论坛时间：
2022年11月18日16:00（北京时间）
参会形式：
Zoom Meeting ID: 874 7360 8084
会议链接：
https://us02web.zoom.us/j/87473608084
主办单位：
北京航空航天大学国际交叉科学研究院
报告主题1：Digital Twin-Driven Aero-Engine Blade Health Monitoring and Damage Quantification
专家简介：
Ruqiang Yan is a Full Professor of the School of Mechanical Engineering, Xi’an Jiaotong University, China. His research interests include data analytics, AI and digital twin, and energy-efficient sensing and sensor networks for the condition monitoring, fault diagnosis and prognosis of large-scale, complex, dynamical systems. He is co-author of the books “Wavelets: Theory and Applications for Manufacturing” and “Structural Health Monitoring: An Advanced Signal Processing Perspective”, has published over 200 refereed journal and conference papers. Dr. Yan is a Fellow of IEEE (2022) and ASME (2019). His honors and awards include the IEEE Instrumentation and Measurement Society Technical Award in 2019 and multiple best paper awards. Dr. Yan is the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement, an Associate Editor of the IEEE Sensors Journal, and Editorial Board Member of Chinese Journal of Mechanical Engineering and Journal of University of Science and Technology of China.
报告简介：
Aero-engine blades, often suffering from harsh environments such as high rotating speed, high temperature, and heavy load, are prone to crack damage or even fatigue fracture.Effective Blade health monitoring (BHM) is vital to ensure the operation safety of aero engines. The emergence of digital twin technology provides a viable tool for virtual-real interaction of a complex dynamic system, which can help to identify and quantify potential faults in the dynamic system, such as aero-engine blade damages. This presentation will focus on the critical technology for the implementation of digital twin-driven BHM, including high-fidelity vibration modeling, real-time vibration reconstruction, and sparse regularization-based blade damage identification. A brief overview will be presented first, followed by some advancements and challenges in this field.
报告主题2：Key Technologies of Shape-performance Integrated Digital Twin for Major Equipment
专家简介：
Xueguan Song is currently a Professor in the School of Mechanical Engineering, Dalian University of Technology. He has published more than 200 peer-reviewed papers （over 100 SCI papers）, two books and two book chapters in various research fields including engineering optimization, computational fluid dynamics simulation and digital twins. His research interest includes multidisciplinary design optimization, surrogate modeling, AI and digital twin. He led the development of cloud-based and data-driven design optimization software DADOS (www.dados.com.cn). Dr. Song received many awards such as the Best Paper Award at QR2MSE2016 Conference, Best Poster Awards at CSO 2011 and PCO’2010 Conferences, and Honorable Mention Award in the PhD student paper symposium and competition at the 2010 ASME PVP Conference.
报告简介：
Digital twin is one of the key technologies to realize accurate prediction and reliable analysis of morphology and performance of major equipment. Considering the timeliness and accuracy requirements of the twin model, a shape-performance integration digital twin(SPI-DT) framework for major equipment is built. Additionally, six specific problems faced by building the digital twin of major equipment are discussed in detail, including “unrealizable calculation”, “inaccurate calculation”, “delayed calculation”, “unmeasurable data”, “incomplete measurement” and “inaccurate measurement”, and the relevant solutions and key technologies are given. The feasibility and validity of the proposed framework and key technologies are described by combining typical case, which provides a theoretical and methodical reference for the further application of digital twin in major equipment.
