal Decomposition (POD)
  • Multiscale POD (mPOD)
  • Fourier Modal Decomposition (FMD)
  • Oscillating Pattern Decomposition (OPD)

Proper Orthogonal Decomposition (POD)

The classic Modal decomposition in DynamicStudio is the POD. As with all modal decompositions, it separates the flow into different modes in both space (Topos) and time (Chronos). Each of these modes represents a certain type of flow feature like a vortex, for example. The modes are sorted by energy content with mode 0, the mean, first, followed by less and less energetic modes.

This chart shows the energy of the different modes and the accumulated energy of a flow. In this example the first 4 modes contain over 92% of the flows energy. The rest could be considered noise.

Classic POD does not need time-resolved input but may mix up different phenomena occurring at different frequencies in the same mode. Classic POD is available for images, scalars, 2D and 3D maps.


Multiscale Proper Orthogonal Decomposition (mPOD)

With time-resolved input, dominant frequencies in a flow can be identified in a frequency domain plot of the covariance matrix. The mPOD allows the user to separate output modes into distinct frequency bands. With just a few mouse clicks, phenomena that classic POD might have mixed up can thus be clearly separated from one another. mPOD is applicable for scalars, 2D and 3D vector maps.

The mPOD collaboration with the von Karman Institute, Belgium (VKI) published in Measurement Science and Technology won the publication’s Outstanding Paper Award for 2020 in the field of Fluid Mechanics.    

Covariance matrix showing several peaks and the created mask for the mPOD.
Comparison of the Chronos (black curves) and Spectrum (red curves) from the same mode of a POD (top), and mPOD (center), and FMD (bottom).

Fourier Mode Decomposition (FMD)

The FMD separates the modes by their frequency content. Thus all modes are frequency independent from each other, which is a great tool if you want to study the influence of e.g. shedding frequencies or impinging jets, where many different vortical structures are present, but with different frequencies.

The main differences of the different modal decomposition methods offered in DynamicStudio can be seen in the comparison figure: the plots of the chronos and the spectrum of a mode.

The black curves are the Chronos and the red ones the corresponding Spectra. The x-axis is the frequency of the Spectrum, or the time of the Chronos.

All spectra show a low frequency peak, but the result from Classic POD include high frequency content, clearly visible in both the spectrum and the Chronos itself. In the mPOD results most of the high frequency content has been filtered away and ensure a smooth Chronos and a ‘clean’ spectrum. FMD results go a step further and exclude all but the dominant frequency also visible in the first two. The high frequency content is accounted for by other modes.

Oscillating Pattern Decomposition (OPD)

With Classic POD Analysis as preprocessing of a time-resolved dataset, OPD performs stability analysis of the flow. Resulting modes have distinct frequencies plus exponential growth or decay rates to identify dominant flow structures. OPD Topos are complex and can be animated to visualize features such as traveling vortices in the flow. This add-on is ideal for investigating vortex shedding in shear regions, acoustic- or thermo-acoustic driven vortex formation, and vortex enhancement. A key reason for investing in a TR-PIV system is the ability to perform frequency domain analysis. OPD software is a dedicated tool for such analysis and therefore an indispensable complement to any TR-PIV system.

OPD analysis of a pulsed jet. e-fold time (i.e. decay rate) of the OPD modes plotted against their associated frequencies (Top Right). Three dominant OPD modes are highlighted and corresponding convected vortices are shown. The higher frequency modes are clearly harmonics.
Complex OPD mode for an oscillating disc in water, where the modal frequency corresponds to the 4th harmonic of the oscillation frequency. The oblique vortex shedding from the disc tip is clearly identifiable. From Ergin F.G. ”Modal Investigation of Vortex Ring Shedding from an Oscillating Disc” 2021, Experimental Techniques.

Features & Benefits

Click here to expand

PIV Solutions

 

EduPIV

The EduPIV system offers a safe, affordable and turnkey solution for introducing students to the PIV technique
 

Planar PIV

Planar, stereo, and time-resolved PIV systems to meet a variety of testing needs
 

Volumetric Velocimetry

Volumetric Velocimetry systems capture all three velocity components in three dimensions (3D3C)
 

Submersible Stereo PIV

Streamlined stereo PIV system for use in towing tanks and other large water flumes

Application Notes

Flow Investigation behind Delta Winglet Vortex Generators by Stereo PIV

Flow Investigation behind Delta Winglet Vortex Generators by Stereo PIV

Enhanced turbulent mixing is essential to intensify convective heat transfer in various industrial devices to improve performance and…
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Phase-Resolved PIV Measurements in a Stirred Tank

Phase-Resolved PIV Measurements in a Stirred Tank

There has been a recent focus on investigating flows existing within viscous stirred tank systems, both experimentally and…
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Analysis of 4th International PIV Challenge Case A

Analysis of 4th International PIV Challenge Case A

PIV images from the 4th PIV challenge, case A, were analyzed. Masking was done analytically, based on the…
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Modal analysis in a micromixer

Modal analysis in a micromixer

Time-resolved MicroPIV systems can provide spatio-temporal modal information in microfluidics research on mixing. In this application note, we…
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Time-Resolved Velocity Measurements of Droplet Formation in a Flow Focusing Junction

