Droplet motion on chemically heterogeneous substrates with mass transfer. II. Three-dimensional dynamics

Groves, Danny; Savva, Nikos

doi:10.1103/physrevfluids.6.123602

Cited by 2 publications

(2 citation statements)

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“…Therefore, in order to mitigate the need for the large computing resources which are required for establishing this proof-of-concept study on contact line dynamics, we have opted to produce datasets by invoking the asymptotic model developed by Savva et al (2019) and Savva and Groves (2021), which reduces equations (1a)-(1d) to a set of evolution equations for the Fourier harmonics of the contact line. The equations are evolved with a typically nonstiff time-stepping algorithm, such that the overall scheme has a reduced computational overhead compared to the full problem (see Savva et al, 2019 for details on the numerical methodology used).…”

Section: Mathematical Modelmentioning

confidence: 99%

“…In this manner, the aim is to improve upon an approximate model, derived from the work of Lacey (1982), with a data-driven component for capturing higher-order corrections. The second approach is inspired by the asymptotic analyses carried out by Savva et al (2019) and Savva and Groves (2021), which have shown that the inclusion of additional terms can indeed considerably improve the agreement with solutions to the governing equation. It is also similar in spirit with other recent contributions, although these were applied in other physical systems and employed different neural network architectures (Wan and Sapsis, 2018; Wan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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AI-assisted modeling of capillary-driven droplet dynamics

Demou,

Savva

2023

DCE

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In this study, we present and assess data-driven approaches for modeling contact line dynamics, using droplet transport on chemically heterogeneous surfaces as a model system. Ground-truth data for training and validation are generated based on long-wave models that are applicable for slow droplet motion with small contact angles, which are known to accurately reproduce the dynamics with minimal computing resources compared to high-fidelity direct numerical simulations. The data-driven models are based on the Fourier neural operator (FNO) and are developed following two different approaches. The first deploys the data-driven method as an iterative neural network architecture, which predicts the future state of the contact line based on a number of previous states. The second approach corrects the time derivative of the contact line by augmenting its low-order asymptotic approximation with a data-driven counterpart, evolving the resulting system using standard time integration methods. The performance of each approach is evaluated in terms of accuracy and generalizability, concluding that the latter approach, although not originally explored within the original contribution on the FNO, outperforms the former.

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Section: Mathematical Modelmentioning

confidence: 99%