Stochastic dynamics and fatigue analysis of large-scale mechanical models using multilevel substructuring

Abstract

An efficient methodology is presented for predicting dynamic response and fatigue life of large-scale nonlinear mechanical models, subjected to random excitation. The methodology developed is based on a combination of techniques leading to a fast and accurate determination of the dynamic response with a method related to an efficient prediction of fatigue life. Specifically, the first step involves application of an appropriate coordinate transformation, causing a drastic reduction in the degrees of freedom. This opens the way to the application of another numerical method, leading to direct determination of periodic steady-state response of nonlinear systems under periodic forcing. This approach provides a solid foundation for the subsequent application of a rainflow stress cycle counting method, leading to prediction of fatigue failure. The computational accuracy and effectiveness of the methodology is illustrated by a quite involved example model, representing a city bus subjected to road excitation. Typical results are presented for both the stochastic response and the fatigue life by considering excitation arising from selected road profiles with known statistical properties. Special attention is paid to assessing the effect of the nonlinearity, by considering profiles of different quality. Moreover, a critical comparison is performed on results referring to the expected fatigue lifetime, obtained by applying methods in both the frequency and the time domain. In this way, the applicability of classical spectral methods in nonlinear models is also investigated.