Supercomputer Real-Time Experimental Data Processing: Technology and Applications

Particle image velocimetry (PIV) data processing time can constrain data set size and limit the types of statistical analyses performed. General purpose graphics processing unit (GPGPU) computing can accelerate PIV data processing allowing for larger datasets and accompanying higher order statistical analyses. However, this has not been widespread likely due to limited accessibility to the GPU-PIV hardware and software. Most GPU-PIV software is platform dependent and proprietary, which restricts the computing systems that can be used and makes the details of the algorithm unknown. This work highlights the development of an open-source, cross-platform, GPU-accelerated, PIV algorithm. Validation of the algorithm is done using both synthetic and experimental images. The algorithm was found to accurately resolve the time-averaged flow, instantaneous velocity fluctuations, and vortices. All data processing was done on a GPU supercomputing cluster and notably outperformed the central processing unit version of the software by a factor of 175. The algorithm is freely available and included in the OpenPIV distribution.

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Supercomputer Real-Time Experimental Data Processing: Technology and Applications

Cited by 3 publications

References 12 publications

Optimizing Numerical Design of a Multi-mode Aero-Cooled Climatic Wind Tunnel Nozzle on a PNRPU High-performance Computational Complex

Optimizing Numerical Design of a Multi-mode Aero-Cooled Climatic Wind Tunnel Nozzle on a PNRPU High-performance Computational Complex

Developing Cyber Infrastructure and a Model Climatic Wind Tunnel Based on the PNRPU High-Performance Computational Complex

Graphical Processing Unit-Accelerated Open-Source Particle Image Velocimetry Software for High Performance Computing Systems

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