Experimental investigation of droplet impact on inclined superhydrophobic surfaces for enhanced self-cleaning

Abstract

Dust accumulation on solar photovoltaic (SPV) panels is a pestering issue in SPV technology. Therefore, panel-cleaning has become increasingly significant to improve the system performance. Traditional panel-cleaning methods rely heavily on water-based cleansing, which entail large water footprints. Self-cleaning of these surfaces through optimal usage of water is a promising alternative, which relies on developing super-hydrophobic (SHPB) panels on which a measured quantity of water is sprayed to achieve the maximum possible liquid–solid contact area. Droplets generated from such spray would impact on the inclined  SHPB surface, where they spread and slide due to the combined action of droplet momentum and gravity, and pick-up the surface-dust before rebounding from the surface. Herein, we experimentally analyze this attribute in-depth, surpassing what is existing in the literature, particularly in the context of self-cleaning of inclined SHPB surfaces. We augment the traditional definition of maximum spreading factor by introducing a new droplet-sweeping parameter—integrated sweeping factor—based on the total liquid–solid contact area arising out of the simultaneous spreading and sliding of the droplet. Effects of the impact Weber number and surface inclination on instantaneous spreading and integrated sweeping behaviour of the droplet are characterized to identify the extent of self-cleaning. In addition, shape of the droplet-surface contact area along with their lateral and longitudinal spreads, and the contact time are characterized. Suitable correlations are developed based on regression analysis of the experimental data. Findings from the study are identified to be relevant for designing a nozzle array system for effective self-cleaning.