報(bào)告題目：Design of piezo-electric energy harvesters for Simultaneous Energy

???????????????? Harvesting and Sensing systems

報(bào)告時間：2024年10月9日（周三） 下午19:00-20:00

報(bào)告地點(diǎn)：建筑工程學(xué)院8號樓2樓報(bào)告廳

報(bào)告人：Elena Atroshchenko

報(bào)告人單位：新南威爾士大學(xué)土木與環(huán)境工程學(xué)院

報(bào)告人簡介：Dr. Elena Atroshchenko holds a PhD in Civil Engineering from University of Waterloo, Ontario, Canada (2010). At present, she is a Senior Lecturer at School of Civil and Environmental Engineering at the University of New South Wales, Sydney, Australia. Prior to her appointment at UNSW, she was an Assistant Professor at Department of Mechanical Engineering at University of Chile, Santiago, Chile. Dr. Atroshchenko’s expertise is in computational mechanics, numerical modelling and optimization, and scientific machine learning. Her research focuses on such areas as piezo-electric energy harvesting, design of meta-materials and meta-structures, physics informed neural networks.

報(bào)告摘要：In this seminar, we will introduce the concept of Simultaneous Energy Harvesting and Sensing (SEHS) system, where a single piece of hardware, a Piezo-electric Energy Harvester (PEH) is used for two objectives: harvesting energy from the source vibration and using the produced voltage signal to acquire information about the vibration source. In particular, we are interested in the design of SEHS for structural health monitoring of bridges. To simulate a healthy and damaged bridge response under passing vehicles we use a vehicle bridge interaction model solved with the finite element method. Subsequently, bridge acceleration serves as input to the PEH model to estimate the produced voltage. PEH model is a based on a bimorph Kirchoff-Love (KL) plate attached to a vibrating base. The system is solved using isogeometric analysis. In order to assess structural state of the bridge, convolutional variational auto-encoder (CVAE) is used. Since in real life, labelled data is usually not available, CVAE is trained on voltage data from a healthy bridge only, which can be characterized as unsupervised learning. Next, we perform a bi-objective optimization of a PEH with respect to energy harvesting performance and sensing accuracy.

邀請單位：黃淮學(xué)院建筑工程學(xué)院 河南省結(jié)構(gòu)力學(xué)與仿真計(jì)算國際聯(lián)合實(shí)驗(yàn)室