Transfer path analyses of vehicle bodies are widely considered as an important tool in the noise, vibration and harshness design process, as they enable the identification of the dominating transfer paths in vibration problems. It is highly beneficial to model uncertain parameters in early development stages in order to account for possible variations on the final component design. Therefore, parameter studies are conducted in order to account for the sensitivities of the transfer paths with respect to the varying input parameters of the chassis components. To date, these studies are mainly conducted by performing sampling-based finite element simulations. In the scope of a sensitivity analysis or parameter studies, however, a large amount of large-scale finite element simulations is required, which leads to extremely high computational costs and time expenses. This contribution presents a method to drastically reduce the computational burden of typical sampling-based simulations. For this purpose, Gaussian processes are introduced to find a probabilistic function approximation of the transfer paths. Initial results reveal that a wider solution space can be explored by only observing a few transfer path samples. This entails a time-efficient and robust technique, which inherently incorporates the variability of the input parameters. As such, Gaussian processes offer a versatile solution strategy for transfer path analyses, where simulation data as well as experimental measurements can be holistically investigated.
