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Nonlinear energy sinks (NESs) are broadband passive vibration absorbers that are nonlinearly connected to a host system. If an NES is attached to a multi-degree-of-freedom mechanical host system under transient loading, the vibrations in the host system will transfer to and dissipate in the NES. During this transfer, the NES sequentially resonates with the modal frequencies of the host system, dissipating one mode at a time. This phenomenon is called resonance capture cascade (RCC). So far, RCC has only been investigated for NESs with a hardening nonlinear stiffness. Because of this stiffness, the transfer of modal vibrations happens from high to low frequency. In this study, an NES with a softening stiffness is proposed. Investigating the slow invariant manifolds reveals that an inverted resonance capture cascade occurs, where the transfer of vibrations to the NES is from low to high frequency. The analysis is carried out by exploiting high-dimensional slow invariant manifolds. The proposed NES is compared to the conventional NES with hardening stiffness.

In vibrating mechanical systems, the targeted energy transfer mechanism (TET) of nonlinear energy sinks (NES) is employed as an alternative to linear tuned mass dampers (TMD) as passive vibrations absorbers for transient vibrations. The major advantages a NES has over a linear TMD are (1) an increased robustness to detuning and (2) the ability to dissipate multiple frequencies with only a single NES through so-called resonance capture cascading (RCC). The performance, especially the speed, of TET and RCC has rarely been a topic of research. In this research, algebraic performance measures for the speed of both TET and RCC are derived, called the pumping time and the cascading time, respectively. It shows that cascading time can be seen as a sum of single-mode pumping times, by introducing a novel modal decomposition. The strength of both measures is that they do not require numerical simulations, allowing easy optimization of the NES. The influence of different nonlinearities on the TET and RCC performance is investigated. Actual numerical simulations presented in the study validate the merit of both the pumping time and cascading time.

A nonlinear energy sink (NES) passively reduces transient vibration energy of a typically impact loaded mechanical system. It is locally connected to the vibrating system through a nonlinear connecting stiffness. For a NES to perform efficiently, through targeted energy transfer (TET), the vibration levels need to exceed a well-defined threshold, below which the NES performs poorly. This threshold can be lowered by considering a NES with a bi-stable connecting stiffness. A bi-stable NES (BNES) has two stable equilibria. Besides vibrating in TET regime, a BNES can also vibrate chaotically or close to one of its equilibria, called intra-well vibrations. However, during both chaotic and intra-well vibrations, the mitigating performance of the BNES is poor. Here, a novel tuning method is developed, which finds the boundary between chaotic and TET regime, such that the BNES avoids the chaos and operates with the more performant TET. This boundary is found by numerically calculating the Lyapunov exponent, a measure for chaos. To quantify performance, two algebraic expressions, requiring no simulations, are derived in the paper expressing the speed of vibration mitigation and expressing the residual vibration energy left after TET. The result is a generic tuning methodology that not only ensures the BNES operates in the efficient TET regime, but also guarantees optimal speed of vibration mitigation. The developed performances measures in function of the NES’s parameters are to the point and easy to use. The tuned BNES shows a superior robustness w.r.t detuning compared to the linear vibration absorbers.

In response to limitations in vibration suppression performance of traditional linear tuned mass damper due to energy threshold constraints and narrow vibration bands, this study proposes a magnetic tri-stable NES (MTNES) formed by combining a linear spring and magnets. Compared to the conventional nonlinear energy sink (NES), the magnetic tri-stable NES (MTNES) incorporates magnetism to enhance the nonlinear stiffness. Firstly, the mechanism of the MTNES is introduced in this study, which reveals the existence of the three stable points in the system. Subsequently, the equations of motion of the coupled system with MTNES attached to the cantilever beam are derived, and the optimal parameter combination for MTNES is determined using a global optimization method. Furthermore, the influence of MTNES parameter variations on vibration suppression efficiency is studied through parameter analysis. Then, the restoring force of the MTNES is simplified into polynomial form, and the system response is analyzed using the harmonic balance method and Runge–Kutta method. Finally, experimental studies on the coupled system are conducted. The results indicate that MTNES can effectively suppress the resonance of the host structure within a wide frequency band, with the highest vibration suppression rate of up to 66% under strong modulated response. Additionally, the results of numerical calculations and theoretical analysis are in good agreement with that of the experiment.

