Johannes Stemick M.Sc.
Advisor / Co. Advisor
Prof. Grepl / Prof. Dahmen
Model Reduction and Multiscale Modeling
AICES Graduate School
RWTH Aachen University
Phone: + 49 (0) 241 80 99 147
Since 08/2012 Doctoral student at AICES Graduate School, RWTH Aachen University, Germany
10/2006 - 08/2012 Student of Mathematics at RWTH Aachen University, Germany
Since 10/2008 Student of Physics at RWTH Aachen University, Germany
Multiscale Modeling concerns itself with the efficient approximation of partial differential equation (PDE) systems that show scale separation. Typically, in such PDE systems one is interested mostly in the macroscopic behavior of its variables. Unfortunately the -- many order of magnitudes smaller -- micro scale can not simply be ignored without a large margin of error. This causes standard Finite Elements modeling processes to quickly reach their limits both in computation time and required memory space. In Multiscale Modeling one seperates both micro and macro scale into individual equation systems that can be solved efficiently by standard means. Here the macro scale model typically depends on the result of the micro scale model, which in turn may depend on the macro scale model solution.
Other interests: Flow Problems, Reduced Basis Method
One of the main goals in aeroplane design is drag reduction. Reducing drag during mid-flight increases travel time and reduces fuel consumption, reducing overall cost. Within water nature already offers an efficient method of drag reduction, the so called riblet structures. Due to their microscopic nature riblet structures typically result in equation systems that exhibit multiscale behavior. While riblets serve as effective passive means of drag reduction within water, experiments suggest that they do not help much within air. On the other hand, active drag reduction methods show promising results. The goal of this project is to develop mathematical methods that handle well active drag reduction techniques within water. To this end a combination of Multiscale Modeling and the Reduced Basis Method will be considered.