Complex nonlinear and chaotic responses have been observed and demonstrated in various compliant ocean systems characterized by nonlinear mooring restoring force and coupled fluid-structure interaction exciting force. The design of these systems, with inherent high degree of nonlinear dynamics, presents a challenge to the engineer. An experimental mooring system exhibiting nonlinear behavior due to geometric nonlinearity of mooring line angles and the complexity of hydrodynamic excitations is chosen for the study. Three alternative multiple-input/single-output models, nonlinear-structure lincarly-damped (NSLD) model, nonlinear-structure coupled hydrodynamically-damped (NSCHD) model, and nonlinear-structure nonlincarly-damped (NSND) model distinguished by the different inputs and outputs used are derived and Reverse Multiple- Input/Single-Output technique are examined. With the input wave and output system response data known, based on multiple input/single-output linear analysis of reverse dynamic system, the methodology identifies the linear and nonlinear system properties. A detailed study has been conducted on the different reverse dynamic models to identify the most suitable physical representative model for the ocean mooring system considered.
The highly nonlinear responses of compliant ocean structures characterized by a large-geemetry restoring force and a coupled fluid-structure interaction excitation are of great interest to ocean engineers and naval architects. An understanding of the nonlinear responses including coexisting periodic (primary, subharmonic and superharmonic resonance) and aperiodic (quasi-periodic and chaotic) phenomena is essential to ensure sound engineering design and safe operation of these structures. To analyze these nonlinear phenomena, deterministic analysis theories and numerical prediction techniques have been developed for single-point mooring systems (Gottlieb et al 1992), ships (Bishop and Virgin 1988), and multipoint mooring systems (Bernitsas and Chung 1990, Gottlieb and Yim 1993). Stochastic extensions of these analysis techniques have also been developed by Lin and Yim (1996, 1997) to provide guidelines for interpreting field and experimental observations where randomness cannot be neglected.
