Evaluating Degree of Freedom Selection Methods for MIMO Vibration Modeling

Multiple-input multiple-output (MIMO) vibration testing techniques offer the ability to closely replicate field environments during ground testing in a cost-effective manner, often with significant time savings compared to single-axis methods. MIMO techniques have been successfully applied to vibration testing for aerospace applications, yet they are also useful for providing inputs for the model validation process. However, choosing appropriate sensor locations and degrees of freedom (DOF) is an important but challenging aspect of the MIMO approach. This work highlights the application of MIMO techniques to perform model validation for an example aerospace system using synthetic test data. An inverse approach is utilized to perform input estimation, with frequency response functions and simulated acceleration response data from a finite element model. To assess the quality of the input estimation, several selection techniques for MIMO are evaluated, including those based on effective independence, optimal experimental design, modal projection error, and an iterative approach based on mean-squared error. This work demonstrates the development of a workflow for model validation of an aerospace system using the application of MIMO techniques, emphasizing the importance of control DOF selection and showcasing advantages over single-axis methods.