Chang Shu
National University of Singapore
453 Papers
2.1K Citations
Chang Shu is an academic researcher from National University of Singapore. The author has contributed to research in topics: Lattice Boltzmann methods & Boundary value problem. The author has an hindex of 63, co-authored 440 publications. Previous affiliations of Chang Shu include Tongji University & University of Birmingham.
Chat about Author
Papers
Implementation of clamped and simply supported boundary conditions in the GDQ free vibration analysis of beams and plates
Chang Shu,Hong-Yan Du +1 more
TL;DR: In this article, a new methodology for implementing the clamped and simply supported boundary conditions is presented for the free vibration analysis of beams and plates using the generalized differential quadrature (GDQ) method.
230
Combustion in micro-cylindrical combustors with and without a backward facing step
TL;DR: In this paper, the experimental results of three types of stainless cylindrical micro-combustors with or without a backward facing step were presented, where hydrogen was used as the fuel and the temperatures at exit and along the wall of the combustors were measured.
216
A novel immersed boundary velocity correction-lattice Boltzmann method and its application to simulate flow past a circular cylinder
Chang Shu,Ning Liu,Y. T. Chew +2 more
TL;DR: A novel immersed boundary velocity correction-lattice Boltzmann method is presented and validated in this work by its application to simulate the two-dimensional flow over a circular cylinder, which directly corrects the velocity to enforce the physical boundary condition.
207
An SPH model for multiphase flows with complex interfaces and large density differences
TL;DR: An improved SPH model for multiphase flows with complex interfaces and large density differences is developed, and a corrected density re-initialization is applied to improve computational accuracy and to obtain smooth pressure fields.
207
Inverse Design of Airfoil Using a Deep Convolutional Neural Network
TL;DR: This paper proposes an approach to perform the inverse design of airfoils using deep convolutional neural networks (CNNs) that are based on the solution of differential equa...
171