Enhancing the performance of a bistable energy harvesting device via the cross-entropy method

Cunha, Americo

doi:10.1007/s11071-020-06109-0

Cited by 30 publications

(20 citation statements)

References 61 publications

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“…Some video animations obtained with this module are available in the GitHub repository see footnote 1 and YouTube channel. 2 They are mentioned in the following publications [10,11]. To the best of our knowledge, these are the only works in the open literature showing this type of animation for a nonlinear vibrating harvester, an innovation in the way of studying the dynamics of this type of system.…”

Section: Initial Value Problemmentioning

confidence: 99%

STONEHENGE — Suite for nonlinear analysis of energy harvesting systems

et al. 2021

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STONEHENGE is a toolbox designed to evaluate nonlinear vibration-based energy harvesting systems, which demand careful studies regarding their nontrivial behavior. It is composed of an ensemble of codes to study and characterize the dynamic behavior, as well as deal with varieties of physical parameters and excitation. For this, it has six modules, initial value problem, dynamic animation, nonlinear tools, sensitivity analysis, stochastic simulation, and chaos control. A bistable oscillator is used as a benchmark for a vibration harvester. We hope this toolbox can contribute to the development and improvement of old and new generations of nonlinear vibration-based energy harvesting systems.

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Section: Initial Value Problemmentioning

confidence: 99%

STONEHENGE — Suite for nonlinear analysis of energy harvesting systems

et al. 2021

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“…In which ( − * ) is a multivariable Dirac delta [8]. Thus, an usual stopping criterion is do the iterations while ∞ > , in which is a small tolerance related to an ideal Dirac delta, in which = 0, and • ∞ is the L ∞ -norm.…”

Section: Deterministic Identification Proceduresmentioning

confidence: 99%

“…Thus, an usual stopping criterion is do the iterations while ∞ > , in which is a small tolerance related to an ideal Dirac delta, in which = 0, and • ∞ is the L ∞ -norm. Algorithm 1 summarizes the cross-entropy optimization procedure employed [9,17,8]. 3.3 Bayesian inference in stochastic identification using HBM amplitudes…”

Section: Deterministic Identification Proceduresmentioning

confidence: 99%

“…The HBM-based procedure is presented in Section 3, which contains two main subsections: first, in Section 3.2, the deterministic identification technique of the Bouc-Wen model proposed by Miguel et al [22] is presented. The methodology is based on minimizing the error between mono-harmonic responses from the experimental data and the analytical approximation through the Cross-Entropy optimization procedure [8,17,9]. It is also assumed the adjustment of a transient velocity is an auxiliary identification metric to achieve better results.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Model Identification Through Harmonic Balance Method For Hysteresis Prediction in Bolted Joints

Miguel

Teloli

Silva

2021

Preprint

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Hysteresis is a nonlinear dissipative phenomenon present in many structures, such as those assembled by bolted joints. However, the approaches for identification are still limited in the literature due to their complexity. Additionally, there are also uncertainties held in bolted structures caused to fluctuations in tightening torque and pressure distribution along the contact surface. Thus, this paper yields a methodology for identifying a stochastic Bouc-Wen model for bolted joints based on the harmonic balance method. Unfortunately, some challenges are encountered when applying conventional series approximation to hysteresis, caused mainly by non-smooth behavior, which induces abrupt transitions between different motion regimes. In this work, previous adaptations were made to split the hysteresis loop in smooth paths and then use a piecewise harmonic balance approach. In this way, it was possible to deal with a deterministic Identification problem based on minimizing the error between the Fourier amplitudes of an experimental signal and those obtained through harmonic balance applying the Cross-Entropy optimization method. So, the results were extended to a stochastic model by applying the Bayesian paradigm, in which the maximized likelihood function was also based on the harmonic balance amplitudes. This methodology was demonstrated to identify Bouc-Wen parameters capable of predicting hysteresis in the BERT benchmark, composed of two aluminum beams jointed by a bolted joint in a cantilever boundary condition. Evaluating the results in the time and frequency domain and the nonlinear behavior through the hysteresis loop, it can be concluded that the method was able to identify an accurate stochastic Bouc-Wen model in predicting the dynamics of bolted structures even taking into account the probable uncertainties of the system.

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“…This energy harvester has become a classical system, being explored for its potential and rich dynamics [20,7,34,27,16,15,53]. Optimization problem to enhance the performance of an energy harvesting device is also developed in [12]. Therefore, in the framework of nonlinear dynamical systems, GSA can be a workable tool to reveal each parameter's uncertainty influence, mainly because nonlinear systems present high sensitivity to input parameter variations and have a complex dynamic with regular and chaotic behaviors.…”

Section: Introductionmentioning

confidence: 99%

Global Sensitivity Analysis of (a)Symmetric Energy Harvesters

Norenberg

Cunha

Silva

et al. 2021

Preprint

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Parametric variability is inevitable in actual energy harvesters and can define crucial aspects of the system performance, especially in susceptible systems to small perturbations. In this way, this work aims to identify the most critical parameters in the dynamics of (a)symmetric bistable energy harvesters with nonlinear piezoelectric coupling, considering the variability of their physical and excitation parameters. For this purpose, a global sensitivity analysis based on the Sobol' indices is performed by an orthogonal decomposition in terms of conditional variances to access the dependence of the recovered power concerning the harvester parameters. This technique quantifies the variance concerning each parameter individually and jointly regarding the total variation of the model. The results indicate that the frequency and amplitude of excitation, asymmetric bias angle, and piezoelectric coupling at the electrical domain are the most influential parameters that affect the mean power harvested. It has also been shown that the order of importance of the parameters can change from stable conditions. In possession of this, a better understanding of the system under analysis is obtained, identifying vital parameters that rule the change of dynamic behavior and constituting a powerful tool in the robust design and prediction of nonlinear harvesters.

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Enhancing the performance of a bistable energy harvesting device via the cross-entropy method

Cited by 30 publications

References 61 publications

STONEHENGE — Suite for nonlinear analysis of energy harvesting systems

STONEHENGE — Suite for nonlinear analysis of energy harvesting systems

Bayesian Model Identification Through Harmonic Balance Method For Hysteresis Prediction in Bolted Joints

Global Sensitivity Analysis of (a)Symmetric Energy Harvesters

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