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.
(in collaboration with von Karman Institute, Brussels, Belgium)
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.
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.
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.
Measurement Principle
Particle Image Velocimetry (PIV) is a whole-flow-field technique providing instantaneous velocity vector measurements in a cross-section of a flow.
