Understanding the expected performance of large-scale solar ponds from laboratory-scale observations and numerical modeling

Solar ponds are low-cost, large-scale solar collectors with integrated storage that can be used as an energy source in many thermal systems. Experimental solar pond investigations at smaller scales have proven to be useful when trying to understand how different factors affect the pond’s efficiency, but they do not necessarily represent the expected performance of large-scale solar ponds. Consequently, it is important to investigate how the results of small-scale solar pond experiments can be scaled up. In this work, we show how models based on laboratory-scale observations can be utilized to understand the expected performance of large-scale solar ponds. This paper presents an approach that combines high-resolution thermal observations with computational fluid dynamics to investigate how different physical processes affect solar pond performance at different scales. The main factors that result in differences between small- and large-scale solar pond performances are boundary effects, light radiation spectrum and intensity, and turbidity. Boundary effects (e.g., pond geometry, thermal insulation) reduce the energy that reaches the storage zone of small-scale solar ponds. Different types of lights result in different radiation spectrum and intensity, which affects the energy reaching the storage zone. Turbidity is typically not important in small-scale solar ponds subject to controlled environmental conditions. However, it is an important factor in outdoor solar ponds in which the pond is prone to particles that can deposit onto the water surface or become suspended in the gradient zone. In general, the combination of these factors results in less energy collected in small-scale solar ponds than in large-scale solar ponds, even though large-scale solar ponds are typically subject to more extreme environmental conditions. High-resolution thermal observations combined with numerical simulations to understand the expected performance of large-scale solar ponds seems to be a promising tool for improving both efficiency and operation of these solar energy systems.