Philipp Schlatter

Philipp Schlatter is a Professor in the Department of Mechanics at KTH Royal Institute of Technology. His research focuses on fluid mechanics, turbulence, and computational fluid dynamics (CFD), with expertise in high-performance computing and direct numerical simulations (DNS). He leads projects involving scalable CFD frameworks like Neko and Nek5000, and investigates turbulent boundary layers, flow control, and coherent flow structures. His work includes experimental and numerical studies of wing profiles, rotating systems, and transition dynamics. Schlatter teaches courses on computational fluid dynamics and turbulence, emphasizing both theoretical and practical aspects of fluid mechanics.

Key research interests include developing numerical methods for high-fidelity simulations, understanding turbulence mechanisms, and optimizing flow control strategies. His contributions span aerodynamics, heat transfer, and the application of machine learning to fluid dynamics problems. Schlatter collaborates extensively on interdisciplinary projects, leveraging advanced computing resources to address complex fluid flow phenomena.

Publications highlight advancements in DNS frameworks, Bayesian optimization for flow control, and analysis of turbulent structures in pipe and boundary layer flows. His research also addresses challenges in measurement techniques and uncertainty quantification in CFD simulations.