Development and experimental validation of a physical model for the soiling of mirrors for CSP industry applications. (October 2018) - Picotti, G.
Highlights: Development of a physical model that allows to predict soiling (reflectance) losses. The size distribution of airborne dust influences significantly its deposition. The concentration of airborne dust influences significantly the soiling losses. Model prediction of the reflectance trend validated with experimental data. Abstract: Addressing soiling-related losses of glazed surfaces in solar power technologies is one of the most critical issues to improve the competitiveness of solar power plants in the current energy market. Reliable predictions of soiling rates and performance losses would enable optimisation of cleaning strategies and add a valuable assessment criterion for the evaluation of potential future power plant sites. However, currently available soiling models are data-driven and thus specific to the site and do not allow for reliable extrapolation to different sites and environmental conditions. To address this gap, this paper details the development of a physical soiling model for the prediction of the deposition of airborne dust onto the surface of solar collectors and the subsequent loss of performance (i.e. reflectance in the specific case of heliostats). The model inputs are the measured airborne dust concentration and estimated size distribution, the position of the mirrors, and the recorded wind speed and air temperature. The outputs of the model have been validated using experiments performed at the Queensland University of Technology. A reflectometer was used to measure the reflectance of the mirrors on an almost-daily basis while meteorological and dust data were obtained through a dust sensor and a weather station. The agreement between the experimental data and the simulated results demonstrates the effectiveness of the model in dry conditions. For example, for a mirror tilted at 45° with a daily loss of reflectance between 0.5%/day and 2.1%/day, the model reflectance loss predictions have an average relative error of 14%. Furthermore, the visual trends of the model predictions agree well with those observed in the experimental data. Finally, the model is used to investigate scenarios at three candidate sites to assess reflectance losses in different conditions.