Alexander Rieß

Model Order Reduction Based Simulation and Optimization of Large Bore Internal Combustion Engines


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ISBN:

978-3-8440-3695-4

Series:

Industriemathematik und Angewandte Mathematik

Keywords:

Bearing; Connecting Rod; Crankshaft; Cranktrain; EHD; Elastohydrodynamics; FEM; Finite Element Method; Friction; Combustion Engine; MOR; Model Order Reduction; MBS; Multibody Simulation; Optimization; POD; Proper Orthogonal Decomposition; Simulation; Wear

Type of publication:

Thesis

Language:

English

Pages:

202 pages

Figures:

79 figures

Weight:

303 g

Format:

21 x 14,8 cm

Binding:

Paperback

Price:

49,80 € / 62,25 SFr

Published:

June 2015

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Abstract:

This thesis is intended for computational engineers and mathematicians who work on multibody simulation, model order reduction, tribology and virtual internal combustion engines. Interconnected modules are used within a simulation workflow to describe a large bore internal combustion engine. The modules are responsible for the generation of finite element discretized mechanical models, model order reduction, and the elastohydrodynamic simulation of flexible multibody systems. The theoretical background of the different modules is presented, and it is shown how to connect them to represent an internal combustion engine. To reduce the computational effort, an optimal scheduling of the modules by a genetic algorithm is investigated. Applications of the simulation workflow are discussed for the 4-stroke diesel engine 20V28/33D and the spark ignited 4-stroke gas engine 20V35/44G. Different convergence analyses, numerical experiments, and optimizations are performed. Algorithms and recommendations for an efficient model order reduction and multibody simulation are proposed. This includes an optimal configuration of the integrator, use of the proper orthogonal decomposition method, parameter dependent model order reduction, time varying model order reduction, a proper orthogonal decomposition based wear simulation, and an efficient gradient calculation of the mean friction loss based on the adjoint approach. Finally, two friction loss optimizations of the running-in process and operational stage are performed.