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Friedrich Froehlig

Manager Turbomachinery and Fluid Dynamics, MTU Friedrichshafen

After his studies of mechanical and aeronautical engineering at RWTH Aachen, Dr. Friedrich Fröhlig received the title of Doctor of Mechanical Engineering  / Turbo Machinery at Karlsruhe Institute of Technology (KIT). Since 2011, he covered the position of Assistant Professor at Karlsruhe Institute of Technology (KIT). Currently he works as a Turbo Machinery and Fluid Dynamics ​Manager at MTU Friedrichshafen / Rolls Royce Powersystems.


Setting an efficient workflow for automated Turbomachinery optimization
Methodologies [Optimization]
Plenary Room, Mon, 12/05/2014 - 18:10 - 18:30

With the demand for lower CO2 emissions and sustainable use of energy resources, engines with maximum achievable efficiency are inevitable. High Turbocharger efficiencies are one of the main levers in order to lower fuel consumption. Increases in turbocharger efficiency directly result in a remarkable improvement of the engine’s overall fuel efficiency.

The aim is to set up an efficient workflow for the automated Turbomachinery optimization, using parametric Computer-Aided Design (CAD) techniques, Computational Fluid Dynamics (CFD) instruments and Multi-Objective optimization strategies. The performance of this workflow is then proved with the optimization of a compressor, first considering only the impeller and then including in the optimization even the volute, in order to obtain a combined optimization involving both impeller and volute.

The workflow consists first in building up a complete parametric geometry of the machine and then in setting up a CFD calculation, afterwards validated by comparison with available experimental data. Thus, an optimization software is used to perform the Multi-Objective optimization of the compressor, using Design Of Experiments (DOE) techniques, Correlation Analysis, Optimization Algorithms and Response Surface Methodology (RSM). The main goal is to obtain a higher Isentropic Total-to-Total Efficiency with an increase of Pressure Ratio as secondary objective. The combination of Optimization Algorithms and Response Surface Methodology insures accurate results by reducing, at the same time, the required computational effort.

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