Headline
PhD Position: "Machine Learning based CFD of Point Particles in Turbulence (self-funded applicants)"
The accurate representation of unresolved point particles remains a fundamental challenge in multiphase flow simulations. Traditional models often rely on empirical assumptions that limit predictive capabilities, especially in complex or turbulent environments. This PhD project aims to revolutionize particle-fluid interaction modeling by leveraging advanced machine learning techniques in CFD. By analyzing high-fidelity simulation data and experimental datasets, the project will develop data-driven models that can accurately predict particle forces, trajectories, and interaction dynamics within the fluid without relying solely on simplified closures ().
