### Samenvatting

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.

Originele taal-2 | Engels |
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Titel | Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 |

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

Uitgeverij | National Technical University of Athens |

Pagina's | 379-399 |

Aantal pagina's | 21 |

ISBN van geprinte versie | 9786188284494 |

DOI's | |

Status | Gepubliceerd - 2019 |

Evenement | 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 - Crete, Griekenland Duur: 24 jun 2019 → 26 jun 2019 |

### Congres

Congres | 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 |
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Land | Griekenland |

Stad | Crete |

Periode | 24/06/19 → 26/06/19 |

## Vingerafdruk Duik in de onderzoeksthema's van 'Machine learning for closure models in multiphase flowapplications'. Samen vormen ze een unieke vingerafdruk.

## Citeer dit

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