Predictive modeling of droplet dust removal on superhydrophobic surface based on dynamic contact mechanics for photovoltaic application

This study aims to introduce a novel and sustainable water-saving dust removal method for photovoltaic power stations in arid regions, which is essential for maintaining the efficiency of renewable energy sources. Leveraging insights from the contact mechanics behavior of droplets and dust particles on superhydrophobic surfaces, a prediction model for the amount of droplet dust removal is established by incorporating (i) the mechanical characteristics of the dynamic wrapping of dust particles by droplets, (ii) the judgment parameters used to determine the state of droplet motion. Besides, the experimental platform is set up to verify the model. Using the model predicts the dust removal amount with 50 μL droplet to clean the superhydrophobic surface with size of 200 mm´200 mm. For dust accumulation densities of 10.27 g/m2, 21.19 g/m2, and 30.77 g/m2, the prediction results show that the dust removal amounts are 4.2 mg, 8.8 mg, and 12.9 mg. The relative error of simulated and experimental results is within 22%, affirming high prediction accuracy. Furthermore, an analysis of water consumption associated with dust removal using different droplet volumes reveals smaller droplets offer enhanced cleaning efficiency. Thus, the predictive model serves as valuable guidance for implementing of droplet cleaning method on superhydrophobic surfaces of photovoltaic modules.