This paper investigates the analysis and design of a vibration-based energy harvester for rotary motion applications. The energy harvester consists of a cantilever beam with a tip mass and a piezoelectric ceramic attached along the beam that is mounted on a rotating shaft. Using this system, mechanical vibration energy is induced in the flexible beam due to the gravitational force applied to the tip mass while the hub is rotating. The piezoelectric transducer is used to convert the induced mechanical vibration energy into electricity. The equations of motion of the flexible structure are utilized along with the physical characteristics of the piezoelectric transducer to derive expressions for the electrical power. Furthermore, expressions for the optimum load resistance and maximum output power are obtained and validated experimentally using PVDF and PZT transducers. The results indicate that a maximum power of 6.4 mW at a shaft speed of 138 rad/s can be extracted by using a PZT transducer with dimensions 50.8 mm × 38.1 mm × 0.13 mm. This amount of power is sufficient to provide power for typical wireless sensors such as accelerometers and strain gauges.

This paper presents modeling and analysis of a piezoelectric mounted rotary flexible beam that can be used as an energy scavenger for rotary motion applications. The energy harvester system consists of a piezoelectric bimorph cantilever beam with a tip mass mounted on a rotating hub. Assuming Euler–Bernoulli beam equations and considering the effect of a piezoelectric transducer, equations of motion are derived using the Lagrangian approach followed by relationships describing the harvested power. The equations provide a quantitative description of how the hub acceleration and gravity due to the tip mass contribute power to the energy harvester. In particular, expressions describing optimum load resistance and the maximum power that can be harvested using the proposed system are derived. Numerical simulations are performed to show the performance of the harvester by obtaining tip velocities and electrical output voltages for a range of electrical load resistances and rotational speeds. It is shown that by proper sizing and parameter selection, the proposed system can supply enough energy for operating wireless sensors in rotating mechanisms such as tires and turbines.

Background: AI-enabled digital twins (DTs) are advanced virtual models of a complex real-world system, augmented with predictive AI capabilities, which have a potential for transforming healthcare and clincial decision making. Despite the growing interest and need in such DTs, the literature lacks a unified framework for its development and implementation. Objective: This study aims to map the existing knowledge on AI-enabled DTs for clinical decision-making, and develop a comprehensive framework for its development and implementation. Methods: Informed by the scoping review frameworks established by Arksey and O'Malley, and the Joanna Briggs Institute, we performed a scoping review of studies reporting on the development and implementation of AI-enabled DTs for clinical decision-making in any healthcare setting. The search strategy was developed by a professional academic librarian for three databases, i.e., PubMed, Embase (Ovid), and IEEE Xplore, from the date of inception until August 2023. We also conducted a grey literature search on Google Scholar. Moreover, we scanned the references of all included studies to identify additional potentially relevant studies for inclusion. A reviewer screened titles and abstracts, full-text articles, and charted data, and the second reviewer verified them. Data were summarised descriptively using content analysis. Key steps in DTs development were identified and combined to create the DECIDE-Twin framework. Results: Out of 421 records, 11 were included: seven reviews and four empirical studies, all published from 2020 onward. The reviews contained either a framework or information that was used to construct our newly developed framework. The empirical studies reported the development of DTs, and one reported a common infrastructure for a wide range of applications for DTs. Conclusion: We developed the DECIDE-Twin framework that could serve as a guide for researchers and practitioners for the development and implementation of AI-enabled DTs in clinical decision making. Further research is needed to validate and implement this framework across various use cases.