Synergistic optimization analysis of dust cleaning efficiency and power generation enhancement on super-hydrophobic photovoltaic panel for droplets with different Weber numbers

Dust deposition on photovoltaic panels severely degrades power output. The combination of droplets and hydrophobic surfaces can effectively solve this problem. However, the underlying physics of droplet-based cleaning and its quantitative impact on generation remain poorly understood. This study employs an innovative multiphysics framework, integrating computational fluid dynamics with the discrete element method, coupled with a photovoltaic power prediction model that links dust deposition directly to photo-generation physics, to simulate droplet-mediated dust cleaning and its impact on power output. The droplet dynamics are analyzed using a multiphase volume of fluid model, and the dust particle behavior are revealed by Edinburgh elastic-plastic adhesion model. Smaller particles (dp ≤ 100 μm) are readily removed, achieving a dust removal rate of 22.7 % at dp = 50 μm. However, particles that agglomerate due to cohesive forces after droplet cleaning become difficult to remove. Droplet cleaning efficiency correlates with the Weber number. At Weber number = 1.91, both coverage radius and contact frequency reach optimal values, yielding a peak dust removal rate of 14.1 %. Coupling dust deposition density with cleaning efficiency results and inputting them into a photovoltaic power generation model indicates that each 1 g/m2 increase in deposition density causes a maximum power degradation of 2.26 %. Under droplet cleaning conditions with Weber number = 1.91, photovoltaic power significantly increases by 2.9 % under small particle conditions. This study provides theoretical basis and parameter optimization paradigms for self-cleaning design of super-hydrophobic photovoltaic.