{
  "id": "2309.00876",
  "version": "v1",
  "published": "2023-09-02T09:37:06.000Z",
  "updated": "2023-09-02T09:37:06.000Z",
  "title": "A Multiscale Method for Two-Component, Two-Phase Flow with a Neural Network Surrogate",
  "authors": [
    "Jim Magiera",
    "Christian Rohde"
  ],
  "categories": [
    "math.NA",
    "cs.NA",
    "physics.comp-ph",
    "physics.flu-dyn"
  ],
  "abstract": "Understanding the dynamics of phase boundaries in fluids requires quantitative knowledge about the microscale processes at the interface. We consider the sharp-interface motion of compressible two-component flow, and propose a heterogeneous multiscale method (HMM) to describe the flow fields accurately. The multiscale approach combines a hyperbolic system of balance laws on the continuum scale with molecular-dynamics simulations on the microscale level. Notably, the multiscale approach is necessary to compute the interface dynamics because there is -- at present -- no closed continuum-scale model. The basic HMM relies on a moving-mesh finite-volume method, and has been introduced recently for compressible one-component flow with phase transitions in [Magiera and Rohde, JCP. 469 (2022)]. To overcome the numerical complexity of the molecular-dynamics microscale model a deep neural network is employed as an efficient surrogate model. The entire approach is finally applied to simulate droplet dynamics for argon-methane mixtures in several space-dimensions. Up to our knowledge such compressible two-phase dynamics accounting for microscale phase-change transfer rates have not yet been computed.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-09-02T09:37:06.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "76T10",
      "65Z05",
      "35L65"
    ],
    "keywords": [
      "neural network surrogate",
      "multiscale method",
      "two-phase flow",
      "two-component",
      "microscale phase-change transfer rates"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}