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Metastructures are engineered systems composed of periodic arrays of identical components, called resonators, designed to achieve specific dynamic effects, such as creating a band gap-a frequency range where waves cannot propagate through the structure. When equipped with patches of piezoelectric material, these metastructures exhibit an additional capability: they can harvest energy effectively even from frequencies much lower than the fundamental frequency of an individual resonator. This energy harvesting capability is particularly valuable for applications where low-frequency vibrations dominate. To support the design of metastructures for dual purposes, such as energy harvesting and vibration suppression (reducing unwanted oscillations in the structure), we develop a multi-patch isogeometric model of a piezoelectric energy harvester. This model is based on a piezoelectric Kirchhoff-Love plate-a thin, flexible structure with embedded piezoelectric patches-and uses Nitsche's method to enforce compatibility conditions in terms of displacement, rotations, shear force, and bending moments across the boundaries of different patches. The model is validated against experimental and numerical data from the literature. We then present a novel, parameterized metastructure plate design and conduct a parametric study to explore how resonator geometries affect key performance metrics, including the location and width of the band gap and the position of the first peak in the voltage frequency response function. This model can be integrated with optimization algorithms to maximize outcomes such as energy harvesting efficiency or vibration reduction, depending on application needs.

The current battery-powered fault detection system for vibration monitoring has a rather limited lifetime. This is because the high-frequency sampling (typically tens of kilo-Hertz) required for vibration monitoring results in high energy consumption in both the analog-to-digital (ADC) converter and wireless transmissions. This paper proposes a new fault detection architecture that can significantly reduce the energy consumption of the ADC and wireless transmission. Our inspiration for the new architecture is based on the observation that the many tens of thousand of data samples collected for fault detection are ultimately transformed into a small number of features. If we can generate these features directly without high frequency sampling, then we can avoid the the energy cost for ADC and wireless transmissions. We propose to use piezoelectric energy harvesters (which can be designed to have different frequency responses) and integrators to obtain these features in an energy-efficient manner. By using a publicly available data set for ball bearing fault detection (which was originally sampled at 51.2kHz) and piezoelectric energy harvester models, we can produce features, which when sampled at 0.33Hz, give a fault detection accuracy of 89% while reducing the sampling requirement by 4 orders-of-magnitude., Comment: 8 pages, 8 figures

Piezoelectric energy harvesters (PEHs) can be used as an additional power supply for a Structural Health Monitoring (SHM) system. Its design can be optimised for the best performance; however, the optimal design depends on the input vibration, e.g. acceleration of a bridge due to wind loads and passing traffic. In previous studies, we have shown that optimal design tunes to some predominant frequency of the signal spectrum. However, our previous study was limited to a single location of a PEH on a bridge. In this work, we extend the rigorous optimisation framework to include the effect of the PEH's location. The optimisation framework uses the PEH plate model discretised by IsoGeometric Analysis (IGA) and coupled with the Particle Swarm Optimisation algorithm to find the designs with maximum energy outputs for many acceleration histories, extracted from the recorded dynamic response data of a real cable-stayed bridge in Australia. Then, clustering is performed to find several best candidates for the entire bridge. An additional study is performed to quantify the effect of traffic intensity on the produced energy. It is shown that variation in traffic volume throughout the 24hr time window leads to variation in optimal PEH designs. The study concludes the impact of location and traffic on energy harvesting by identifying the optimal PEH design and placement throughout the bridge structure. The results indicate that the key factors of maximising energy harvesting efficiency are related to the input excitation and the mode of vibration being excited. The position of maximum displacement in the vibration mode corresponds to the best location for energy harvesting. Also, the change in traffic intensity affects the amount of convertible mechanical energy and also directs the fundamental frequency of a PEH to shift within a specific range of frequencies to achieve the highest energy conversion.

Piezoelectric Energy Harvesters (PEHs) are typically employed to provide additional source of energy for a sensing system. However, studies show that a PEH can be also used as a sensor to acquire information about the source of vibration by analysing the produced voltage signal. This opens a possibility to create Simultaneous Energy Harvesting and Sensing (SEHS) system, where a single piece of hardware, a PEH, acts as both, a harvester and as a sensor. This raises a question if it is possible to design a bi-functional PEH device with optimal harvesting and sensing performance. In this work, we propose a bi-objective PEH design optimisation framework and show that there is a trade-off between energy harvesting efficiency and sensing accuracy within a PEH design space. The proposed framework is based on an extensive vibration (strain and acceleration) dataset collected from a real-world operational cable-stayed bridge in New South Wales, Australia. The bridge acceleration data is used as an input for a PEH numerical model to simulate a voltage signal and estimate the amount of produced energy. The numerical PEH model is based on the Kirchhoff-Love plate and isogeometric analysis. For sensing, convolutional neural network AlexNet is trained to identify traffic speed labels from voltage CWT (Continuous Wavelet Transform) images. In order to improve computational efficiency of the approach, a kriging metamodel is built and genetic algorithm is used as an optimisation method. The results are presented in the form of Pareto fronts in three design spaces.

Vibrational energy harvested from the bridge excitation due to the traffic flow or the wind load can be supplied to sensors in a Structural Health Monitoring (SHM) system and help prolong its service life, reduce chemical battery waste and enable its use in remote locations. A common approach to designing a Piezoelectric Energy Harvester (PEH) consists in tuning its fundamental frequency to some target value (e.g., the fundamental frequency of a bridge). However, such approach does not answer the question if the chosen target frequency is optimal, and does not take into account the possibility to have multiple design configurations with the same fundamental frequency. In this work, we approach the problem of a PEH design optimisation in a rigorous way, using a PEH model based on the Kirchhoff-Love plate theory and Isogeometric Analysis (IGA), coupled with the Modal Order Reduction (MOR) approach and Runge-Kutta time integration method. The model is further equipped with the Particle Swarm Optimisation (PSO) algorithm that allows finding geometry which maximises energy output from a given base acceleration signal. A comprehensive study is conducted to infer the impact of a PEH geometry on its energy harvesting performance in a real-world setting by considering field health monitoring data of a large-scale cable-stayed bridge in NSW, Australia. A shape optimisation framework is developed based on acceleration events, i.e., where the level of response exceeds a certain threshold. Designs obtained in the event-based optimisation are then clustered to propose several best candidates for continuous energy generation. This work represents the first study on PEH design optimisation for real operational conditions.