This review serves as a comprehensive design strategy for designing quasi-zero stiffness (QZS) mechanical metamaterials (MMs). It discusses their underlying deformation mechanisms that enable the attainment of QZS behavior under both compressive and tensile loadings. While the QZS characteristic of metamaterials has garnered considerable attention, further research is essential to unlock their potential fully. Numerous QZS metamaterials have been meticulously reviewed. They comprise various elements and mechanisms, including positive and negative stiffness elements (PS and NS), PS elements with variable stiffness, bending mechanisms employing stiff joints/areas, buckling, buckling-rotating, and bending/buckling deformation mechanisms leading to a QZS feature. Furthermore, the capability of multi-material, adaptive, smart metamaterials, origami (bending around the hinge of the folded joints), and kirigami lattices (out-of-plane buckling via cutting patterns) are weighted. These diverse mechanisms contribute to achieving QZS behavior in metamaterials under both compression and tension loads, which is paramount for various mechanical applications such as passive vibration isolation. This review effectively categorizes QZS metamaterials based on their underlying mechanisms, providing scholars with valuable insights to identify suitable mechanisms for the desired QZS feature.

The advancement of information and energy technologies has spurred an increased demand for low-power and compact electronic devices with across various fields. Developing energy harvesting technologies to capture ambient and sustainable energy offers a promising solution to complement or replace conventional batteries. The piezoelectric technique provides a solution for energy harvesting from different energy sources, and high-frequency operation in piezoelectric energy harvesting offers several advantages. These include increased power output, as more charge is generated per unit of time, which increases the current. Additionally, better alignment with the natural resonance of piezoelectric elements enhances energy conversion efficiency. Considering the growing interest in efficient energy harvesting, a review of recent advancements in piezoelectric energy harvesting under high-frequency excitations and operations is presented in this paper. A brief introduction to the operating modes of piezoelectric energy harvester (PEH) is first introduced to provide a general understanding of energy conversion from the piezoelectric effect. PEHs under high-frequency operations from different energy sources are then reviewed and classified into three categories: wind, vehicle and train, and water flow. Next, novel ideas and structures to facilitate high-frequency operations for PEHs are summarized and discussed in detail. Subsequently, the working mechanisms for PEHs under high-frequency operations are described in detail and classified into three groups: high-speed rotation, frequency up-conversion, and friction-induced vibration mechanisms. Finally, applying advanced piezoelectric materials in novel structures and fostering application-oriented prototype testing are identified as trends for future development.

Rolling element bearings are one of the key components of many rotating machines. Through condition monitoring and vibration analysis of bearings, valuable information about the health status of the machine can be obtained, such as detection of a local fault, unbalance, and misalignment. For more than three decades, various bearing dynamic models have been developed by researchers to simulate the acceleration signal of the bearing and enhance the understanding of bearing vibration response in case of having local defects. Nevertheless, recent studies have shown that measurement of local strain in the bearing can be more effective in fault diagnosis of bearings since local strain signal is less subjected to surrounding noise and non-fault interferences. However, previously developed dynamic models lack a deep study on simulating strain signals at different locations in a bearing. Therefore, this paper presents the result of a combined lumped parameter and finite element models (FEMs) for simulating strain signals of a cylindrical roller bearing in healthy and defective conditions. Contrary to previous combined dynamic models, more realistic modeling assumptions are adopted, such as applying contact pressure to the raceway rather than concentrated nodal forces. The contact pressures are obtained from the lumped parameter model and exported to the FEM for transient analysis. The accuracy of the model in generating stress distribution is validated by a static 2D finite element contact model and theoretical formulas. Dynamic responses of the bearing, such as strain changes at different locations, are investigated in the time and frequency domains to investigate the fault symptoms such as sharp impulses and amplitude modulation. Lastly, the effect of a transmission path on strain measurement and using residual strain signal for fault diagnosis are discussed. Overall, the proposed model is an effective method for strain simulation of healthy and defective roller bearings.

This paper tackles the problem of finding the optimal design parameters for a piezoelectric energy harvester. A new simulation-based optimization procedure is proposed with the goal of acquiring the optimal geometric and circuit design parameters that leads to higher energy harvesting efficiency and also enhances the obtained electrical power. The basis of the optimization platform is a numerical model of the energy harvesting system operating during electrical transient of charging an external storage capacitor. The model consists of a cantilever beam partially coated with piezoelectric patches, a non-linear interfacing and conditioning circuit, and a storage device. The numerical model simulates a complete energy harvesting scenario from piezoelectric transduction, to power enhancement and conditioning through interfacing circuit and energy storage. Two different case studies are considered for beams under harmonic tip-force, and harmonic base-excitation. Since performing multiple simulations in order to evaluate the objective function is computationally expensive and imposes time and space (memory) complexities, a more efficient Neural Network (NN) model is first trained based on a set of training data obtained from the numerical model. Performance and accuracy of the NN training is studied using available statistical methods. Second, a Genetic Algorithm (GA) optimization performs a block-box optimization procedure, using the trained Neural Network model for objective function evaluation. Finally, a thorough analysis of the optimal design parameters obtained from the optimization process is provided.