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Understanding the dynamics of phase boundaries in fluids requiresquantitative knowledge about the microscale processes at the interface. Weconsider the sharp-interface motion of compressible two-component flow, andpropose a heterogeneous multiscale method (HMM) to describe the flow fieldsaccurately. The multiscale approach combines a hyperbolic system of balancelaws on the continuum scale with molecular-dynamics simulations on themicroscale level. Notably, the multiscale approach is necessary to compute theinterface dynamics because there is -- at present -- no closed continuum-scalemodel. The basic HMM relies on a moving-mesh finite-volume method, and has beenintroduced recently for compressible one-component flow with phase transitionsin [Magiera and Rohde, JCP. 469 (2022)]. To overcome the numerical complexityof the molecular-dynamics microscale model a deep neural network is employed asan efficient surrogate model. The entire approach is finally applied tosimulate droplet dynamics for argon-methane mixtures in severalspace-dimensions. Up to our knowledge such compressible two-phase dynamicsaccounting for microscale phase-change transfer rates have not yet beencomputed.

A Multiscale Method for Two-Component, Two-Phase Flow with a Neural Network Surrogate