Abstract:

Quantitative characterization of surface morphological evolution is critical for coating quality control. A hybrid MC-CFD (i.e., Monte Carlo coupled with computation fluid dynamics) method was developed to capture the complex coating formation process, which involves wet film formation through deposition of huge amount of droplets and the succeeding levelling process. The effects of the mean diameter, viscosity, density and surface tension of paint droplets on coating roughness evolution have been systematically and quantitatively explored. It was found that increasing mean drop diameter led to the increase of the initial surface roughness, hence increased levelling time; the increase of paint viscosity led to the increased initial and final surface roughness and hence increased levelling time; paint density had little influence on levelling time and final surface roughness; increasing surface tension of paint however led to the increase of levelling velocity and the decrease of levelling time, but had little effect on final coating roughness.

Key words: surface morphology, droplet deposition, levelling, Monte Carlo simulation, CFD, two-phase flow

摘要：

表面涂层形貌形成过程的定量描述对涂层质量控制效果的提升至关重要，建立了集成蒙特卡罗（Monte Carlo）和计算流体力学（CFD）的混合方法模拟复杂的海量液滴沉积成液膜及其随后的流平过程，并系统研究油漆液滴平均直径、黏度、密度以及表面张力对涂层表面粗糙度、流平速度以及流平时间的影响。模拟结果表明，油漆液滴平均直径增大，涂层初始表面粗糙度增大，流平速度减小，流平时间延长；黏度增大，涂层的初始和最终表面粗糙度增大，流平速度减小，流平时间延长；密度减小，涂层初始表面粗糙度增大，初始流平速度增大，流平时间稍微缩短，对最终表面粗糙度影响不大；表面张力增大，涂层流平速度增大，流平时间缩短，对涂层最终表面粗糙度影响很小。

关键词: 表面形貌, 液滴沉积, 流平, 蒙特卡罗模拟, 计算流体力学, 两相流