Investigate utilization of machine-learning methods to derive drag models for Euler-Euler simulations from DNS data

Multiphase flow plays a vital role in many industrial applications. DNS simulations provide an insight
into the complexity of multiphase flows but are limited due to very high computational costs. Instead,
Euler-Euler (E-E) simulations provide a reliable prediction for a wide range of engineering applications.
E-E simulations are highly dependent on the choice of closure models for the interaction terms. Modeling
of interfacial drag force is one of the main aspect of E-E simulations. In this thesis an attempt has been
made to develop a drag model for E-E simulations by analyzing the DNS data using machine learning
techniques. The entire work was carried out at HZDR (Helmholtz Zentrum Dresden Rossendorf).