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This work presents a novel technique for nonlinear system identification that operates in the frequency domain and fits a model to measured spectra to estimate the parameters in a modal domain nonlinear equation of motion (EOM). Nonlinear terms are added to the linear EOM in the form of polynomials, and the proposed algorithm estimates the polynomial coefficients as well as the underlying linear Frequency Response Function (FRF). This method is an extension to a popular nonlinear system identification algorithm called NIFO, from Nonlinear Identification through Feedback of the Outputs. However, NIFO identifies the nonlinear parameters as complex numbers that may be different at each frequency line, even though the mechanical system is expected to be governed by an EOM in which the nonlinear parameters are real and constant with frequency. This might be problematic, because any variation in the identified nonlinear parameters will distort the linear FRFs estimated by NIFO, and those linear FRFs are important to tell the user whether all of the significant nonlinearity has been extracted from the system. The proposed algorithm, here dubbed Nonlinear Identification through eXtended Outputs (NIXO), estimates the nonlinear parameters as frequency-independent and real. Additionally, it is demonstrated that for the systems studied here that the algorithm works when random and swept-sine inputs are used to excite the tested structure, while NIFO only worked well when random inputs were used. The method is first evaluated numerically using benchmark case studies, starting with the SDOF equation and then reduced models of a clamped-clamped flat beam, and the results are compared to those obtained with NIFO. Then the algorithm is applied to swept-sine measurements from a 3D-printed flat beam and the results are validated by computing the primary nonlinear normal mode of the identified model and comparing it with measurements. [ABSTRACT FROM AUTHOR]

Finite element models can be used to model and predict the hysteresis and energy dissipation exhibited by nonlinear joints in structures. As a result of the nonlinearity, the frequency and damping of a mode is dependent on excitation amplitude, and when the modes remain uncoupled, quasi-static modal analysis has been shown to efficiently predict this behavior. However, in some cases the modes have been observed to couple such that the frequency and damping of one mode is dependent on the amplitude of other modes. To model the interactions between modes, one must integrate the dynamic equations in time, which is several orders of magnitude more expensive than quasi-static analysis. This work explores an alternative where quasi-static forces are applied in the shapes of two or more modes of vibration simultaneously, and the resulting load–displacement curves are used to deduce the effect of other modes on the effective frequency and damping of the mode in question. This methodology is demonstrated on a simple 2D cantilever beam structure with a single bolted joint which exhibits micro-slip nonlinearity over a range of vibration amplitudes. The predicted frequency and damping are compared with those extracted from a few expensive dynamic simulations of the structure, showing that the quasi-static approach produces reasonable albeit highly conservative bounds on the observed dynamics. This framework is also demonstrated on a 3D structure where dynamic simulations are infeasible. [ABSTRACT FROM AUTHOR]

Abstract: The authors recently presented a new nonlinear system identification method, here dubbed the NL-LTP method, in which the system of interest is forced harmonically so that it responds in a stable periodic orbit, and then it is perturbed slightly and its response is recorded as it returns to the orbit. Under mild assumptions the response about the periodic orbit can be approximated using a linear time periodic system model, which can be identified from the measurements using techniques that are akin to linear modal analysis. While the prior work focused on simulated measurements from single degree-of-freedom systems, this work presents several tools that are needed in order to use this approach on multi-degree-of-freedom systems and focuses on applying the method to experimental hardware. The proposed system identification methodology is unique in that it identifies both the order of the nonlinear system and a mathematical model for the nonlinear restoring forces without assuming the mathematical form for the nonlinearities a priori. Towards these ends, this work explains how to extract the underlying nonlinear system model, or nonlinear restoring force versus displacement relationships, from the time periodic model that governs deviations of the system from its periodic orbit, and presents various metrics that can be used to determine which terms in the model are meaningful. These new tools are used to apply the identification method to a continuous, multi-degree-of-freedom structure with a discrete geometric nonlinearity, using both simulated and experimental measurements. The experimental hardware consists of a cantilever beam with a nonlinear spring attached to its tip, which is driven in a periodic limit cycle by an electromagnetic shaker. [Copyright &y& Elsevier]

Abstract: This work presents a new method for identifying models of nonlinear systems from experimental measurements. The system is first forced to oscillate in stable periodic orbit, and then a small impulsive disturbance force is used to perturb the system slightly from that orbit. One then measures the response until the system returns to the periodic orbit. If the nonlinearities in the system are sufficiently smooth and the perturbation from the periodic orbit is sufficiently small, then one can linearize the perturbed response about the periodic orbit and approximate the system as linear time periodic. One of a variety of methods can then be used to extract the time varying modal model of the system from the response. The extracted modes can be used to construct a time periodic state transition matrix and state coefficient matrix, which describe the system''s nonlinear dynamics over a range of the states. The resulting model for the nonlinear system encompasses that portion of the state space is traversed by the system during its periodic orbit. [Copyright &y& Elsevier]

