ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

This article is available in the ASME Digital Collection at .

This article is available in the ASME Digital Collection at .

A methodology based on the Wiener path integral (WPI) technique is developed for stochastic response determination and reliability-based design optimization of a class of nonlinear electromechanical energy harvesters endowed with fractional derivative elements. In this regard, first, the WPI technique is appropriately adapted and enhanced to account both for the singular diffusion matrix and for the fractional derivative modeling of the capacitance in the coupled electromechanical governing equations. Next, a reliability-based design optimization problem is formulated and solved, in conjunction with the WPI technique, for determining the optimal parameters of the harvester. It is noted that the herein proposed definition of the failure probability constraint is particularly suitable for harvester configurations subject to space limitations. Several numerical examples are included, while comparisons with pertinent Monte Carlo simulation (MCS) data demonstrate the satisfactory performance of the methodology. This article is available in the ASME Digital Collection at .

Vibration energy harvesting (VEH) is a promising alternative for powering wireless electronics in many practical applications. Ambient vibration energy in the surrounding space of a target application often involves an inescapable randomness in the exciting vibrations, which may lead to deterioration of the expected power gains due to insufficient tuning and limited optimal designs. Stochastic resonance (SR) is a concept that has recently been considered for exploiting this randomness toward improving power generation from vibrating systems, based on the coexistence of near-harmonic vibrations with broadband noise excitations in a variety of practical mechanical systems. This paper is concerned with the optimal conditions for SR in vibration energy harvesters, exploring the frequently neglected effect of realistic architectures of the electrical circuit on the system dynamics and the achievable power output. A parametric study is conducted using a numerical path integration (PI) method to compute the response probability density functions (PDFs) of vibration energy harvesters, focusing on the effect of standard electrical components; namely, a load resistor, a rectifier, and a capacitor. It is found that the conditions for SR exhibit a nonlinear dependence on the weak harmonic excitation amplitude. Moreover, the modified nonlinear dissipation properties introduced by the rectifier and the capacitor lead to a tradeoff between the power output and the nonconducting dynamics that is essential in order to determine optimal harvesting designs. This article is available in the ASME Digital Collection at .

This article deals with the case study of a marina located in Roccella Jonica (Italy), where a wave energy harvester belonging to the family of U-oscillating water columns (U-OWC) is going to be installed. U-OWCs are wave energy harvesters composed of a water column exposed to the action of random sea waves and an air pocket connected to the atmosphere by a power take—off (PTO) system. In Roccella Jonica, this device is going to be embedded in a vertical breakwater expanding the main layout of the infrastructure. For ensuring the structural safety of the system, to characterize statistically its response peaks in severe environmental conditions is important. In this context, one of the main difficulties is utilizing appropriate environmental conditions representing real extreme events at the installation site. This article proposes to adopt the DNV trapezoidal storm model for representing the time history of an extreme event in conjunction with a nonlinear U-OWC model. Relevant Monte Carlo simulations show that the DNV storm model provides peak distributions that are rather close to the ones obtained by processing real storm time histories. Thus, it can be adopted for checking the performance of the system in extreme conditions. This article is available in the ASME Digital Collection at .

A framework that allows the use of well-known dynamic estimators in piezoelectric harvesters (PEHs) (i.e., deterministic performance estimators) and that accounts for the random error associated with the mathematical model and the uncertainties of model parameters is presented here. This framework may be employed for Posterior Robust Stochastic analysis, such as when a harvester can be tested or is already installed and the experimental data are available. In particular, the framework detailed here is introduced to update the electromechanical properties of PEHs using Bayesian techniques. The updated electromechanical properties are identified by adopting a Transitional Markov Chain Monte Carlo. A well-known device with a nonlinear constitutive relationship is employed for experiments in this study, and the results demonstrated the capability of the proposed framework to update nonlinear electromechanical properties. The importance of including model parameter uncertainties to generate robust predictive tools is also supported by the results. Therefore, this framework constitutes a powerful tool for the robust design and prediction of PEH performance. This article is available in the ASME Digital Collection at .

