时 间：2019年11月28日（星期四）
地 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.
