Submitted successfully

Speaker Ⅲ

Biography: Dr. Hong Xiao is a full professor at Northwestern Polytechnical University. Prof. Xiao is author and co-author of more than 100 peer-reviewed publications and 4 books on a wide range of digital intelligence topics for aircraft engines. His main research activities are dedicated to artificial intelligence (AI) and digital twins of aero-engines, alongside digital test. Professor Xiao has pioneered a core technology that integrates aero-engine physics into AI algorithms. This technology has been harnessed for digital test of aero-engines, achieving a digital test capability that augments physical testing. It has found successful applications in various sectors, including aero-engines and gas turbines. Between 2012 and 2015, Professor Xiao was employed by Gyeongsang National University (South Korea) as a research professor, specializing in numerical simulations within the aerospace domain. From 2016 to 2019, he served as a researcher in the Department of Pure Mathematics, engaging in mathematical theoretical research on AI algorithms.

Speech Title: Artificial Intelligence and Digital Twin in Aircraft Engines

Abstract: With the  development of artificial intelligence (AI) technology, it has sparked keen interest among engineering and technical personnel across a wide range of industries.  This lecture delves into the strategies, theories, and achievements of integrating AI technology with complex equipment expertise, using aero-engines and gas turbines as illustrative examples. The presentation primarily encompasses the following three aspects: (1) Can the currently available AI algorithms be directly applied to the realm of complex equipment? (2) If AI algorithms cannot be directly utilized in the field of complex equipment, how can they be adapted? Furthermore, how can physical knowledge be incorporated into AI algorithm design? (3) Taking aero-engines and gas turbines as case studies, we will explore how AI technology can be integrated into digital twins, and how digital twins and digital testing in areas such as testing, manufacturing, and operation and maintenance can drastically reduce testing costs, expedite the development process.