报告题目：MathematicalMethods of Turbulence Control
Lecture #1: ROM-basedControl (POD, Galerkin method,Mean-field modeling)
Lecture #2: MachineLearning Control (MLC)
Lecture #3: Featurespace, manifolds and cluster-based ROM (CROM)
报 告 人：Prof. Dr. Bernd Rainer Noack
Directorof Research CNRS at LIMSI, Paris-Saclay
HonoraryProfessor and Chair in Turbulence Control at TU Berlin
Professorand Chair in Flow Modeling and Control at TU Braunschweig
VisitingProfessor at Harbin Institute of Technology, Shenzhen
报告日期：2018年5月19日星期六 9:00-17:00
报告地点：嘉定校区上海地面交通工具风洞中心212会议室
联 系 人：杨志刚教授 电话69589240
个人简介：
Bernd Noack develops closed-loop turbulence control solutions for cars,airplanes and transport systems - in an interdisciplinary effort with leadinggroups in Europe, USA/Canada and China. He has been pushing the frontiers ofturbulence control with nonlinear reduced-order modeling and the discovery ofmachine learning control. He has co-authored over 200 publications, 2 patentsand 2 textbooks. His work has been honored by numerous awards, e.g. aFellowship of the American Physical Society, a Senior ANR Chair of Excellencein France and an annual von Mises Award of International Association of AppliedMathematics and Mechanics.
He is Director ofResearch CNRS at LIMSI, Paris-Saclay, Honorary Professor and Chair inTurbulence Control at TU Berlin, Professor and Chair in Flow Modeling andControl at TU Braunschweig, and Visiting Professor at Harbin Institute ofTechnology, Shenzhen. Recent visiting professorships include University ofWashington, University of New South Wales and Princeton. Past affiliationsinclude the United Technologies Research Center, Max-Planck Society, GermanAerospace Center and University ofGöttingen.
报告摘要：
This compact coursedescribes state-of-the art methods of open- and closed-loop turbulence controltargeting aerodynamic performance increases. Focus is placed on strategies witha proven track-record in experiments. The lectures start with classicallinear modeling and control. Key nonlinearities of turbulence aredistilled in reduced-order models (ROM) and corresponding control design. Then,we describe recent breakthroughs with machine learning control (MLC) whichexplores and exploits nonlinear actuation mechanisms in an automated manner.MLC may perform complex control optimization in a single 1-2 hour experiment.Finally, powerful auxiliary methods of machine learning are presented for dataanalysis to dynamic modeling and control.