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Technical Paper

Uncertainty Quantification in Vibroacoustic Analysis of a Vehicle Body Using Generalized Polynomial Chaos Expansion

In order to perform reliable vibroacoustic predictions in the early design phase, it is essential to include uncertainties in the simulation process. In this contribution, uncertainties are quantified using the generalized Polynomial Chaos (gPC) expansion in combination with a Finite Element (FE) model of a vehicle body in white. The objective is to particularly investigate the applicability of the gPC method in the industrial context with a high number of uncertain parameters and computationally expensive models. A non-intrusive gPC expansion of first and second order is implemented and the approximation of a stochastic response process is compared to a Latin Hypercube Sampling (LHS) based reference solution with special regard to accuracy and computational efficiency. Furthermore, the method is examined for other input distributions and transferred to other FE models in order to verify the applicability of the gPC method in practical applications.
Technical Paper

Challenges in Vibroacoustic Vehicle Body Simulation Including Uncertainties

For many years, the model quality and frequency range of NVH simulation with Finite Element (FE) models have been increased and led to a better vehicle quality. Nowadays, model range and quality are on such a high fidelity and there is often no further improvement, even with extreme modelling and computation effort. So in order to improve the quality of predictions, the next step is to take uncertainties into account. With this approach there are many challenges on the way to valid and useful simulation models and they can be divided into three areas: the input uncertainties, the propagation of uncertainties through the FE model and finally the statistical output quantities. Each of them must be investigated to choose sufficient methods for a valid and fast prediction of vehicle body vibroacoustics. With a discrimination of different types of uncertainties it can be shown that the dimensionality of the corresponding random space is tremendously high.