报告人：Dr. Xutao Mei，The University of Tokyo

邀请人：黄冬梅

报告时间：2021年6月4日14:00-16:00

腾讯会议ID：326 606 360

报告人简介：Dr. Xutao Mei is currently a project researcher at the Department of Mechanical Engineering of the University of Tokyo (UT), working on the energy harvesting, nonlinear dynamic, soft sensing and data modeling. Before that, he spent half year at Institute of Industrial Science (IIS) of UT, as a postdoctoral fellow, and obtained his Ph.D degree from UT in Mar. 2020. He was a recipient of the MEXT Scholarship of Japanese Government from Oct. 2016 to Mar. 2020. He has published 10 journal papers, including 7 first-author journal papers on the top journal such as Joule, Nonlinear dynamic, Mechanical Systems and Signal Processing and Journal of Sound and Vibration, and 6 papers in International Conference, covering energy harvesting, nonlinear dynamic and soft sensing.

Title 1: Mathematical Problems in Mechanical Modeling of Rotational Energy Harvesting

Abstract：For rotational energy harvesting, the centrifugal force has a great influence on the dynamic performance of the designed harvesters. How to broaden the effective frequency range and optimize the structural parameters of rotational harvester is still a challenging issue. This presentation will introduce a novel mathematical model for beam-type harvesters to reveal the mechanisms of centrifugal softening effect and centrifugal stiffening effect, which can provide the theoretical guidance for the design of rotational energy harvesters, such as MEMS, in future.

Title 2: Application of Data Modeling in Soft Sensing

Abstract：With the development of intelligent industry, tons of data about industry operations have been measured by sensors or instruments. Now the soft sensor method based on the statistical analysis and machine learning has become popular in recent year in industry monitoring, control and prediction. The presentation will introduce an application of a soft sensor model to predict the crude oil fouling in a heat exchanger, and provide the theoretical guidance for optimizing the cleaning schedule in the future.