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航空学院青年学术论坛第175讲-流体力学智能化研讨会
2019-11-26 14:23   审核人:

 

  间:20191128日(星期四)

  点:西北工业大学友谊校区航空楼A310(上午)、A706(下午)

 

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-Learning for Turbulence   Modeling

Richard P. Dwight

Associate Professor

Delft University of Technology

 

 

14:30~15:00

Group Research Introduction

Aerodynamics and Fluid-Structure   Interaction

Weiwei Zhang

Professor

Northwestern Polytechnical   University

Prof.

Gang Chen

15:00~15:30

A New Evolutionary   Optimization Framework Based on Model Prediction for Aerodynamic Design

Xiaojing Wu

Lecturer

Xidian University

15:30~16:00

A Novel 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.

Chunna Li

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

 

 

邀请报告一

报告题目:Machine-Learning Based Active Flow Control.

报告人:Dr. Hui TANG, the Hong Kong Polytechnic University

摘要:

  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.

 

报告人简介:

  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.

 

 

邀请报告二

报告题目:Data-driven turbulence modelling with Machine-learning.

报告人:代尔夫特理工大学Richard P. Dwight教授

 

摘要:

   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.

 

报告人简介:

  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.

 

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