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PRODID:ics.py - http://git.io/lLljaA
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DESCRIPTION:Come learn about how to keep vast fields of mirrors pointed at just the right angle to collect the most solar power! Pizza at noon outside Tutt Science!\n\nThe presenter is Dr. Rebecca Mitchell\, CC Class of 2012\, a researcher at the National Renewable Energy Laboratory.\n\n11/4/22 <a href="https://en.wikipedia.org/wiki/Template:%E2%80%A2">•</a> 12:30 - 1:30 PM <a href="https://en.wikipedia.org/wiki/Template:%E2%80%A2">•</a> Kresge Lecture Hall (Tutt 122)\n\nFull abstract:\nConcentrating Solar Power (CSP) is a renewable energy technology that uses mirrors to concentrate sun rays onto a receiver\, where it is converted to thermal energy. This energy can be stored in a heat transfer medium which allows it to be converted to electricity using a heat exchanger and generator. Central tower CSP plants use field of heliostats\, or mirrors that track the sun in two-dimensions\, to focus sunlight at a single fixed receiver positioned on top of a tower. A heliostat consists of a grid of discrete mirror facets that can be curved and canted (“tilted”) to approximate the shape of a parabola with a focal length close to the distance to the receiver. Heliostat optical errors play a major role in the plant efficiency\, with models showing that an increase of 4 mrad (~0.2°) in mirror surface slope error can reduce the heliostat field performance by up to 10%. However\, there exists no reliable tool to measure the optical errors of the thousands of heliostats in a utility-scale solar field. The Non-Intrusive Optical (NIO) method has been developed by the National Renewable Energy Laboratory (NREL) to automatically survey and derive heliostat optical error estimates of a commercial CSP plant. The approach uses Unmanned Aircraft Systems (UAS) to collect images of the reflected tower in the mirror surfaces.  Key heliostat and reflected tower features are detected in the image frames using machine learning and computer vision techniques. Methods in photogrammetry and geometrical optics are used to reconstruct a three-dimensional model of the heliostat\, camera\, and tower and to derive the mirror surface slope error\, facet canting errors\, and overall heliostat tracking error. Initial data collection and sensitivity analysis has shown that the NIO method is capable of estimating slope error within accuracy of 0.25 mrad. The presentation will begin with an introduction of CSP technology. The NIO methodology will be presented with example image data collected from a commercial CSP plant and image processing result. The presentation will conclude with information on future opportunities for students to get involved with research in heliostat technologies.\n\nhttps://today.coloradocollege.edu/events/5576
DTEND:20221104T193000Z
LOCATION:Tutt Science 122 - Lecture Hall (Social Science)
DTSTART:20221104T183000Z
SUMMARY:Fearless Friday: Making the Most of Archimedes' Burning Mirrors!
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