An energy harvesting dynamic vibration absorber (EHDVA) is studied to suppress undesirable vibrations in a host structure as well as to harvest electrical energy from vibrations using piezoelectric transduction. This work studies the feasibility of using vibration absorber for harvesting energy under random excitation and in presence of parametric uncertainties. A two degrees-of-freedom model is considered in the analytical formulation for the host along with the absorber. A separate equation is used for energy generation from piezoelectric material. Two studies are reported here: (i) with random excitation where the base input is considered to be Gaussian and (ii) parametric uncertainty is considered with harmonic excitation. Under random base excitation, the analytical results show that, with the proper selection of parameters, harvested electrical energy can be increased along with the reduction in vibration of the host structure. Graphs are reported showing tradeoff between harvested energy and vibration control. Whereas, Monte Carlo simulations are carried out to analyze the system with parametric uncertainty. This showed that the mean harvested power decreases with an increase in uncertainties in the natural frequency as well as damping ratio. In addition, optimal electrical parameters for obtaining maximum power for the case of uncertain parameters are also reported in this study. This article is available in the ASME Digital Collection at .

Quantifying effects of system-wide uncertainties (i.e., affecting structural, piezoelectric, and/or electrical components) in the analysis and design of piezoelectric vibration energy harvesters have recently been emphasized. The present investigation proposes first a general methodology to model these uncertainties within a finite element model of the harvester obtained from an existing finite element software. Needed from this software are the matrices relating to the structural properties (mass, stiffness), the piezoelectric capacitance matrix as well as the structural-piezoelectric coupling terms of the mean harvester. The thermal analogy linking piezoelectric and temperature effects is also extended to permit the use of finite element software that do not have piezoelectric elements but include thermal effects on structures. The approach is applied to a beam energy harvester. Both weak and strong coupling configurations are considered, and various scenarios of load resistance tuning are discussed, i.e., based on the mean model, for each harvester sample, or based on the entire set of harvesters. The uncertainty is shown to have significant effects in all cases even at a relatively low level, and these effects are dominated by the uncertainty on the structure versus the one on the piezoelectric component. The strongly coupled configuration is shown to be better as it is less sensitive to the uncertainty and its variability in power output can be significantly reduced by the adaptive optimization, and the harvested power can even be boosted if the target excitation frequency falls into the power saturation band of the system. This article is available in the ASME Digital Collection at .

Tuned inerter dampers (TID) have been demonstrated as efficient energy dissipation devices for seismic response control. However, its potential capability for energy harvesting remains largely unexplored. Here, we present a theoretical analysis of the power of a structure-TID system subjected to earthquake ground motions. The analytical solutions (ASs) of the average damping power of the system are derived for considering white noise base excitations and the Kanai-Tajimi earthquake model, respectively. Comparisons of the numerical results of a Monte Carlo simulation and the theoretical predictions verify the accuracy of the analytical solutions. Besides, we uncover the influence of the TID parameters on the average damping power and output power of the system. The optimal frequency ratio of the TID for maximizing its output power slightly differs from that for seismic response control, and the former varies with site conditions. In contrast, both the damping power and output power are not sensitive to the damping ratio of the TID. For short-period structures, a small inertance-to-mass ratio ($\mu$) of the TID is beneficial to maximize its output power, while seismic response control requires a large $\mu$. For long-period structures, the damping power and output power are not sensitive to the $\mu$. Generally, a structure-TID system on a soft soil site absorbs more energy from a given earthquake and is capable of harvesting more energy than that on a hard soil site. This study may help develop new strategies for self-powered control and monitoring in civil structures. This article is available in the ASME Digital Collection at .

This paper assesses the vibratory energy harvesting performance of a tuned inertial mass electromagnetic transducer (TIMET) through hardware-in-the-loop (HIL) testing under random vibration. The TIMET has been developed by adding a tuning spring and an extra rotational inertial mass to a conventional electromagnetic transducer (ET) with a motor. The authors have already shown that the energy harvesting efficiency of the TIMET can be increased by taking advantage of the mechanical resonance effect of the rotational inertial mass due to the tuning spring through numerical simulation studies. In addition, further improvement in power generation of the TIMET can be achieved theoretically by controlling the current to the motor based on the appropriately developed algorithms. In this paper, the superiority of the TIMET over the ET under random disturbances when the current to the motor is controlled by the algorithms proposed for the ET in the literature is experimentally verified. Moreover, the accuracy of the numerical simulation using the developed device models is validated by comparing with the test results. This article is available in the ASME Digital Collection at .

