Integrating Experimental Diagnostics, Numerical Simulations, and Machine Learning Algorithms to Make Vehicles Cleaner and Smarter
Date: 2020/12/04 - 2020/12/04
Academic Seminar: Integrating Experimental Diagnostics, Numerical Simulations, and Machine Learning Algorithms to Make Vehicles Cleaner and Smarter
Speaker: David L.S. Hung, Associate Dean for Graduate Education, Professor of Mechanical Engineering, UM-SJTU Joint Institute, Shanghai Jiao Tong University
Time: 10:00 - 11:30, December 4, 2020 (Beijing Time)
Location: CIMC Auditorium (Room 300), JI Long Bin Building
via Zoom (Meeting ID: 67867585307, Password: 4394)
New vehicles equipped with advanced engine and powertrain technologies have demonstrated high potentials in improving thermal efficiency with better fuel economy and lower exhaust emissions. Using time-resolved particle image velocimetry (PIV) measurement and numerical large eddy simulation (LES) method, researchers can study the ensemble flow dynamics as well as their cycle-to-cycle variations in the combustion cylinder. However, it is still a daunting task to develop robust techniques for capturing highly transient 3D or simultaneous multi-plane in-cylinder flow-field data particularly for large number of engine cycles. In this seminar, a novel approach utilizing machine learning algorithms based on multi-cycle and multi-operating conditions of flow datasets extracted from PIV and LES is presented. Large PIV flow-field datasets and LES databases are employed for training machine learning algorithms and establishing flow characteristic models. Well-trained deep learning neural network models are capable of efficiently and accurately predicting the flow-field behavior. This research provides valuable knowledge for making vehicles cleaner and smarter by integrating experimental diagnostics and numerical simulations with machine learning algorithms to reveal the physics-based fundamentals of fluid dynamic mechanism.
Dr. David L.S. Hung is the Associate Dean for Graduate Education and Professor of Mechanical Engineering at the University of Michigan-Shanghai Jiao Tong University Joint Institute of Shanghai Jiao Tong University. His research focuses on green mobility, advanced powertrain systems, optical diagnostics, and data-driven analytics. He has received research funding from agencies such as China’s Ministry of Education, NSF-China, and other global automotive companies including General Motors and Nissan. He also received numerous teaching awards for his contributions to the Joint Institute’s reform and innovation in training engineering students. He is the first JI faculty member to be named a Top Ten SJTU “KoGuan Professor”. Prior to working in the Joint Institute, he was an associate professor in the Department of Mechanical Engineering at Michigan State University, USA. Previously he held professional positions in the US at General Motors, Delphi, and Visteon Corporations where he researched and developed powertrain components for use in advanced combustion engines.
He is currently serving as an Editor-in-Chief of Atomization and Sprays and an Associate Editor of the ASME Journal of Engineering for Gas Turbines and Power. He is also a voting member of the SAE (Society of Automotive Engineers) Gasoline Fuel Injection Standards Committee. He is a recipient of numerous professional awards, including ASME Internal Combustion Engine Division Outstanding Speaker Award, SAE Engineering Meetings Board Outstanding Oral Presentation Award, SAE Henry Souther Standards Award, and SAE/InterRegs Standards and Regulations Award for Young Engineers. He is a Fellow of SAE and a distinguished visiting fellow at the University of Oxford, sponsored by the Royal Academy of Engineering, United Kingdom.
He earned his Ph.D. in mechanical engineering from Carnegie Mellon University, USA.