Jointly trained at The University of Michigan and Peking University, Dr. Han obtained his Ph.D. degree in Fluid Mechanics from Peking University in 2017. He continued his research in the STFS institute at TU Darmstadt as a Postdoctoral Associate, then Associate Lecturer at UNSW Sydney, prior to joining The University of Edinburgh as a tenured Lecturer/Assistant Professor in March 2020.
With his expertise in physics-based and data-driven combustion modeling and CFD, Dr. Han has published original research in scientific journals including Combustion and Flame, Proceedings of the Combustion Institute, Fuel, etc. He won the prestigious Bernard Lewis Fellowship from the Combustion Institute in 2018.
In the Lab, we apply high performance computational fluid dynamics models, Machine Learning methods, and 3D Printing technologies to multiphase and reactive thermofluids that underpin the performance of propulsion & power systems and the dynamics of accidental fires. Our work at the nexus of big data and engineering applications is usually carried out with the aid of large-scale supercomputing resources with a view to making fundamental and practical impacts on problems of industrial relevance in transportation, power generation, and fire safety. Much of our work involves interaction with industry, with experimental researchers, and with international collaborators at universities and laboratories around the globe.
Computational Fluid Dynamics (CFD)
Combustion and Turbulence
Turbulent Combustion Modelling
Spray and Engine Simulation
Latest PhD/PDRA Positions:
PhD Project 2: Modelling and Simulation of Supercritical Fluid Mixing and Combustion (applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.).
PhD Project 1: Machine Learning for Clean Combustion (Tuition fees and stipend are available for Home/EU students. International and self-funded students can also apply, but the funding only covers the Home/EU fee rate).
Undergraduate and Master Students
Undergraduate and Master Students who are interested in Machine Learning, 3D printing, and multiphase & reactive flows, please contact Dr. Han.