From Event: SPIE Optical Engineering + Applications, 2018
We present a computational framework for optimizing nonimaging solar concentrators. Our approach is to represent the concentrator’s shape as a polygon, use ray tracing to compute the flux at the receiver, and employ Generalized Pattern Search (GPS) on the polygon’s vertices. Many shape optimization techniques use gradients to seek a direction of steepest ascent or descent. For solar concentrators, these approaches can easily get trapped in local minima. In contrast, GPS is a derivative-free method that seeks a global optimum on suitable meshes, without computing gradients. This helps to avoid getting trapped in local minima. Results for 2D concentrators show that our algorithm can converge to the ideal concentrator's shape as the number of polygon vertices increases. We also show that when the number of vertices is small and fixed, the optimal polygon can differ significantly from the polygon that would be obtained using a uniform collocation of the ideal shape. This approach could lead to a simple, accurate, and fast design method, and improve the performance and lower the fabrication costs of nonimaging concentrators for solar and thermal applications.
B. Ilan, C. Hoffman , and C. Rimland, "Computational optimization for nonimaging solar concentrators using generalized pattern search," Proc. SPIE 10758, Nonimaging Optics: Efficient Design for Illumination and Solar Concentration XV, 107580L (Presented at SPIE Optical Engineering + Applications: August 20, 2018; Published: 14 September 2018); https://doi.org/10.1117/12.2503895.
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