Identifying Nonlinear Parameters for Reduced Order Models. Part II, Validation using Experimental Data

Assembling nonlinear dynamic models of structures is the goal of numerous research and development organizations. Such a predictive capability is required in the development of advanced, high-performance aircraft structures. Specifically, the ability to predict the response of complex structures to aero-acoustic loading has long been a United States Air Force goal. Sonic fatigue has plagued the Air Force since the advent and adoption of the aircraft turbine engine. While the problem has historically been a maintenance one, predicting dynamic response is crucial for future aerospace vehicles. Decades have been spent investigating the dynamic response and untimely failures of aircraft structures, yet little work has been accomplished towards developing practical nonlinear prediction methods. The aim of this paper and a companion one is to present a novel means of assembling nonlinear reduced order models using experimental data and an analytical basis. The companion paper, Part I, outlines a unique extension of a recently introduced nonlinear identification method; Nonlinear Identification through Feedback of the Outputs (NIFO). This paper, Part II, details a high-fidelity experiment and the resulting successful identification conducted on a well characterized clamped-clamped beam subjected to broadband random loading. Geometric nonlinear parameters were identified for a multiple degree-of-freedom (MDOF) nonlinear reduced order model. The assembled MDOF nonlinear model was used to successfully predict the experimental response of the beam to another loading condition. Beam response spectra and displacements from the prediction model also compare well with the experimental results.