Antonio Attili - Head of group
Room 2.2413, James Clerk Maxwell Building.
Peter Guthrie Tait Road, King's Buildings,
Edinburgh EH9 3FD
antonio.attili@ed.ac.uk
Antonio Attili is a Lecturer in Computational Reactive Flows at the University of Edinburgh and leads the TARFS Lab. He holds a Master's degree in Aerospace Engineering and a Ph.D. in Theoretical and Applied Mechanics from Sapienza University of Rome. Following his Ph.D., he worked at KAUST in Saudi Arabia and the Institute for Technical Combustion at RWTH Aachen in Germany. Antonio’s research interests include turbulence and combustion, soot formation in flames, aerosols, combustion instabilities, rocket propulsion, numerical methods, and massively parallel computing. He employs large-scale simulations to explore the physics of multiphysics and multiscale phenomena and to develop reduced models for industrial applications. He is also passionate about big-data analysis and applying machine learning and generative AI to fluid mechanics.
Geveen Arumapperuma Arachige - PhD student
Geveen Arumapperuma is a PhD student at the University of Edinburgh and a member of the TARFS group working under the supervision of Antonio Attili. He obtained his MEng degree in Mechanical Engineering from the University of Edinburgh in 2021. His research interests are in the field of turbulent reactive flows mainly focused on developing numerical models to predict soot formation. He is currently focusing on analysing large-scale simulations to investigate the physics of soot formation to develop reduced-order models for industrial applications.
Sofiane Al Kassar - PhD student
Panagiotis Alexandrou - PhD student
Panagiotis Alexandrou (Panos) is a PhD student under the supervision of Antonio Attili and a member of the TARFS group. He holds a Master’s degree in Mechanical Engineering from the University of Edinburgh, where his thesis, “On the Aerodynamic Performance of Wingfoil Sections,” explored the aerodynamic behavior of wing-like structures. His PhD research focuses on the effects of turbulence on the settling dynamics of discs using large-scale Direct Numerical Simulations (DNS). In the future, Panos aims to expand his work into turbulent combustion and compressible flows, deepen his expertise in High-Performance Computing (HPC), and leverage AI to uncover hidden mechanisms in turbulent flows and Fluid-Structure Interaction (FSI).
Mahsa Hassanpour - PhD student
