13th European Conference on Turbomachinery Fluid dynamics & Thermodynamics

Paper ID:

ETC2019-263

Main Topic:

Optimization

Authors

Nicolas Lachenmaier - MTU Friedrichshafen GmbH
Daniel Baumgärtner - Technical University of Munich
Heinz-Peter Schiffer - Technical University of Darmstadt
Johannes Kech - MTU Friedrichshafen GmbH

Abstract

A turbocharger's radial turbine has a strong impact on the fuel consumption and transient response of internal combustion engines. This paper summarizes the efforts to design a new radial turbine aiming at high efficiency and low inertia by applying two different optimization techniques to a parametrized CAD model. The first workflow wraps 3D fluid and solid simulations within a meta-model assisted genetic algorithm to find an efficient turbine subjected to several constraints. In the next step, the chosen turbine is re-parametrized and fed into the second workflow which makes use of a steepest descent algorithm to further fine-tune the design. This requires the computation of gradients with respect to the CAD parametrization, which is done by calculating and combining surface sensitivities and design velocities. Both methods are applied successfully, i.e. the first delivers a well performing turbine, which, by the second method, is further improved by 0.34% in efficiency.







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