Parametric Uncertainty Quantification Using Polynomial Chaos Expansions Applied to a Wet Friction Clutch Model

Abstract

Recently, designing mechatronic systems has become more and more dependent on models that are used to predict performance in a virtual environment, and the models involved are becoming increasingly more complex multiphysical systems. Instead of spending much time modeling increasingly detailed physical models, uncertainties can be explicitly considered to model the lack of knowledge. The mismatch between real-life experiments and model simulations due to parametric uncertainties can be quantified using likelihood estimation and Monte Carlo sampling techniques for propagation. In this paper, we attempt to significantly accelerate the process using polynomial chaos expansions for propagation and a genetic algorithm to maximize likelihood. The soundness of this approach is demonstrated on a wet friction clutch system. The results show that the method has a strong potential for scalability with respect to the number of uncertain parameters.

Inverse parametric uncertainty identification using polynomial chaos and high-order moment matching benchmarked on a wet friction clutch

“…The detailed pressure dynamics are out of scope and we refer to prior research for more information [2,29]. During the filling phase the plates do not make contact.…”

“…The exact model identification is not in the scope of this paper and a general overview is given. We refer to [29,30,5,2] for further reading. Figure 9 illustrates the simplified dynamical scheme of the wet clutch system.…”