Seminar on Intelligent Fluid Mechanics

      Release Date:Nov 28, 2019      Click:次     Audit:航空学院


Time:Thursday, Nov 28, 2019

Venue: Morning Session at A310, Afternoon Session at A706






Machine-Learning Based Active Flow Control

Hui Tang

Associate Professor

The Hong Kong Polytechnic University

Prof. Weiwei Zhang

coffee break


Machine-Learningfor Turbulence Modeling

Richard P. Dwight


Delft University of Technology


Group Research Introduction

Aerodynamics and Fluid-Structure Interaction

Weiwei Zhang


Vice Dean of School of Aeronautics

Northwestern Polytechnical University

Prof. Chen Gang


ANew Evolutionary Optimization Framework Based on

Model Prediction for Aerodynamic Design

Xiaojing Wu


School of Aerospace Science and Technology

Xidian University


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

Renkun Han

Doctoral student

Xi'an Jiaotong University


coffee break


Study on High Reynolds Number Turbulence Modeling

Based on Machine Learning methods

Linyang Zhu

Doctoral student

Northwestern Polytechnical University

Associate Prof. Li Chunna


Airfoil Dynamic Stall Aerodynamic Prediction Method

Based on Data Fusion Model

Xu Wang

Doctoral student

Northwestern Polytechnical University


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


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.


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


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.


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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