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Numerical Analysis of the Influence Patterns of Random Rough Surface Skewness and Kurtosis on Droplet Bouncing. | LitMetric

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Article Abstract

In engineering applications where extreme environmental conditions are becoming increasingly prevalent, the dynamic behavior of liquid droplets on solid surfaces plays a vital role in determining system efficiency and reliability. Particularly in scenarios such as anti-icing, anticorrosion, and self-cleaning, the fabrication of micro/nanostructured surfaces with exceptional hydrophobic properties has emerged as a critical strategy. However, constrained by the technical limitations of current experimental equipment in microscale observation and the capture of transient droplet impact processes, the influence mechanism of statistical roughness parameters (skewness and kurtosis) on droplet bouncing remains underexplored. This study develops a rough surface model with controllable skewness and kurtosis using Fast Fourier Transform (FFT) and Pearson distribution transformation. The roughness model is applied as a boundary condition in a gas-liquid two-phase computational framework. Additionally, an equivalent analytical expression is introduced in CFD-Post to quantitatively extract the contact volume fraction during the spreading phase. This approach enables a systematic investigation of how statistical surface features affect droplet spreading, retraction, and detachment. Results show that, compared to a smooth surface, roughness reduces droplet contact time by up to 16.15%. Except in cases where skewness is zero and kurtosis exceeds 3, an inverse correlation is found between contact volume fraction and contact time for most parameter combinations, indicating that energy dissipation is mainly governed by the pinning effect of sharp asperities. When parameters reach extreme values, rebound is fully suppressed and spreading diminishes significantly. In contrast, kurtosis between 3 and 3.5 and skewness around ±0.2 enhance bouncing. These findings provide a theoretical basis and quantitative reference for optimizing hydrophobic microtexture design and surface postprocessing.

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http://dx.doi.org/10.1021/acs.langmuir.5c02814DOI Listing

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