Smoothed Particle Hydrodynamics (SPH): an Overview and Recent Developments

By Moubin Liu, Guirong Liu. 2010

1,863 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computational Mechanics, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/6ae2960b7cbeab3e1969033b343dbe3594c99cb3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. A. Zainali
2. Mostafa Safdari Shadloo
3. Moubin Liu
4. Guirong Liu
5. Antonio Souto-Iglesias
6. Tarek I. Zohdi
7. M. A. Russell
8. Mehmet Yildiz

In Computational Mechanics

1. G. Em Karniadakis
2. Zhiping Mao
3. Mostafa Safdari Shadloo
4. A. Zainali
5. Shengze Cai
6. Mehmet Yildiz
7. Moubin Liu
8. Zhicheng Wang

In Mechanics of Materials

1. Moubin Liu
2. A. Zainali
3. Mostafa Safdari Shadloo
4. Guirong Liu
5. Antonio Souto-Iglesias
6. Tarek I. Zohdi
7. M. A. Russell
8. Mehmet Yildiz

Citations in the corpus

• Numerical simulation of Laser Fusion Additive Manufacturing processes using the SPH method (2018, methodology): “Derivation of the fundamental SPH discretization involves two approximations, the so called Kernel Approximation and the Particle Approximation [10].”
• Experimental and numerical investigation of microstructure and mechanical behavior of titanium/steel interfaces prepared by explosive welding (2017, background): “The function values and their derivatives are found by a kernel approximation instead of being constructed from a grid [24].”
• Improved Incompressible Smoothed Particle Hydrodynamics method for simulating flow around bluff bodies (2011, background): “In literature, it is possible to find a wide variety of kernel functions which satisfy above-listed conditions, such as Gaussian, cubic or quintic kernel functions [18,23].”
• A robust weakly compressible SPH method and its comparison with an incompressible SPH (2012, background): “A thorough discussion on the details of these attributes of the kernel function can be found in [29] and the references therein.”
• Physics-informed neural networks (PINNs) for fluid mechanics: a review (2021, background): “1 Division of Applied Mathematics, Brown University, Providence, RI 02912, USA 2 School of Mathematical Sciences Xiamen University, 361005 China 3 Laboratory of Ocean Energy Utilization of Ministry of Education, Dalian University of Technology, Dalian, 116024, China 4 School of Engineering, Brown Un”
• An improved SPH method for modeling liquid sloshing dynamics (2012, background): “The sloshing of liquefied fuel inside the fuel tank in an aeronautic or astronautic craft can disturb or even breakdown normal navigation of the craft.”