Coulomb sliders are used widely in order to model frictional interfaces. In many applications, the nonlinear Frequency Response Functions (FRFs) of these systems is desired, but it is extremely computationally expensive to use time-domain integration methods to compute the steady-state response. The nonlinear force and stiffness of these devices depends on the position of sliders, and the implicit nature of the slider positions makes these systems hysteretic and therefore non-trivial to address using conventional continuation methods. This paper proposes a novel numerical method, dubbed "Hysteresis Identification via Reversal Points or (HIRP)" that can compute the steady-state harmonically forced response of frictional nonlinear systems. For this purpose, a quasi-static algorithm is introduced which can evaluate the state of sliders at any time based on the reversal points over a period. This reduces the number of state variables needed in the continuation routine by at least an order of magnitude. The method has been tested by computing the harmonic response of a hysteretic system with multiple Coulomb sliders. The method is evaluated on multi-degree of freedom systems with Iwan joints, demonstrating that the method can be a valuable tool for predicting the nonlinear, steady-state response of hysteretic systems. [ABSTRACT FROM AUTHOR]

Structures with mechanical joints are difficult to model accurately. Even if the natural frequencies of the system remain essentially constant, the damping introduced by the joints is often observed to change dramatically with amplitude. Although models for individual joints have been employed with some success, accurately modeling a structure with many joints remains a significant obstacle. To this end, Segalman proposed a modal Iwan model, which simplifies the analysis by modeling a system with a linear superposition of weakly-nonlinear, uncoupled single degree-of-freedom systems or modes. Given a simulation model with discrete joints, one can identify the model for each mode by selectively exciting each mode one at a time and observing how the transient response decays. However, in the environment of interest several modes may be excited simultaneously, such as in an experiment when an impulse is applied at a discrete point. In this work, the modal Iwan model framework is assessed numerically to understand how well it captures the dynamic response of typical structures with joints when they are excited with impulsive forces applied at point locations. This is done by comparing the effective natural frequency and modal damping of the uncoupled modal models with those of truth models that include nonlinear modal coupling. These concepts are explored for two structures, a simple spring-mass system and a finite element model of a beam, both of which contain physical Iwan elements to model joint nonlinearity. The results show that modal Iwan models can effectively capture the variations in frequency and damping with amplitude, which, for damping, can increase by as much as two orders of magnitude in the microslip regime. However, even in the microslip regime the accuracy of a modal Iwan model is found to depend on whether the mode in question is dominant in the response; in some cases the effective damping that the uncoupled model predicts is found to be in error by tens of percent. Nonetheless, the modal model captures the response qualitatively and is still far superior to a linear model. [ABSTRACT FROM AUTHOR]

• Two different nonlinear models are identified for two experimental test rigs. • The models are used to predict dynamic behavior under sine (-sweep) excitations. • The modal model is accurate for sine excitation close to resonance. • The modal model is sensitive for uncontrolled sweeps if the excitation force drops. • Polynomial nonlinear state-space models highly depend on the used training data. In the present paper, two existing nonlinear system identification methodologies are used to identify data-driven models. The first methodology focuses on identifying the system using steady-state excitations. To accomplish this, a phase-locked loop controller is implemented to acquire periodic oscillations near resonance and construct a nonlinear-mode model. This model is based on amplitude-dependent modal properties, i.e. does not require nonlinear basis functions. The second methodology exploits uncontrolled experiments with broadband random inputs to build polynomial nonlinear state-space models using advanced system identification tools. The methods are applied to two experimental test rigs, a magnetic cantilever beam and a free-free beam with a lap joint. The respective models obtained by either method for both specimens are then challenged to predict dynamic, near-resonant behavior observed under different sine and sine-sweep excitations. The vibration prediction of the nonlinear-mode and state-space models clearly highlight capabilities and limitations. The nonlinear-mode model, by design, yields a perfect match at resonance peaks and high accuracy in close vicinity. However, it is limited to well-spaced modes and sinusoidal excitation. The state-space model covers a wider dynamic range, including transient excitations. However, the real-life nonlinearities considered in this study can only be approximated by polynomial basis functions. Consequently, the identified state-space models are found to be highly input-dependent, in particular for sinusoidal excitations where they are found to lead to a low predictive capability. [ABSTRACT FROM AUTHOR]