### Abstract

Multiphase flows are described by the multiphase Navier-Stokes equations. Numerically solving these equations is computationally expensive, and performing many simulations for the purpose of design, optimization and uncertainty quantification is often prohibitively expensive. A simplified model, the so-called two-fluid model, can be derived from a spatial averaging process. The averaging process introduces a closure problem, which is represented by unknown friction terms in the two-fluid model. Correctly modeling these friction terms is a long-standing problem in two-fluid model development. In this work we take a new approach, and learn the closure terms in the two-fluid model from a set of unsteady high-fidelity simulations conducted with the open source code Gerris. These form the training data for a neural network. The neural network provides a functional relation between the two-fluid model's resolved quantities and the closure terms, which are added as source terms to the two-fluid model. With the addition of the locally defined interfacial slope as an input to the closure terms, the trained two-fluid model reproduces the dynamic behavior of high fidelity simulations better than the two-fluid model using a conventional set of closure terms.

Original language | English |
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Title of host publication | Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 |

Editors | M. Papadrakakis, V. Papadopoulos, G. Stefanou |

Publisher | National Technical University of Athens |

Pages | 379-399 |

Number of pages | 21 |

ISBN (Print) | 9786188284494 |

DOIs | |

Publication status | Published - 2019 |

Event | 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 - Crete, Greece Duration: 24 Jun 2019 → 26 Jun 2019 |

### Conference

Conference | 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 |
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Country | Greece |

City | Crete |

Period | 24/06/19 → 26/06/19 |

### Keywords

- Closure Terms
- Machine Learning
- Multiphase Flow
- Two-Fluid Model

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## Cite this

*Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019*(pp. 379-399). National Technical University of Athens. https://doi.org/10.7712/120219.6348.18409