Time-Resolved Velocity Measurements of Droplet Formation in a Flow Focusing Junction

Understanding the flow field related to monodisperse emulsions is crucial as they are often the subject of lab-on-the-chip…
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PIV measurements during the dolphin kick of a human swimmer

PIV measurements during the dolphin kick of a human swimmer

Only a few methods to visualize and quantify flow have been so far applied to human swimming. They…
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Flow Discharge Vectoring using a Miniature Fluidic Actuator

Flow Discharge Vectoring using a Miniature Fluidic Actuator

Particle Image Velocimetry was used to measure the centerplane velocities in the near-field of a subsonic jet with…
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Submersible Stereo PIV at MARIN

Submersible Stereo PIV at MARIN

MARIN acquired a DANTEC stereo-PIV system which has been operational since September 2009. The power of the lasers…
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Multi-Parameter Measurements in a Micromixer

Multi-Parameter Measurements in a Micromixer

MicroPIV systems can provide multi-parameter information in microfluidics research on mixing. In this application note, we describe the…
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Measurement of an Impinging Jet with the Latest Stereo PIV Technique

Measurement of an Impinging Jet with the Latest Stereo PIV Technique

Impinging jets have been studied extensively thanks to their potential for obtaining high convective heat transfer rates in…
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Flow in Metallurgical Reactors

Flow in Metallurgical Reactors

A NDT method for a structure with stiff stainless steel skins and low-density foam cores was to be…
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Velocity characterization of NIST’s reference spray combustion facility using stereoscopic PIV

Velocity characterization of NIST’s reference spray combustion facility using stereoscopic PIV

Computational fluid dynamics (CFD) offers a cost-effective alternative to experiments, and it is increasingly being used to solve…
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Flow characterization of flickering methane/air laminar diffusion flames using PIV

Flow characterization of flickering methane/air laminar diffusion flames using PIV

Vortex interactions with flames play a key role in many practical combustion applications. Such interactions form the basis…
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Velocity Measurements in Hydrogen Flames using LDA and PIV

Velocity Measurements in Hydrogen Flames using LDA and PIV

The transition from the use of hydrocarbons to alternative fuels such as hydrogen is expected to be a…
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Flow investigations in the aortic arch during cardiopulmonary bypass with Stereo-PIV

Flow investigations in the aortic arch during cardiopulmonary bypass with Stereo-PIV

In case of open-heart surgery, an external blood pump maintains the circulatory pressure. Venous blood is collected, oxygenated…
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Flow Field Mapping With Stereoscopic PIV Around A Full-Scale Car

Flow Field Mapping With Stereoscopic PIV Around A Full-Scale Car

While obtaining full-scale PIV measurements in an airflow has always been a dream of many, the research team…
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Measuring trailing vortices behind an Airbus

Measuring trailing vortices behind an Airbus

During take-off and landing, powerful vortices are created behind an aircraft. Such vortices result from pressure differences between…
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PIV Measurements on a Delta Wing with Periodic Blowing and Suction

PIV Measurements on a Delta Wing with Periodic Blowing and Suction

The mechanism by which periodic blowing and suction improves lift and stall angle of a 70 degree sweep…
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Download Library

TITLE
AUTHOR(S)
YEAR
DOWNLOAD FILE
AUTHOR(S)

F. Gökhan Ergin

YEAR

2021

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Springer
AUTHOR(S)

F. Gökhan Ergin, Jimmy Olofsson, Per Petersson, Nicolai Fog Gade-Nielsen

YEAR

2020

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ScienceDirect
AUTHOR(S)

Laurentiu Moruz, Jens Kitzhofer, David Hess, Mircea Dinulescu

YEAR

2019

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ScienceDirect
AUTHOR(S)

Malene H. Vested, F. Gökhan Ergin, Stefan Carstensen, Erik D. Christensen

YEAR

2018

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PDF
AUTHOR(S)

F. Gökhan Ergin, Jimmy Olofsson, Bo B. Watz, Nicolai Fog Gade-Nielsen

YEAR

2018

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PDF
AUTHOR(S)

F. Gökhan Ergin

YEAR

2017

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AUTHOR(S)

F. Gökhan Ergin, Jimmy Olofsson, Nicolai Fog Gade-Nielsen

YEAR

2017

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PDF
AUTHOR(S)

F. Gökhan Ergin, Dantec Dynamics A/S

YEAR

2017

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PDF
AUTHOR(S)

Hua Wang, F. Gökhan Ergin

YEAR

2014

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PDF
AUTHOR(S)

Per Petersson, Rikard Wellander, Jimmy Olofsson, Henning Carlsson, Christian Carlsson, Bo Beltoft Watz, Nicolas Boetkjaer, Mattias Richter, Marcus Aldén, Laszlo Fuchs, Xue-Song Bai

YEAR

2012

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AUTHOR(S)

V. Jaunet, J. F. Debiève and P. Dupont

YEAR

2012

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AUTHOR(S)

J. Kitzhofer, F. G. Ergin, V. Jaunet

YEAR

2012

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AUTHOR(S)

Ivana M. Milanovic, Khaled J. Hammad

YEAR

2010

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AUTHOR(S)

Palle Gjelstrup

YEAR

2009

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