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  • Events
    Current Location: English > Events > Content
    Seminar on Intelligent Fluid Mechanics
    2019-12-05   審核人:

     

    Time:Thursday, Nov 28, 2019

    Venue: Morning Session at A310, Afternoon Session at A706

    Time

    Topic/Title

    Speaker

    Chair

    09:00~10:20

    Machine-Learning Based Active Flow Control

    Hui Tang

    Associate Professor

    The Hong Kong Polytechnic University

    Prof. Weiwei Zhang

    coffee break

    10:30~11:50

    Machine-Learningfor Turbulence Modeling

    Richard P. Dwight

    Professor

    Delft University of Technology

    14:30~15:00

    Group Research Introduction

    Aerodynamics and Fluid-Structure Interaction

    Weiwei Zhang

    Professor

    Vice Dean of School of Aeronautics

    Northwestern Polytechnical University

    Prof. Chen Gang

    15:00~15:30

    ANew Evolutionary Optimization Framework Based on

    Model Prediction for Aerodynamic Design

    Xiaojing Wu

    Lecturer

    School of Aerospace Science and Technology

    Xidian University

    15:30~16:00

    ANovel Spatial-Temporal Prediction Method for Unsteady Wake Flows Based on Hybrid Deep Neural Network

    Renkun Han

    Doctoral student

    Xi'an Jiaotong University

    16:00~16:10

    coffee break

    16:10~16:40

    Study on High Reynolds Number Turbulence Modeling

    Based on Machine Learning methods

    Linyang Zhu

    Doctoral student

    Northwestern Polytechnical University

    Associate Prof. Li Chunna

    16:40~17:10

    Airfoil Dynamic Stall Aerodynamic Prediction Method

    Based on Data Fusion Model

    Xu Wang

    Doctoral student

    Northwestern Polytechnical University

    17:10~17:40

    Adaptive Control of The Transonic Buffet Flow

    Kai Ren

    Doctoral student

    Northwestern Polytechnical University

    Invited lecture one

    Title:Machine-Learning Based Active Flow Control.

    Lecturer:Dr. Hui TANG, the Hong Kong Polytechnic University

    Abstract:

    In this talk, some recent applications of machine learning (ML) in active flow control (AFC) will be introduced. Here the term AFC means that the control is realized by injecting a small amount of energy into existing flow systems. These applications contain generic-programming (GP) method in vortex-induced vibration (VIV) control, deep reinforcement learning (DRL) for eliminating the velocity deficit, and DRL for finding best drag reduction strategies. Through these ML based AFC studies, some new and unexpected control strategies have been revealed.

    Biography:

    Dr. Hui Tang is an Associate Professor, Director of Research Center for Fluid-Structure Interactions, and Associate Head of Department of Mechanical Engineering, The Hong Kong Polytechnic University. He received his BEng and MEng degrees from Tsinghua University, and his PhD degree in Aeronautical Engineering from University of Manchester. Prior to joining HK PolyU, he worked in Nanyang Technological University, and University of Michigan - Ann Arbor. His research interests include aerodynamics/hydrodynamics, active flow control, fluid-structure interaction, and heat and mass transfer.

    Invited lecture two

    Title:Data-driven turbulence modelling with Machine-learning.

    Lecturer:Prof Richard P. Dwight, Delft University of technology

    Abstract:

    Several groups worldwide are investigating numerical and statistical methods for deriving turbulence models directly from this data-corpus - efforts which generally involve some form of machine-learning. We will give an overview of this nascent field, and the key results and observations obtained so far. We discuss our own work in the area, and finally application of our techniques to shape-optimization and to wind-farm wake modelling.

    Biography:

    Professor Richard P. Dwight received his Ph.D. from the University of Manchester in 2006, worked as an associate professor in the Department of aerodynamics at the school of Aerospace Engineering, Delft University of technology since 2009, and as a visiting professor at the Centrum voor Wiskunde en Informatica (CWI) in The Netherlands since 2017. Professor Richard P. Dwight has made great achievements in computational fluid dynamics, machine learning, aerodynamic design optimization, surrogate-based optimization method, adjoint method, uncertain optimization method and other fields.

    This seminar is sponsored by the overseas Expertise Introduction Center for Discipline Innovation on Complex Flow and Its Control (the 111 Center), School of Aeronautics.

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