Aeroelastic energy harvesting can be used to power wireless sensors embedded into bridges, ducts, high-altitude buildings, etc. One challenging issue is that the wind speed in some application environments is low, which leads to an inefficiency of aeroelastic energy harvesters. This paper presents a novel nonlinear magnetic-coupled flutter-based aeroelastic energy harvester to enhance energy harvesting at low wind speeds.The presented harvester mainly consists of a piezoelectric beam, a two-dimensional airfoil, two tip magnets and two external magnets. The function of magnets is to reduce the cut-in wind speed of the flutter-based aeroelastic energy harvester and enhance energy harvesting performance at low wind speeds. A theoretical model is deduced based on Hamilton's principle, theory of aeroelasticity, Kirchhoff's laws and experimental measurements, etc. A good agreement is found between numerical simulation and experimental results, which verifies the accuracy of the theoretical model. Stability analysis is provided to determine the characteristics of the presented harvester. More importantly, it is numerically and experimentally verified that the presented harvester has a much lower cut-in wind speed (about 1.0 m/s) and has a better energy harvesting performance at a low wind speed range from 1.0 m/s to 2.9 m/s, when compared with traditional flutter-based aeroelastic energy harvesters.

This paper aims at presenting a novel magnetic-coupled bi-stable flutter-based energy harvester (MBFEH) for the purpose of enhancing power generation in low speed air flow. The MBFEH comprises of a piezoelectric beam with a magnet on its tip, a hinged rigid flat airfoil, and an external magnet. The magneto-electro-aeroelastic system is theoretically modeled, numerically investigated, and experimentally validated. The influences of the separation distance between the magnets on flutter characteristics and performance of power generation are studied. The numerical and experimental results show that the supercritical flutter will be transformed into the subcritical flutter which realizes a significant reduction of cut-in air speed for energy harvesting. Compared with the conventional flutter-based energy harvester, the cut-in air speed of the MBFEH is reduced by more than 50% when the separation distance is set as 15 mm. The most preferred distance among the tested configurations is 12 mm which broadens the usable air speed by 1.8 m s −1 , and leads to a 102% improvement of the energy harvesting performance throughout the air speed region from 2 m s −1 to 6.6 m s −1 . The study shows that the proposed MBFEH is an effective design approach for enhancing energy harvesting capability in low air speed range.

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Dock-less bicycle-sharing programs have been widely accepted as an efficient mode to benefit health and reduce congestions. And modeling and prediction has always been a core proposition in the field of transportation. Most of the existing demand prediction models for shared bikes take regions as research objects; therefore, a POI-based method can be a beneficial complement to existing research, including zone-level, OD-level, and station-level techniques. Point of interest (POI) is the location description of spatial entities, which can reflect the cycling route characteristics for both commuting and noncommuting trips to a certain extent, and is also the main generating point and attraction point of shared-bike travel flow. In this study, we make an effort to model a POI-level cycling demand with a Bayesian hierarchical method. The proposed model combines the integrated nested Laplace approximation (INLA) and random partial differential equation (SPDE) to cope with the huge computation in the modeling process. In particular, we have adopted the dock-less bicycle-sharing rental records of Mobike as a case study to validate our method; the study area was one of the fastest growing urban districts in Shanghai in August 2016. The operation results show that the method can help better understand, measure, and characterize spatiotemporal patterns of bike-share ridership at the POI level and quantify the impact of the spatiotemporal effect on bicycle-sharing use.