Many new features for Time Resolved PIV in DynamicStudio 7.3

Our new release of DynamicStudio includes faster analysis, new and improved modal decomposition methods, and easy data exchange with CFD.

Combine PIV, CFD and CAD Data by the CFD Export Feature

PIV data is used as boundary conditions for a CFD simulation of the flow inside the EduPIV flow nozzle. The PIV and CAD data are visualized together with a CAD drawing of the flow nozzle, using ParaView®.

Research projects in fluid dynamics often use both experimental and numerical approaches. Numerical simulations using CFD (Computational Fluid Dynamics) are computed using experimental data as boundary/initial conditions. The goal is to obtain the most detailed knowledge of the flow.

CFD Export for DynamicStudio allows direct use of experimental PIV data in CFD simulations, by placing PIV velocity vector fields in a CFD mesh and geometry.

PIV vector field data are exported in a native OpenFOAM® format, allowing them to be read, manipulated and visualized by tools such as ParaView®.

Read more here.

New Multiscale Proper Orthogonal Decomposition (mPOD) Add-on

mPOD modes separated
mPOD mode 2 and 4 are similar at first glance, but mode 2 shows saddle points where mode 4 shows vortices.

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. 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.

Read more here.

POD and mPOD Now Support Volumetric Velocimetry Data

Manually filtered: range, UOD, Temporal, UOD
POD filtered, one method only

POD and mPOD can be used to find dominant structures in a flow, and to filter the flow data for further processing by removing the least energetic modes which are likely to be noise.

Finding dominant structures can be challenging if you have no a priori knowledge of the flow. POD and mPOD can do this in a single step, rather than by a trial and error process with a number of manually defined filters.

Read more here.

Active Image Stitching Takes the ActiveTarget One Step Further

With ActiveTarget(s) and the new, semi-automated Active Image Stitching, you can create larger fields of view without sacrificing spatial resolution by using two or more cameras with overlapping fields of view. Each dot on the Active Target can be recognised separately, so there is no need for a center marker visible by each camera.

Read more here.

GPU-accelerated Adaptive PIV Processing

Adaptive PIV is generally recognised as the state-of -the-art processing method for PIV: it adapts the interrogation are size to the seeding density, and the shape to velocity gradients.

Adaptive PIV processing can now be done up to 4.6 times faster than before. This speed-up is achieved by using the CPU and a GPU in parallel. The speed gain is highest for high resolution PIV images with many vectors per time step.

Read more here.

Support for New High-speed Cameras for Time Resolved PIV (TR PIV)

Three new cameras:

SpeedSense T1340 – a 4 MP camera with up to 3270 frames per second (fps)

SpeedSense VEO 1310 and VEO 1010: 1 MP cameras with 10860 and 8420 fps, respectively.

These cameras fill gaps in the current portfolio and have extremely high sensitivity: up to 80,000 ISO for the VEO cameras, and up to 125,000 ISO for the T1340. This makes them well suited for applications with difficult illumination conditions.

Read more here.