Invite Speaker

Abstract: Advances in GNSS technologies and the ever-increasing capabilities of mobile devices have fuelled many useful applications and opportunities. In this talk, I will describe my PhD student’s journey in seeking techniques to improve the GNSS localization accuracy of low-cost mobile devices. In particular, the journey began with the well-established model-based design methods, evaluating their localization performances using raw GNSS measurements collected from low-cost Android devices. While model-based techniques can reduce the impact induced by pseudorange inaccuracy, it is still challenging to eliminate the pseudorange errors that were caused by multipath, non-line-of-sight (NLOS) propagation, etc. This journey demonstrated the value of marrying the knowledge encapsulated in the well-established model-based methods with the emerging datadriven paradigm.

Bio: Dr Keck Voon LING is an Associate Professor at the Nanyang Technological University (NTU) in the School of Electrical and Electronic Engineering (EEE). He is also the current Chairman of the EEE Accreditation Committee since 2020 and was the Deputy Programme Lead (2022-2025) of the Master of Science (Computer Control & Automation) Programme. He was previously Deputy Head of the Control and Automation Division (2006-2008) in the School of EEE, and was concurrently Programme Manager of A*STAR Embedded and Hybrid Systems II (EHS-II) Research Programme (2006-2010) where he oversaw R&D efforts in Body Sensor Networks for Healthcare Applications. In 2010, Dr LING chaired the International Conference on Body Sensor Networks that was held in Singapore. He was in the organising committee, served as Financial Chair, of the 2018 IEEE International Conference on Intelligent Rail Transportation. Dr LING obtained a BEng (First Class) in Electrical Engineering from the National University of Singapore in 1988 and a DPhil in Control Engineering at Oxford University in 1992. He was awarded the Commonwealth Fellowship by the Association of Commonwealth Universities in 2001 and the Tan Chin Tuan Fellowship by NTU in 2006. Dr LING was a visiting researcher at the Department of Engineering, University of Cambridge in 2001 and 2006. He was appointed as a senior scientist at the Singapore Institute of Manufacturing Technology (SIMTech) from 2005 to 2010. In 2016, he was a visiting by-fellow at Churchill College, Cambridge University. Dr LING’s research interests include Model Predictive Control, Receding Horizon Estimation, and their embedded implementation on reconfigurable computing platforms and applications. In recent years, his focus is on applying his research in control and estimation to multi-band GNSS software receiver, navigation and positioning applications, as well as smart grid and power systems applications.

Shuai Zhang
 Institute of Mechanics, Chinese Academy of Sciences (CAS)

Title: New Developments and Application Exploration of Multidisciplinary Design Optimization (MDO) for Aircraft

Abstract: Multidisciplinary Design Optimization (MDO) for aircraft was formally defined by AIAA in the 1990s. NASA subsequently introduced MDO processes into the development of several advanced demonstrators and related projects, significantly improving design efficiency and the degree of disciplinary integration. MDO technology was then gradually promoted and applied in industry. In 2007, the International Council on Systems Engineering (INCOSE) released a clear definition of Model-Based Systems Engineering (MBSE), and the MBSE methodology began to take shape. The integration of MDO and MBSE has since entered a stage of large-scale accelerated application. In 2016, GPUs were widely adopted for AI training; big data, deep learning, and multi-layer neural networks have driven the reintroduction of AI technology into MDO engineering applications, with MDO rapidly evolving toward intelligent systems engineering.This report first reviews cases of MDO methodology research and engineering applications in which the presenter participated or gained knowledge during doctoral studies at Nanjing University of Aeronautics and Astronautics, summarizing challenges faced by traditional MDO methods. It then describes the application and exploration of MDO in civil aircraft pre-research and conceptual design. Next, it introduces recent attempts by the team over the past three years to integrate AI technology into aircraft MDO. Finally, the presenter offers several reflections on the impact of AI technology on the future development and application of aircraft MDO.

Bio: Shuai Zhang, Senior Engineer, Institute of Mechanics, Chinese Academy of Sciences (CAS) Ph.D. in Aircraft Design from Nanjing University of Aeronautics and Astronautics (NUAA). Mainly engaged in aircraft conceptual design and multidisciplinary design optimization (MDO). Research focuses on aircraft design parameters analysis and multidisciplinary optimization methodologies. Developed design parameters analysis and optimization design workflows for various types of aircraft, which have been successfully applied in multiple production model programs and pre-research projects. Previously served as Technical Expert for Conceptual Design, Deputy Chief Engineer at the Aircraft Level, and Chief Engineer of the New-Energy Verification Aircraft at COMAC Beijing Research Center. Responsible for establishing the conceptual design process and multidisciplinary integrated optimization framework for new-energy aircraft. Joined the Institute of Mechanics, CAS in 2023. Currently serves as Head of the Aircraft MDO Team at the Wide range Flight Engineering Science and Applications Center (WESA), leading the construction and application of the Aircraft MDO platform.

