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Model, predict, optimise: Real-time approaches for unpredictable fluids

Time: Fri 2022-04-01 15.00 - 16.00

Location: Faxén, Teknikringen 8

Video link: Hybrid e-Seminar (Zoom)

Participating: Luca Magri (Imperial College)

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Abstract: The ability of fluid mechanics modelling to predict the evolution of a flow is enabled by physical principles and empirical approaches. Physical principles, for example conservation laws, are extrapolative (until the assumptions upon which they hinge break down): they provide predictions on phenomena that have not been observed. Human beings are excellent at extrapolating knowledge because we are excellent at finding physical principles. Empirical modelling provides correlation functions within data. Artificial intelligence and machine learning are excellent at empirical modelling. In this talk, the complementary capabilities of both approaches will be exploited to achieve adaptive modelling and optimization of nonlinear, unsteady and uncertain flows. The focus of the talk is on computational methodologies (i) to model chaotic thermoacoustic oscillations and discover new sources of sound, predict their evolution with real-time data assimilation, and optimise their behaviour with a gradient-free method; and (ii) to predict and suppress extreme turbulent events with physics-aware auto-encoder echo state networks (AE-ESN). The flows under investigation are relevant to aerospace propulsion and turbulence.

Bio:  Dr. Magri is a Reader in data-driven fluid mechanics at Imperial College London, Aeronautics Department, a Fellow of The Alan Turing Institute, Hans Fischer Fellow of the Institute for Advanced Study (TU Munich), and Affiliated Lecturer at Cambridge University Engineering Department. Prior to joining Imperial, Dr. Magri was a Lecturer at Cambridge University Engineering Department, Royal Academy of Engineering (RAEng) Research Fellow, and Fellow of Pembroke College, and a postdoctoral fellow at Stanford University Center for Turbulence Research. Dr. Magri obtained his PhD in Engineering at the University of Cambridge, and his research is currently funded by an ERC Starting Grant and the UKRI Excalibur grant on exascale computing. At The Alan Turing Institute, Dr. Magri is group leader of Physics-Informed Machine Learning and Data Assimilation under the Data-Centric Engineering Programme

Page responsible:Ardeshir Hanifi
Belongs to: FLOW
Last changed: Feb 25, 2022