Importance of Virtual Sensing and Model Reduction in the Structural Identification of Bolted Assemblies

Creating mathematical models of mechanical structures with bolted joints is still a challenging topic, which is the base of ongoing research efforts to develop methods to characterize and quantify nonlinearities in mechanical structures. An approach to efficiently handle the identification of large nonlinear structural systems using measured data is to formulate the identification problem in a low-dimensional space, that is, nonlinear modal reduced order model (NMROM). This chapter discusses the effects of truncating the order of reduced order model in their prediction accuracy and its impact on the nonlinear model identification process that uses the reduced order model. This chapter also presents results of a virtual sensing strategy that is used alongside to the model reduction to lift the necessity of direct measurement of the degrees of freedom where the active nonlinear element is located. An experimental setup of a bolted joint structure is used to demonstrate the application of reduced order modeling for identification purpose. The results quantify the accuracy of the selected model in predicting global responses of the structure such as amplitude-dependent frequencies and damping ratios.