This paper examines the performance of a regenerative dynamic vibration absorber, dubbed energy harvesting-enabled tuned mass-damper-inerter (EH-TMDI), for simultaneous vibration suppression and energy harvesting in white-noise-excited damped linear primary structures. Both single-degree-of-freedom (SDOF) structures under force and base excitations and multi‐degrees-of-freedom (MDOF) structures under correlated random forces are studied. The EH-TMDI includes an electromagnetic motor (EM), assumed to behave as a shunt damper, sandwiched between a secondary mass and an inerter element connected in series. The latter element resists relative acceleration at its ends through a constant termed inertance known to be readily scalable in actual inerter device implementations. In this regard, attention is herein focused on gauging the available energy for harvesting at the EM and the displacement variance of the primary structure as the inertance increases through comprehensive parametric investigations. This is supported by adopting simplified inertance-dependent tuning formulae for the EH-TMDI stiffness and damping properties and deriving in closed-form the response of white-noise-excited EH-TMDI-equipped SDOF and MDOF systems through linear random vibration analyses. It is found that lightweight EH-TMDIs, having 1% the mass of the primary structure, achieve improved vibration suppression and energy harvesting performance as inertance amplifies. For SDOF structures with grounded inerter, the rate of improvement is higher as the inherent structural damping reduces and the EM shunt damping increases. For MDOF structures with nongrounded inerter, improvement rate is higher as the primary structure flexibility between the two EH-TMDI attachment points increases. This article is available in the ASME Digital Collection at .

A method is developed to characterize the performance of voltage processes X(t) harvested from primary-absorber dynamical systems subjected to Gaussian forcing functions. The method is based on properties of the Slepian model of X(t) and Monte Carlo simulation. Statistics are calculated for excursions of X(t) above levels which can be related to energy demand. The duration and the area of these excursions are used as metrics for the voltage process. Their statistics depend on the topology and the parameters of primary-absorber dynamical systems, which can be optimized to maximize the output voltage. This article is available in the ASME Digital Collection at .

The vibration level is a function of the defects in the bearing. By identifying a change in vibration level, one can predict the dynamic behavior and fault in the rotor-bearing system. An imminent bearing fault detection can reduce downtime or avoid the failure of rotating machinery. The condition monitoring or maintenance schedule can be set well if the diagnosis estimate bearing fault size accurately. In view of this, the adaptive neurofuzzy inference system (ANFIS) and dimension analysis (DA) were utilized to detect the bearing fault size. Several experiments were performed at different rotating speeds on the rotor-bearing system. Localized defects were simulated on bearing races artificially using electrode discharge machining (EDM) and the vibration responses are acquainted by accelerometer and fast Fourier techniques (FFT). With a 0.1 mm error band to fix minor bugs, a two-performance indicator evaluated the model accuracy. A comparison of the performance of models with experimental results and artificial neural network (ANN) validated the potential of the present approach. The results showed that an ANFIS performs better over DA and ANN. This contributes to detecting bearing fault effectively and accuracy improvement in the estimation of the bearing fault size. This article is available in the ASME Digital Collection at .

Composite materials have a myriad of applications in complex engineering systems, and multiple structural health monitoring (SHM) strategies have been developed. However, these methods are challenging due to signal attenuation and excessive noise interference in composite materials. Signal processing can capture a small difference between the input–output signals associated with the severity of the damage in composites. Thus, the research question is “can signal processing techniques reduce the required number of features and assess the randomness of fatigue damage classification in composite materials using machine learning (ML) algorithms?” To answer this question, piezo-electric signals for carbon fiber reinforced polymer (CFRP) test specimens were taken from NASA Ames prognostics data repository. A framework based on a comparative analysis of signals was developed. For the first specific aim, the effectiveness of features based on statistical condition indicators of the sensor signals were evaluated. For the second specific aim, actuator-sensor signal pair were analyzed using cross-correlation to extract two features. These features were used to train and test four supervised ML algorithms for damage classification and their performance was discussed. For the third specific aim, randomness in the dataset of fatigue damage of the specimens was assessed. Results showed that by signal processing, the requirement of features for training ML was reduced with the improvement in the performance of ML. The randomness was captured by the utilization of two specimens from the same material. This work contributes to the improvement of intelligent damage classification of composite materials, operating under complex working conditions. This article is available in the ASME Digital Collection at .