Energy harvesting induced from flowing fluids (e.g., air and water flows) is a well-known process, which can be regarded as a sustainable and renewable energy source. In addition to traditional high-efficiency devices (e.g., turbines and watermills), the micro-power extracting technologies based on the flow-induced vibration (FIV) effect have sparked great concerns by virtue of their prospective applications as a self-power source for the microelectronic devices in recent years. This article aims to conduct a comprehensive review for the FIV working principle and their potential applications for energy harvesting. First, various classifications of the FIV effect for energy harvesting are briefly introduced, such as vortex-induced vibration (VIV), galloping, flutter, and wake-induced vibration (WIV). Next, the development of FIV energy harvesting techniques is reviewed to discuss the research works in the past three years. The application of hybrid FIV energy harvesting techniques that can enhance the harvesting performance is also presented. Furthermore, the nonlinear designs of FIV-based energy harvesters are reported in this study, e.g., multi-stability and limit-cycle oscillation (LCO) phenomena. Moreover, advanced FIV-based energy harvesting studies for fluid engineering applications are briefly mentioned. Finally, conclusions and future outlook are summarized.

Track irregularities induce potential risks to the safety and stability of railway track systems. This paper proposes a novel methodology to identify vertical and lateral track irregularities. The method involves measuring system-based attitude calculation and a model-based unknown input observer estimator, based on the dynamic responses of distributed multi-sensors on the vehicle and bogie. First, a mechanical model of wheel-rail contacts is built with dynamic methods. The model considers the different directions of motion for a railway vehicle and consists of two bogies and four wheelsets. Based on the multi-sensor acceleration measurement, the vertical and lateral acceleration signals of the vehicle and bogies are integrated into the displacement signal. Then a state-space description of the vehicle suspension model is established for inverse dynamical analysis to extract the input signals. A suitable unknown input observer is constructed to estimate the track irregularities by transforming the state space equations of the vehicle into an augmented system that can monitor the track irregularities in-service. This method provides an opportunity to reduce the costs of the monitoring infrastructure and provide quicker and more reliable information about the status of a track.