Higher-order FRFs and their applications to the identifications of continuous structural systems with discrete localized nonlinearities

Many modern structural systems are found to contain localized areas, often around structural joints and boundaries, where the actual dynamic behavior is far from linear. Such nonlinearities need to be properly identified so that they can be incorporated into the improved mathematical model used for design and operation. One approach to this task is to use higher-order frequency response functions (FRFs). Identification of structural nonlinearities using higher-order FRFs is currently an active and growing emerging research area and to date, much research has been conducted. However, most existing research seems to be confined to very simple nonlinear system models such as SDOF mass-spring-damper models or to the most, MDOF mass-spring chain models. For more general continuous nonlinear structures with discrete localized nonlinearities however, analytical derivation of higher-order FRFs remains unknown and as a result, identification of nonlinearities of such systems using measured higher-order FRFs becomes impossible to achieve. This missing link between higher-order FRFs and physical parameters of nonlinearities of continuous structures has to be established before any real progress can be possibly made. In this paper, a new novel method is developed which can be used to derive analytical higher-order FRFs of continuous structural systems with discrete localized nonlinearities. The method is generally applicable and theoretically exact, and it serves exactly as that missing link. Having established analytical higher-order FRFs which serve as the theoretical foundation for any subsequent identification, method of parameter identification of nonlinearity is then further developed. Important characteristics of higher-order FRFs are discussed, some of which are revealed the first time since there has never been higher-order FRFs derived from nonlinear continuous structures. Various numerical aspects on how to improve accuracy of identified nonlinear system parameters are discussed.