Research Scientist
Temasek Laboratories
National University of Singapore
Welcome to my personal academic webpage.
I will showcase some of my research works and interest.
Still a work in progress.
Feel free to contact me if you have any question.
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Thanks!
Research Interests
Computational Fluid Dynamics (CFD)
Biomimetic research such as flapping wing and fish simulations
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Immersed Boundary Method (IBM)
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Micro Aerial Vehicle (MAV)
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Unsteady flow
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Machine learning (Physics Informed Neural Network)
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Researcher Scientist
ABOUT ME
I was born in Singapore. I obtained my Bachelor of Engineering, Master of Engineering and PhD from the National University of Singapore in 2001, 2004 and 2009 respectively. During my graduate years, I was also a teaching assistant helping with tutorials for year 1 and 3 modules. I then worked as a part-time facilitator (tutor) at Republic polytechnic using the Problem-based learning (PBL) pedagogical structure for a year. Subsequently, I went to Taiwan’s National Taiwan University and the Netherlands’s Delft University of Technology to do my postdoctorate for 1 and 3 years respectively. I joined Temasek Laboratories in November 2013. My hobbies include table tennis, jogging and models making.
Working Experience
2000 (Internship)
DaimlerChrysler AG, Stuttgart, Germany
Industrial Attachment at the R&D (Computer system for production fields)
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2000 (Internship)
DaimlerChrysler AG, Stuttgart, Germany
Industrial Attachment at the R&D (Computer system for production fields)
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2009 - 2010
National Taiwan University, Taipei, Taiwan
Postdoctorate
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2010 - 2013
Delft University of Technology, Delft, The Netherlands
Postdoctorate
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2013 - Present
Temasek Laboratory
National University of Singapore, Singapore
Research Scientist
Current Research
Numerical simulation of a flapping wing MAV based on wing deformation capture analysis
The DelFly is a flapping MAV (FMAV) developed by the Delft University of Technology. It has two pairs of membrane wings which undergo counter-phase flapping. The membrane wings undergo large deformation during high speed flapping. We attempt to use cameras to perform a FMAV wing motion capture, and then use the wing shape captured as an input to the numerical solver. This will allow one to perform a realistic simulation of the FMAV. The results from the simulations provide us with visualization of the flow fields through pressure contours and vortical iso-surfaces, which enable us as to get a better understanding of the underlying flapping wing aerodynamics. The ability to accurately simulate the flapping MAV with membrane wings also opens up many opportunities for future simulations such as control surfaces, wing gust and optimization analysis.
V formation flight in Flapping MAVs
Current FMAVs are much more agile and maneuverable compared to fixed wing or quadcopter MAVs. However, they are still restricted by short endurance and low payload. This severely limits their capability for more complex mission. Besides improving battery life and wing kinematics efficiency, another way is to fly in swarms with some special formations, such as the V formation. It has proven that birds flying in V formation save more energy. Hence, the purpose of this research is to understand the aerodynamics of flapping wing micro aerial vehicles (FMAVs) flying in V formation and how the V formation can improve the endurance of FMAVs.
Numerical simulation and validation of a single-joint robotic fish in forward cruise
This research project is a collaboration between Prof. Xu Jianxin of the Department of Electrical & Computer Engineering (ECE) NUS and Temasek Laboratories (TL). In this research, we investigate the motion of a single-joint robotic fish in forward cruise through numerical simulation and experiments. The robotic fish is built by Prof. Xu’s research group. By using camera and Inertial Measurement Unit (IMU), we measure the steady-state speed, yaw, roll of the fish. Similar simulations are performed to validate with the experimental results. These results will be beneficial for the implementation of the feedback based motion planning and motion control algorithms.
Numerical simulation of biomimetic propeller designs to reduce noise
This research project is a numerical and experimental investigation of biomimetic propeller designs which includes serrated and nature inspired leading and trailing edge designs. Our objective is to maintain or improve thrust and efficiency while reducing noise generation in these propellers. The targeted platform is for small and medium sized quadrotors.
Numerical simulation of high speed flow
This research project is a numerical and experimental investigation of high speed flow phenomenon with focus on shock layer boundary interaction and ablation.
Numerical investigation of applying Physics Informed Neural Network (PINN) and variants on 2D aerodynamics problems
This research investigates the effects of using Artificial Neural Network (ANN) and Physics Informed Neural Network (PINN) for flow prediction of a converging-diverging nozzle. Results show that ANN by itself is already able to give relatively good prediction. With the addition of PINN, the error reduces even more, although by only a relatively small amount. However, in the case of little or no data, PINN sometimes outperforms ANN when the centerline boundary data is missing.
A framework has been designed to predict the flow fields and perform shape optimization of two-element airfoils using Nvidia Modulus, an open-source Physics Informed Neural Network (PINN) solver. Modulus integrates physical law constraints into the loss terms during training of its neural network so that its prediction honors the underlying physical principles. Modulus can predict flow fields and force coefficients for a range of geometric variables including angles of attack, airfoil thickness, and also over a range of fluid variables such as viscosity.
Optimization of Two-Element Airfoils Using Nvidia Modulus, a Physics-Informed Neural Network Solver
Numerical Simulation of a Bird-Inspired UAV Which Turns Without a Tail Through Proverse Yaw
This study numerically explores a bird-inspired tail-less unmanned aerial vehicle (UAV) design which can turn through proverse yaw by using a bell-shaped spanload wing configuration. The solver used is OpenFOAM and a special self-written routine is used to allow the grid to move together with the UAV, which has six degrees-of-freedom (6DOFs) to translate and rotate when its ailerons deflect after reaching steady motion. Results show that proverse yaw is indeed produced due to the bell-shaped spanload wing configuration, as CFD simulation shows the UAV turning after aileron deflection. The effect of the sweep angle is more profound on the proverse yaw as simulations show that increasing the sweep angle by 10â—¦ increases the turning effect slightly, but decreasing it by 10â—¦ instead results in adverse yaw.
Updated 12th October 2025
