Bounce or coalescence : a physical learning frame
J. H. Xu, Z. L. Wang

TL;DR
This paper introduces a physics-guided machine learning framework for simulating droplet coalescence and bouncing, effectively handling interface interactions and transitions with high adaptability.
Contribution
It develops a unified simulation framework that models coalescence and bouncing using multiple volume-of-fluid fields and machine learning, reducing empirical dependencies.
Findings
Framework accurately reproduces droplet coalescence and bouncing under various conditions.
Simulation results align well with experimental observations.
Captures complete bouncing and coalescence sequences in a single run.
Abstract
In this study, we develop an interface-contact simulation framework based on physical criteria and machine-learning-assisted classification to describe coalescence and bouncing within a unified formulation. The framework realizes interfacial coalescence and bouncing through the fusion and generation of multiple volume-of-fluid fields. When adjacent interfaces are predicted to coalesce, multiple VOF fields are collapsed into a single VoF field. When approaching interfaces are predicted to bounce, a single VOF field is regenerated into multiple VOF fields, allowing the interfaces to continue evolving independently. With this treatment, the difficulties associated with topological transition, regime-map identification, increasing computational demand, and stochastic behavior during interfacial approach are separated from the interface-tracking procedure. These decisions are instead…
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