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Gang Sun, Fudan University, China
Gang Sun is currently a Professor and the Dean of Department of Aeronautics and Astronautics, Fudan University. He receivved the Ph.D. degree in computational aerodynamics from the Northwestern Polytechnical University, Shaanxi, China, in 1994. He has participated and headed tens of projects in the fields of commercial aircrafts and aero-engines. He has authored or coauthored more the 200 research papers in foreign and domestic authorative journals. His research interests include filght vehicle design, aerodynamics, artificial intelligence methods and digital twin technologies for aircrafts and aero-engines. Prof. Sun currently serves as a member of Editorial Board for several academic journals, such as Acta Aerodynamica Sinica and Applied Mathematics and Mechanics.
Juan Du, Digital Twin Research Center at Institute of Engineering Thermophysics, Chinese Academy of Sciences, China
Juan Du is currently a professor and the head of Digital Twin Research Center at Institute of Engineering Thermophysics, Chinese Academy of Sciences. She received the B.Sc. degree in thermal energy and power engineering from University of Science and Technology Beijing, Beijing, China, in 2005 and the Ph.D. degree in power machinery and engineering from the Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing, China, in 2010. Her research interests include unsteady flow, rotating stall, flow control methods and digital twin in aeroengine and gas turbines. Du currently serves as a member of Editorial Board for Journal of Thermal Science, International Journal of Aerospace Engineering and the Young Editorial Board for Chinese Journal of Aeronautics.
Hong Xiao, Northwestern Polytechnical University, China
Hong Xiao is currently a Professor with the School of Power and Energy, Northwestern Polytechnical University, Xian, China. He received B.Sc.'s, Master's, and PHD degrees in aviation engines from Northwestern Polytechnical University in 2000, 2003, and 2006, respectively. Then he worked at the Hong Kong Polytechnic University, Gyeongsang National University in South Korea, Massachusetts Institute of Technology, and the University of Cambridge. His current interests include deep learning of aircraft engine data and digital twin of aircraft engines.
Jun Tao, Fudan University, China
Jun Tao is currently an Associate Research Fellow with the Department of Aeronautics and Astronautics, Fudan University, Shanghai, China. He received the B.Sc. degree in flight vehicle design and engineering from Fudan University in 2009, and the Ph.D. degree in fluid mechanics from Fudan University, in 2014. He was sponsored by Shanghai Pujiang Program in 2020. His current interests include flight vehicle design, aerodynamics, aeroacoustics, and artificial intelligence methods for aircrafts and aero-engines.
In recent years, digital twin technologies on aero-engines have received ubiquitous research concern with the rapid development of big data and artificial intelligence (AI). Digital twin technologies based on advanced data gonernance approaches and AI algorithms have been widely applied in many processes of the research and development of aero-engines, and the efficiency of the research and development of aero-engines has been improved obviously. The Special Session on Digital Twin Technologies of Aero-engines aims to provide a platform for researchers and practitioners to exchange the latest developments in this field, including the big data technologies, modelling methods of digital twin, prognostic and health management, digital experiments. Original and high-quality contributions related to digital twin technologies on aero-engines are welcomed.
Topics of interest for the Special Session include, but are not limited to:
Modelling methods of digital twin for aero-engines
Virtualization-reality interaction experiments for aero-engine
Prognostic and health management for aero-engine
Digital experiments for aero-engines
Data governance methods for aero-engines
Multi-source information fusion methods of aero-engines
近年来，随着大数据和人工智能技术的快速发展，航空发动机数字孪生技术受到了普遍的研究关注。基于先进数据治理技术和人工智能算法的数字孪生技术开始应用于航空发动机研制的各个阶段，大大提高了航空发动机研制的效率。本特别专题旨在为研究人员和从业者提供一个关于航空发动机数字孪生技术的交流平台，分享该领域的最新发展，如大数据技术、数字孪生建模方法、故障诊断与健康管理、数字化试验等。本次会议欢迎原创性且高质量的成果，展示航空发动机数字孪生技术的巨大应用潜力。
本特别专题感兴趣的主题包括但不限于：
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