During the last decades, big steps have been taken towards a realistic simulation of NVH (Noise Vibration Harshness) behavior of vehicles using the Finite Element (FE) method. The quality of these computation models has been substantially increased and the accessible frequency range has been widened. Nevertheless, to perform a reliable prediction of the vehicle vibroacoustic behavior, the consideration of uncertainties is crucial.
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.
It can be shown by rough estimation that dimensionality of the corresponding random space for different types of uncertainty is tremendously high. Therefore, a substantial reduction of the dimensionality is crucial.
Next important step is to choose a proper method to model uncertainties and include them in the FE model. Here, many different methods are available: From well-known sampling based methods like Monte-Carlo to more sophisticated spectral methods like generalized Polynomial Chaos.
Finally, the output of these simulations is not a single deterministic value but rather completely new results like mean value, variance and probability distribution. Therefore, the mindset has to change from comparing single deterministic curves and values to an evaluation of stochastic quantities and their relations. This new kind of output requires dealing with new demands as well as a new mindset from simulation engineers.