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                  語言: CN

                  科學研究SCIENTIFIC RESEARCH

                    發表時間:2018-05-10  閱讀次數:1491




                  報告題目:Mathematical Methods of Turbulence Control

                  Lecture #1: ROM-based Control (POD, Galerkin method, Mean-field modeling)

                  Lecture #2: Machine Learning Control (MLC)

                  Lecture #3: Feature space, manifolds and cluster-based ROM (CROM)

                  報 告 人:Prof. Dr. Bernd Rainer Noack

                  Director of Research CNRS at LIMSI, Paris-Saclay

                  Honorary Professor and Chair in Turbulence Control at TU Berlin

                  Professor and Chair in Flow Modeling and Control at TU Braunschweig

                  Visiting Professor at Harbin Institute of Technology, Shenzhen

                  報告日期:2018年5月19日星期六 9:00-17:00


                  聯 系 人:楊志剛教授 電話69589240



                  Bernd Noack develops closed-loop turbulence control solutions for cars, airplanes and transport systems - in an interdisciplinary effort with leading groups in Europe, USA/Canada and China. He has been pushing the frontiers of turbulence control with nonlinear reduced-order modeling and the discovery of machine learning control. He has co-authored over 200 publications, 2 patents and 2 textbooks. His work has been honored by numerous awards, e.g. a Fellowship of the American Physical Society, a Senior ANR Chair of Excellence in France and an annual von Mises Award of International Association of Applied Mathematics and Mechanics.

                  He is Director of Research CNRS at LIMSI, Paris-Saclay, Honorary Professor and Chair in Turbulence Control at TU Berlin, Professor and Chair in Flow Modeling and Control at TU Braunschweig, and Visiting Professor at Harbin Institute of Technology, Shenzhen. Recent visiting professorships include University of Washington, University of New South Wales and Princeton. Past affiliations include the United Technologies Research Center, Max-Planck Society, German Aerospace Center and University of Göttingen.


                  This compact course describes state-of-the art methods of open- and closed-loop turbulence control targeting aerodynamic performance increases. Focus is placed on strategies with a proven track-record in experiments. The lectures start with classical linear modeling and control. Key nonlinearities of turbulence are distilled in reduced-order models (ROM) and corresponding control design. Then, we describe recent breakthroughs with machine learning control (MLC) which explores 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 data analysis to dynamic modeling and control.



                  學術報告-風洞中心-Bernd Noack


                  地址:寧遠館(中福在线连环夺宝)A202 郵編:201804 電子郵箱:qiche@tongji.edu.cn

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