2019-02-19

# Machine Learning for Closure Models in Multiphase-Flow Applications

## Publication

### Publication

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 cheaper, 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 (NN). The NN 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.

Additional Metadata | |
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Eindhoven University of Technology | |

Y. van Halder (Yous) , B. Sanderse (Benjamin) , B. Koren (Barry) , G.J.F. van Heijst (GertJan) | |

Organisation | Scientific Computing |

Buist, J.F.H. (2019, February 19).
Machine Learning for Closure Models in Multiphase-Flow Applications. |