       Xiangyu Meng
   Nanjing University of Aeronautics and Astronautics

Title: Three-dimensional Burning Surface Spatiotemporal Distribution Prediction Technology for Wheel-shaped Hybrid Rocket Motors

Abstract: The wheel-shaped hybrid rocket motor has the advantages of large thrust and continuous adjustability, which meets the demand for intelligent control of the power system of the new generation of air and aerospace vehicles. In order to realize the intelligent control of the motor, it is necessary to grasp the real-time performance of the motor, so it is necessary to carry out the numerical simulation research considering the dynamic change of the combustion surface, but the three-dimensional combustion surface spatio-temporal distribution is very complex, and the existing model is difficult to realize the accurate prediction of the combustion surface and performance, which seriously restricts the development of the motor performance prediction technology. Therefore, to carry out the wheel-shaped hybrid rocket motor three-dimensional combustion surface spatio-temporal distribution prediction technology research has an important application value. This paper establishes a three-dimensional dynamic numerical simulation model of wheel-shaped hybrid rocket motor, adopts the dynamic mesh technology based combustion surface prediction model and coupled with the discrete Laplace operator mesh smoothing method, completes the prediction of three-dimensional combustion surface spatial-temporal distribution of the wheel-shaped grain, and systematically analyzes the three-dimensional combustion surface spatial-temporal distribution and the change of performance parameters. This paper realizes the dynamic change of the combustion surface of wheel-shaped hybrid rocket motor, which provides a reference for the development of performance prediction technology of other complex fuel shape in hybrid rocket motors.

Bio: Associate Professor at the College of Astronautics, Nanjing University of Aeronautics and Astronautics. The main research focus includes advanced aerospace power system design, rotating detonation rocket engines, and hybrid rocket motors. He serves as a professional member of the International Deep Space Exploration Society, China Society of Astronautics, and Chinese Society of Aeronautics and Astronautics. He also holds editorial positions as a Young Editorial Board Member for Nano Micro Letters and Exploration, as well as for the Journal of National University of Defense Technology. He has published 30+ papers in reputable journals and conferences, including Aerospace Science and Technology, Chinese Journal of Aeronautics, and Acta Astronautica, and won the Wu Zhonghua Award for Outstanding Doctoral Dissertation by the Chinese Society of Engineering Thermophysics in 2025.

Mingliang Bai
Beihang University

Title: Superconducting Hydrogen-Electric Propulsion for Zero-Carbon Aviation: Multi-Field Coupling and System-Level Optimization

Abstract: Achieving carbon-neutral aviation demands a fundamental shift in propulsion paradigms. Liquid hydrogen, with its exceptional specific energy and zero-carbon combustion, combined with superconducting electrical systems capable of near-lossless power delivery at significantly reduced weight, offers a compelling pathway toward high-power, zero-emission flight. However, the deep integration of hydrogen energy conversion, superconducting power transmission, and cryogenic thermal management introduces complex multi-field coupling effects—spanning thermodynamic, electromagnetic, and electrochemical domains—that critically constrain system efficiency, stability, and scalability. These coupling challenges remain insufficiently understood and constitute a primary barrier to practical deployment. This report addresses the above issues through a systematic investigation of superconducting hydrogen-electric propulsion architectures. The representative system configurations and their hydrogen–electric–thermal coupling mechanisms are analyzed. Key enabling technologies, including hydrogen turbines, fuel cells, superconducting powertrains, and integrated cryogenic thermal management, are reviewed with focus on current bottlenecks. A multi-field coupling optimization framework is then proposed and validated on civil aircraft platforms, demonstrating improved energy efficiency and operational adaptability for future green aviation propulsion systems.

Bio: Dr. Mingliang Bai is currently a Postdoctoral Research Fellow at Beihang University, China. He received his Ph.D degree in Aerospace Propulsion Theory and Engineering from Beihang University in 2025. His research focuses on superconducting electric machine systems, integrated energy management, and hydrogen-powered aircraft. He has authored over 20 peer reviewed journal and conference papers, published in Int. J. Hydrog. Energy, Chin. J. Aeronaut., and Green Energy Intell. Transp., and participated in ten national and provincial research projects. Dr. Bai acts as a regular reviewer for numerous international journals and conferences. His academic honor includes the Science and Technology Progress Award of China Electrotechnical Society.