Abstract—The design of mechatronic systems relies more and more on models that are used to predict the performance in a virtual environment. The models involved are increasingly more complex multiphysical systems. Instead of spending more time modeling on increasingly detailed physical models, uncertainty can be explicitly considered to model the lack of knowledge. The mismatch between real life experiments and model simulations due to parametric uncertainty can be quantified by a likelihood estimation and Monte-Carlo sampling techniques for propagation. This paper proposes to significantly accelerate the process by using Polynomial Chaos Expansions for the propagation and a Genetic Algorithm to maximize likelihood. The soundness of the approach is demonstrated on a wet friction clutch system. The results show the method has a strong potential of scalability with respect to the number of uncertain parameters.
Index Terms—Polynomial chaos expansions, system identification, uncertainty quantification, wet clutch.
Georges Tod, Bruno Depraetere and Suzanne Van Poppel are with in 1CoDesigns and 2DecisionS at Flanders Make, Belgium (e-mail: georges.tod@ flandersmake.be).
Wannes De Groote, Tom Lefebvre, Nele De Geeter and Guillaume Crevecoeur are with 3EEMMeCS, Ghent University and 4EEDT Decision & Control, Flanders Make, Belgium (e-mail: wannes.degroote@ugent.be).
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Cite: Georges Tod, Wannes De Groote, Tom Lefebvre, Nele De Geeter, Bruno Depraetere, and Guillaume Crevecoeur, "Parametric Uncertainty Quantification Using Polynomial Chaos Expansions Applied to a Wet Friction Clutch Model," International Journal of Modeling and Optimization vol. 9, no. 4, pp. 185-191, 2019.
