The Sandia Wake Imaging System (SWIS) is being developed to improve the spatial and temporal resolution capabilities of velocity measurements within wind farms. These high-resolution velocity measurements are needed to provide the necessary data for validating high-fidelity simulations. SWIS uses a technology (explained thoroughly in a previous report) where the velocity component measured and quality of the measurement depends on the configuration of the transmitter (laser sheet), receiver (camera), and viewing region. As a result of the complicated measurement dependence on setup configuration and the need to meet the validation requirements at many locations in a wind turbine wake, a new tool that models the physics of SWIS has been developed to better predict and anticipate the system’s performance when deployed at the Scaled Wind Farm Technology (SWiFT) facility.
With this new tool we can more effectively plan and optimize testing configurations for different flow structures of interest at SWiFT. The tool is a MATLAB-based program that models the three-dimensional arrangement and physics of the transmitter, receiver, and viewing region. The image below shows an example of both the ideal and expected velocity measurement using the SWIS setup in illustration above.
Representative velocity fields at the SWiFT facility, produced using high-fidelity simulation methods, are imported into the program which calculates the ideal velocity along with the noise equivalent velocity, and a representation of the system measurement uncertainty. The simulated measurement adds the calculated noise equivalent velocity to the ideal velocity, resulting in the degraded image presented on the right in Expected Measured Velocity. The example case shows the results of a representative flow field covering a 5 × 5m area with a noise equivalent velocity of ±1 m/s displayed in the camera frame of reference. The SWIS modeling tool provides a quick and efficient method for analyzing and optimizing the system configuration and setup before deployment at SWiFT in July or August of 2015.
Sandia National Labs
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