研究目的
Investigating the optimization of heliostat field layouts using Particle Swarm Optimization (PSO) combined with GPU-based ray-tracing to improve solar field efficiency.
研究成果
Particle swarm optimization of a radial stagger layout reduces the field footprint and increases the efficiency using fewer heliostats than the base layout. The particle swarm optimization of a spiral layout reduces the footprint even further, but decreases the efficiency using more heliostats than the base layout. The improvement in computation time using GPUs is significant.
研究不足
The PSO runs were limited to a day or two of computation time, suggesting that longer runs could further improve efficiency. The study focuses on rectangular heliostats and specific field configurations.
1：Experimental Design and Method Selection:
The study employs the Particle Swarm Optimization (PSO) algorithm combined with GPU-based ray-tracing for optimizing heliostat field layouts.
2：Sample Selection and Data Sources:
Two case studies are used: a small field and a large field with rectangular heliostats.
3：List of Experimental Equipment and Materials:
A cluster of six Windows-based machines with NVIDIA GPUs (GTX 580, GTX 590, GTX Titan, GTX Titan Black, GTX 980Ti, GTX 1080, GTX 1080Ti) is utilized.
4：Experimental Procedures and Operational Workflow:
The PSO algorithm is applied to optimize the layout of heliostat fields, with annual performance computed using a fast ray-tracing engine on GPUs.
5：Data Analysis Methods:
The efficiency of the optimized layouts is compared to baseline layouts in terms of number of heliostats, annual energy production, and reflective surface area.
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