In this paper, an evolutionary-based sparse regression algorithm is proposed and applied onto experimental data collected from a Duffing oscillator setup and numerical simulation data. Our purpose is to identify the Coulomb friction terms as part of the ordinary differential equation of the system. Correct identification of this nonlinear system using sparse identification is hugely dependent on selecting the correct form of nonlinearity included in the function library. Consequently, in this work, the evolutionary-based sparse identification is replacing the need for user knowledge when constructing the library in sparse identification. Constructing the library based on the data-driven evolutionary approach is an effective way to extend the space of nonlinear functions, allowing for the sparse regression to be applied on an extensive space of functions. The results show that the method provides an effective algorithm for the purpose of unveiling the physical nature of the Duffing oscillator. In addition, the robustness of the identification algorithm is investigated for various levels of noise in simulation. The proposed method has possible applications to other nonlinear dynamic systems in mechatronics, robotics, and electronics.

This paper presents a novel First Order Plus Fractional Diffusive Delay (FOPFDD) model, capable of modeling delay dominant systems with high accuracy. The novelty of the FOPFDD is the Fractional Diffusive Delay (FDD) term, an exponential delay of non-integer order \\(\\), i.e. \\(e^-(Ls)^\\) in Laplace domain. The special cases of \\( = 0.5\\) and \\( = 1\\) have already been investigated thoroughly. In this work \\(\\) is generalized to any real number in the interval \\(]0,1[\\). For \\(=0.5\\), this term appears in the solution of distributed diffusion systems, which will serve as a source of inspiration for this work. Both frequency and time domain are investigated. However, regarding the latter, no closed-form expression of the inverse Laplace transform of the FDD can be found for all \\(\\), so numerical tools are used to obtain an impulse response of the FDD. To establish the algorithm, several properties of the FDD term have been proven: firstly, existence of the term, secondly, invariance of the time integral of the impulse response, and thirdly, dependency of the impulse response's energy on \\(\\). To conclude, the FOPFDD model is fitted to several delay-dominant, diffusive-like resistors-capacitors (RC) circuits to show the increased modeling accuracy compared to other state-of-the-art models found in literature. The FOPFDD model outperforms the other approximation models in accurately tracking frequency response functions as well as in mimicing the peculiar delay/diffusive-like time responses, coming from the interconnection of a large number of discrete subsystems. The fractional character of the FOPFDD makes it an ideal candidate for an approximate model to these large and complex systems with only a few parameters.

This study investigates a detailed analytical and numerical investigation of a nonlinear two-degree-of-freedom (2DOF) mechanical oscillator subjected to parametric excitation, magnetic stiffness nonlinearities, and dry friction. The considered system consists of two coupled oscillators, both of which are connected to a rotating rectangular beam that induces a time-periodic stiffness variation. The Complex Averaging (CxA) method is employed to derive approximate analytical solutions, which are thoroughly validated through time-domain simulations and bifurcation analyses. The dynamic analysis reveals a rich spectrum of nonlinear behaviors, including periodic, quasi-periodic, and chaotic responses. Detailed bifurcation diagrams, Lyapunov exponent analysis, and Poincaré maps demonstrate the influence of nonlinear stiffness degree, mass symmetry, and frictional effects on system stability and response amplitude. The obtained results give a significant understanding of the dynamic behavior of coupled nonlinear systems and establish a conceptual framework for the development of complex vibration abatement strategies, energy harvesting devices, and advanced mechanical systems.

In this paper, an evolutionary-based sparse regression algorithm is proposed and applied onto experimental data collected from a Duffing oscillator setup and numerical simulation data. Our purpose is to identify the Coulomb friction terms as part of the ordinary differential equation of the system. Correct identification of this nonlinear system using sparse identification is hugely dependent on selecting the correct form of nonlinearity included in the function library. Consequently, in this work, the evolutionary-based sparse identification is replacing the need for user knowledge when constructing the library in sparse identification. Constructing the library based on the data-driven evolutionary approach is an effective way to extend the space of nonlinear functions, allowing for the sparse regression to be applied on an extensive space of functions. ,e results show that the method provides an effective algorithm for the purpose of unveiling the physical nature of the Duffing oscillator. In addition, the robustness of the identification algorithm is investigated for various levels of noise in simulation. ,e proposed method has possible applications to other nonlinear dynamic systems in mechatronics, robotics, and electronics.

In this paper we aim to apply an adaptation of the recently developed technique of sparse identification of nonlinear dynamical systems on a Duffing experimental setup with cubic feedback of the output. The Duffing oscillator described by nonlinear differential equation which demonstrates chaotic behavior and bifurcations, has received considerable attention in recent years as it arises in many real-world engineering applications. Therefore its identification is of interest for numerous practical problems. To adopt the existing identification method to this application, the optimization process which identifies the most important terms of the model has been modified. In addition, the impact of changing the amount of regularization parameter on the mean square error of the fit has been studied. Selection of the true model is done via balancing complexity and accuracy using Pareto front analysis. This study provides considerable insight into the employment of sparse identification method on the real-world setups and the results show that the developed algorithm is capable of finding the true nonlinear model of the considered application including a nonlinear friction term.