Thomas Rung

Prof. Dr.-Ing. Thomas Rung is a Professor of Computational Fluid Dynamics at the Hamburg University of Technology (TUHH), where he leads research at the Institute for Fluid Dynamics and Ship Theory (M8). He has been with the university since 2005 and maintains an active research program focusing on advanced computational methods for fluid dynamics problems.

His academic background includes:

• 1993: Dipl. Ing. in Aeronautical Engineering from TU Berlin
• 2000: Dr.-Ing. in Mechanical Engineering from TU Berlin

Prof. Rung's research spans multiple domains within fluid dynamics, with particular expertise in computational methods for engineering applications. His work combines theoretical developments with practical implementations for real-world problems in naval architecture, aerospace engineering, and biomedical applications. He has pioneered approaches using emerging computing paradigms including GPU acceleration and quantum computing for fluid dynamics simulations. His research group develops advanced numerical methods for multiphase flows, shape optimization, and high-fidelity simulations of complex engineering systems.

His recent publications demonstrate a strong focus on integrating machine learning techniques with traditional computational fluid dynamics, exploring quantum computing applications for fluid simulation, and advancing methods for biomedical flow analysis. The research shows consistent innovation in numerical methods while maintaining relevance to practical engineering challenges across multiple industries.

Prof. Rung has supervised numerous research projects related to ship hydrodynamics, vehicle aerodynamics, and urban area simulations. His work has applications in naval architecture, aerospace engineering, and biomedical device design. His research group maintains strong connections with industry partners in transportation and engineering sectors.

At the Institute for Fluid Dynamics and Ship Theory, Prof. Rung leads a research team that utilizes advanced computational facilities including high-performance computing resources for large-scale fluid dynamics simulations. The team develops and applies both mesh-based (FV, FD) and particle-based (LBM, SPH) methods to tackle challenging fluid dynamics problems across multiple scales and applications.