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.

No abstract

This paper presents a mobile testing rig developed for small wind turbine (SWT) experimental work to orchestrate, cost-effectively, turbine performance characterization in both controlled wind inflow speeds and turbulent ambient flows. It facilitates off-grid testing of up to a 1 kW wind turbine. It is a dual-purpose machine that can be towed behind a vehicle to conduct steady-state tests (track testing) or be parked to collect unsteady field data (field testing), all with the same rotor and instrumentation. Its mechanical design included computational fluid dynamics (CFD) analysis to gauge the potential impact of towing vehicle disturbance on the free stream available to the rotor. To provide a compelling platform for full rotor speed control, a reconfigurable control system coupled to an electric vehicle controller with regenerative braking technology has been modeled and implemented into its electrical design. Uncertainty analysis has also been rigorously conducted to project the error bounds pertaining to both precision and bias components of the testing results. The rig has been tested in a towed scenario and blade element momentum (BEM) simulations have been compared with the actual aggregate performance curves obtained experimentally. Future work involves testing in unsteady winds, for which the rig was ultimately designed in order to better understand unsteady rotor performance and adaptive design.

No abstract

No abstract