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
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®.
New Multiscale Proper Orthogonal Decomposition (mPOD) Add-on
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.
POD and mPOD Now Support Volumetric Velocimetry Data
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.
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.
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.
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.
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