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Efficient Component Reductions in a Large-Scale Flexible Multibody Model

Journal Article
ISSN: 2380-2162, e-ISSN: 2380-2170
Published March 05, 2018 by SAE International in United States
Efficient Component Reductions in a Large-Scale Flexible Multibody Model
Citation: Andersson, N. and Abrahamsson, T., "Efficient Component Reductions in a Large-Scale Flexible Multibody Model," SAE Int. J. Veh. Dyn., Stab., and NVH 2(1):5-26, 2018,
Language: English


To make better use of simulations in the automotive driveline design process there is a need for both improved predictive capabilities of typical system models and increased number of variant evaluations carried out during system concept design phase. A previously developed large-scale multibody rotor dynamical powertrain model that combines detailed linear-elastic finite element components and nonlinear joints is used to more accurately simulate system response modes and their variations across the operating-range. However, the total simulation time is too long to include extensive parameter evaluations during the rapid design iterations, which will have a negative influence on the total understanding of the designed system's behaviour. Therefore this article is about reducing such a large-scale model to one that runs faster, but without losing the ability to predict the most fundamental system characteristics. Reduction methods considering defined stimuli-response relations are well established and used within the field of control systems, to balance prediction accuracy and evaluation effort, but are not yet commonly applied to large-scaled structural models and analysis of vibrations in continuous and lightly damped structures. Here, an implementation of two such state-space reduction methods into a common computational software workflow is described and their overall efficiency is compared to standard methods. Reductions are applied to two major structural components of the powertrain model. Steady-state simulations are performed for multiple engine speeds and responses related to vehicle noise and vibrations are compared using a quantitative error metric. The prediction accuracy, reduction and response simulation times of different model orders are evaluated, as well as the corresponding mode frequency spectra.