(Special) Energy Harvesting for Autonomous Powering of Wireless Sensor Network
Chair: Dr. Kyungjun SONG (Korea Insititute of Machineary and Materials)
Metamaterial-based Enhancement of Elastic Wave Energy Harvesting Dr. MISO KIM, Choon-su PARK, Wonjae CHOI, Yong Chang SHIN, Soo-Ho JO, Heonjun YOON and Byeng D. YOUN (Korea Research Institute of Standards and Science, Seoul National University)
Enhancement of metamaterial-based energy harvesting will be presented from design, analysis towards experimental demonstration. Metamaterials, artificially engineered materials, exhibit unique properties including bandgap and negative refractive index and thus enable us to manipulate mechanical wave propagations. In order to amplify input mechanical wave energy into energy harvesting systems, metamaterials can be utilized to guide and localize acoustic and elastic waves towards the desired position for harvesting. Recently, several research efforts on metamaterial-based enhancement of energy harvesting have been reported, but mostly based on intuitive design or with little experimental support. We propose several metamaterial-based energy harvesting systems including phononic crystals with defect and acoustic metamaterials with local resonances. Systematic design through geometric and bandgap optimization process is performed and followed by experimental demonstration. Drastic enhancement of energy harvesting performance via metamaterials is demonstrated and thoroughly investigated both analytically and experimentally.
A Metamaterial-based Energy Harvester Design for Enhanced Power Supply to Wireless Sensor Network Mr. Yong Chang SHIN, Heonjun YOON, Soo-Ho JO, Guilian YI, Miso KIM, Choon-Su PARK, Wonjae CHOI and Byeng D. YOUN (Seoul National University, Korea Research Institute of Standards and Science)
Health sensing module in prognostics and health management (PHM) requires accurate acquisition of sensory signal and optimal deployment of sensor nodes. To this end, wireless sensor network (WSN) is widely utilized by virtue of the ease of the sensor deployments and the advances in the wireless communication. However, periodic replacements of the batteries are demanded for continuous operation of the WSN, which incurs additional costs and temporary suspension. To overcome these drawbacks, vibration energy harvesting (VEH) has emerged as a possible solution to realize self-powered/sustainable WSN operation. The VEH converts ambient vibration into electric power through an energy conversion medium. Low output power density, however, is still a critical issue in the VEH for its feasible application. Metamaterial-based energy harvesting (MBEH) has recently been proposed as a breakthrough technology to drastically improve output power generation. Metamaterials are engineered structures to exhibit exotic properties such as a bandgap. The bandgap refers to a certain frequency range within which elastic waves are prohibited to propagate. It can be used to enlarge elastic wave energy going into the energy conversion medium. While various mechanism for the MBEH has been introduced, only a few research has considered detail factors that can degrade the metamaterial’s performance. In this study, we propose a new phononic crystal design which prevents wave cancellation to improve the metamaterial’s performance. In building the model, the multiphysics finite element method are used to analyze dispersion and energy harvesting performance. Finally, the energy harvesting performance of the proposed design is compared with the design without the consideration of the wave cancellation.
Energy Scanning for Designing Self-Generative Power Grid to Operate Wireless Sensors in Health Sensing Modules Mr. Heonjun YOON, Byeng D. YOUN, Miso KIM and Choon-Su PARK (Seoul National University, Seoul National University / OnePredict Inc., Korea Research Institute of Standards and Science)
The effectiveness of Prognostics and Health Management (PHM) in operation reliability improvement and failure prevention strongly depends on health-relevant information conveyed by a sensory signal. The advances in wireless communications and low power electronics have allowed the deployment of Wireless Sensor Network (WSN) for a health sensing module in the PHM. However, since the powering of the wireless sensors has still relied on a chemical battery, the limited lifespan of the battery makes it difficult to use the WSN, especially when their replacement is needed in inaccessible and remote locations. Energy Harvesting (EH) has received much attention as a possible solution of a self-generative power grid to sustainably operate the wireless sensors. Piezoelectric Vibration Energy Harvesting (PVEH) is a technology that converts ambient, otherwise wasted, vibration energy into electric power in response to the mechanical strain. Prior to selecting best sites for installation of the PVEH devices, it is of great importance to preliminary scan the amount of harvestable electric power in a cost-effective manner. This study thus aims at establishing an Energy Scanning (ES) concept to provide a guideline on how to optimally design the number and the location of the PVEH devices for the self-generative power grid in the health sensing module. The backbone idea of the ES is to implement the stochastic electroelastically-coupled analytical model using time-frequency analysis for quantifying the expected output power based on a non-stationary random vibration signal acquired from an engineered system. The five-fold steps are systematically organized with an aim to realize the ES as: (1) generation of an amplitude- and frequency-modulated signal, (2) estimation of the time-variant power spectral density of the input signal by the smoothed pseudo Wigner-Ville distribution, (3) derivation of the frequency response function for the output voltage by solving simultaneously the electroelastically-coupled mechanical equation of motion with the Rayleigh-Ritz method under the Kirchhoff hypothesis, and the electrical circuit equation with the Gauss’s law, (4) calculation of the time-varying output power from the autocorrelation function of the output voltage, and (5) experimental validation of the predictive capability of the ES. The contribution of this study lies in that the ES plays an essential role in scheduling the operation time interval of wireless sensors when the amount of harvestable electric power is larger than the threshold at which it is set to activate the operation.