A System Level Model Reduction Approach for Flexible Multibody Systems with Parametric Uncertainties.

Stochastic analysis of flexible multibody system for uncertain parameters typically requires a large number of simulation runs for example for Monte-Carlo simulation. However, as the computational load of a regular flexible multibody model is typically rather high, this is often infeasible. A solution to this high computational load is model reduction, but regular model reduction approaches for flexible multibody simulation do not maintain the parameter dependency. This leads to a new model reduction for each parameter which also leads to high computational costs. The current work presents a novel system level model reduction technique for parameterized flexible multibody simulation. The proposed approach is a parameterized version of the Global Modal Parameterization method. In this approach a system level model reduction of the flexible mechanism is performed in which a configuration dependent projection space is used. For the parameterized approach, affine parameter dependence is assumed. In this case the parameter dependency can be externalized and is exactly preserved through the model reduction. The accuracy of the proposed approach is demonstrated through a numerical validation. The model is used for a Monte-Carlo simulation of mechanism with uncertain parameters and delivers accurate probabilistic distributions for the motion of the mechanisms at a highly reduced cost compared to the original model. The proposed approach is shown to provide reliable results with a computational load which is reduced from days to hours.