Introduction

CFAM is affiliated to the School of Aeronautics & Astronautics at Shanghai Jiao Tong University, China. It locates at Aeronautics & Astronautics Building on the Minhang campus of Shanghai Jiao Tong University. The Group Leader of CFAM is Dr. Hui XU.

CFAM” represents “Complex Flows and Applied Mathematics”. The main research interests of CFAM include :

    • Hydrodynamic instability (laminar-turbulent transition)
    • Artificial intelligence & machine learning in Fluid Mechanics
    • Turbulence & Aeroacoustics
    • Numerical analysis and high fidelity numerical methods
    • Kinetic theory-based modelling/methods
    • Other related areas

Our researches are/were supported by

  •  . . .

The members of CFAM include postdoctoral fellows, PhD candidates, M.SC. students, undergraduate students, etc. If you want to

    • Apply for a domestic visiting scholar or postdoctoral fellow
    • Study for a PhD or master’s degree
    • Do an undergraduate thesis (limited to students of Shanghai Jiao Tong University)

To join the research group CFAM, please contact Hui XU with related materials including a resume with regular life photo, undergraduate transcript (for master’s degree), one-page “research motivation description”, and other materials that can help us know you better.

Please note that all applicants have to go through an interview in advance (the interview procedure is the same, and the final result depends on the two-way choice between you and your tutor).

Research Member

Group Leader


Hui XU, Dr.

Associate Professor in the School of Aeronautics & Astronautics at Shanghai Jiao Tong University and Honorary Research Fellow at Imperial College London. I am now supported by “Overseas High-level Talents Program” (Shanghai Specially Recruited Experts). Before, I was a Rolls-Royce researcher in the Department of Aeronautics and a researcher in the Department of Mathematics at Imperial College London. My research interests span applied/computational mathematics and fluid mechanics: hydrodynamic instability (laminar-turbulent transition), aeroacoustics, turbulence, kinetic theory-based modelling/method, numerical analysis and methods. I am currently working on broadband noise modelling and prediction, laminar-turbulent transition, machine learning. Especially, I have been working on spectral/hp element methods and am a developer of the opensource software Nektar++. I was also one of the pioneering porotype designers of LaBS and a developer of Palabos. My researches are/were supported by NSFC, EPSRC, Rolls-Royce, Airbus, Bombardier, McLaren, Renault and so on. My research papers were mainly published in Journal of Fluid Mechanics, SIAM Journal on Scientific Computing, Journal of Computational Physics and so on.

Access our research papers by

Email: dr.hxu@sjtu.edu.cn

Continue reading “Research Member”

Research Focus-on

Research Highlights

High-Order Numerical Methods Laminar-Turbulent Transition Deep Reinforcement Learning We are devoted to exert a combination between deep reinforcement learning (DRL) and high-accuracy spectral/hp element methods to explore the underlying physics mechanism of flow, leading to a development of fluid mechanics. As the following figures show, the wake of cylinder is controlled by the control law …

Collaborations

We have been and will be working with world-leading experts in the fields of applied/computational mathematics and fluid mechanics.

Domestic Collaborations


Wenquan Tao, CAS Member

Professor at Xi’an Jiaotong University. As the professor of Energy and Power Engineering and doctoral tutor of Xi’an Jiaotong University, he was ever awarded one of the first session National Famous Teachers in 2003 and elected the academician of the Chinese Academy of Sciences in 2005. He is my Ph.D supervisor.


International Collaborations


Spencer J. SherwinFREng, FRAeS

Professor at Imperial College London.  Head of Aerodynamics and Professor of Computational Fluid Mechanics in the Department of Aeronautics and Director of Research Computing Service at Imperial College London. He received his MSE and PhD from the Department of Mechanical and Aerospace Engineering Department at Princeton University. Prior to this he received his BEng from the Department of Aeronautics at Imperial College London.


Continue reading “Collaborations”

Teaching

  • Numerical Analysis (Fall-term)

This course is a master’s degree course for all graduate students except those of the Department of Math. Numerical Analysis provides the numerical methods and theoretical foundation for a multitude of mathematical problems solving by computer.

The objective of the course includes: 1) know the source of the numerical computation problems, 2) understand the basic idea and the theory of the solving method, the construction principle of the methods, 3) apply the corresponding methods and the formula, 4) analysis the source of the error and know how to control the error, 5) explain the meaning of the numerical results, 6) make a prediction based on the numerical results. After the study of this course, the students will gain basic experience such as to other fields which they may encounter later in the professional career.

This course includes the basic contents of numerical methods: numerical algebra, numerical approximation, numerical integration, numerical solution of equations, numerical solution of ordinary differential equations.

  • Fundamentals of Aerodynamics (Spring-term)

Aerodynamics I is designed for the students majoring in aircraft design disciplines of aeronautics and astronautics school/department. It covers the following topics: fundamentals concepts of aerodynamics; principles of inviscid incompressible flows; inviscid, incompressible flows over airfoils and finite wings; boundary layer; etc. By learning the course, students can rapidly master fundamental concepts and principles of aerodynamics, formulate and apply appropriate aerodynamic models, and assess the applicability of various aerodynamic models, thus establish the basis for their future research work on aerodynamics and aircraft design.