Recent advances in reduced order models for complex nonlinear system
时间： 2017-07-17 来源： 信息员
Abstract: The presentations will cover fundamental elements of reduced order models that are developed to reduce a complex and nonlinear system of equations into a smaller and simpler set of equations. The advantages of doing this are the ability to run parametric searches for practical design and significantly reduce the time needed for the simulations. Reduced order models find large applications in any engineering (and non) field. We will look at several techniques, from a nonlinear projection method, to the harmonic balance and a multi-fidelity method. We will discuss the development of the reduced order model as an approximation to the original system, and the accuracy of the approximation applied to a number of practical test cases. These methods are currently being extended by the research group to new research areas, such as wall turbulence for example.
Part I: Dr Andrea Da Ronch
Part 2:Dr Jernej Drofelnik
Time: 2017.7.17 9:30-11:00
Address: North 328,No.1 building Teaching
Dr Andrea Da Ronch is a Lecturer at the University of Southampton, UK. He is a AIAA Senior Member and a member of the Atmospheric Flight Mechanics Technical Committee. In 2015 and 2016, he was awarded an Industrial Secondment Fellowship at Airbus UK sponsored by the Royal Academy of Engineering. Before joining the University of Southampton in 2013, he was a post-doctoral research graduate at the University of Liverpool, where he also received his PhD in 2012. He holds a MSc in Aeronautical Engineering from Politecnico of Milan, Italy, and KTH, Sweden. His research interests are in computational fluid dynamics, aeroelasticity, and aircraft design. He has contributed to the development of software tools like NeoCass, CEASIOM, and DLR-Tau.
Dr Jernej Drofelnik is a research associate at the University of Southampton working on computational aeroelasticity. Before joining the University in 2017, he was a PhD student at the University of Glasgow working on the development of a computational fluid dynamics solver for wind turbine applications.