Drop spreading and drifting on a spatially heterogeneous film: capturing variability with asymptotics and emulation
A liquid drop spreading over a thin heterogeneous precursor film (such as an inhaled droplet on the mucus-lined wall of a lung airway) will experience perturbations in shape and location as its advancing contact line encounters regions of low or high film viscosity. Prior work on spatially one-dimensional spreading over a precursor film having a random viscosity field (Xu and Jensen, Proc R Soc A 472:20160270, 2016) has demonstrated how viscosity fluctuations are swept into a narrow region behind the drop’s effective contact line, where they can impact drop dynamics. In this paper, we investigate two-dimensional drops, seeking to understand the relationship between the statistical properties of the precursor film and those of the spreading drop. Assuming the precursor film is much thinner than the drop and viscosity fluctuations are weak, we use asymptotic methods to derive explicit predictions for the mean and variance of drop area and the drop’s lateral drift. For larger film variability, we use Gaussian process emulation to estimate the variance of outcomes from a restricted set of simulations. Stochastic drift of the droplet is predicted to be the greatest when the initial drop diameter is comparable to the correlation length of viscosity fluctuations.
KeywordsDrop spreading Gaussian process emulation Surface tension Thin-film flow Uncertainty quantification
This study was supported by EPSRC Grant No. EP/K037145/1. The GP_emu_UQSA package may be downloaded from https://github.com/samcoveney/maGPy.
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