Lattice Boltzmann Simulation of Surface Impingement at High-Density Ratio

2009 ◽  
Vol 23 (4) ◽  
pp. 773-785 ◽  
Author(s):  
Xin Gu ◽  
Amit Gupta ◽  
Ranganathan Kumar
Keyword(s):  
Lattice Boltzmann ◽  
Density Ratio ◽  
High Density ◽  
Lattice Boltzmann Simulation ◽  
High Density Ratio

The Lattice Boltzmann Equation Method for Complex Flows

2012 ◽  
Author(s):  
Laura Schaefer ◽  
Michael Ikeda ◽  
Jie Bao
Keyword(s):  
Boltzmann Equation ◽  
Lattice Boltzmann ◽  
Thermal Effects ◽  
Multiphase Flows ◽  
Density Ratio ◽  
Kinetic Equations ◽  
High Density ◽  
Lattice Boltzmann Equation ◽  
High Density Ratio ◽  
Density Ratios

The lattice Boltzmann equation (LBE) method is a promising technique for simulating fluid flows and modeling complex physics. Because the LBE model is based on microscopic models and mesoscopic kinetic equations, it offers many advantages for the study of multi-component or multiphase flows. However, there are still challenges encountered when dealing with thermal effects and multiphase flows, particularly at small scales or in varying geometries. In this paper, we discuss some techniques to overcome these challenges. First, we present an overview of the LBE method, and show how it can be extended to model multiple phases and thermal effects. Next, we describe our multi-component and multiphase (MCMP) LBE method for high density ratios. While the original formulation of Shan and Chen’s (SC) model can incorporate some multiphase and component scenarios, the density ratio of the different components is restricted (less than approximately 2.0), which limits the applications. Hence, based on the SC model and improvements in the single-component multiphase (SCMP) flow model reported by Yuan and Schaefer, we have developed a new model that can simulate a MCMP system with a high density ratio. An example of that system is shown. Finally, we have developed a parallel computation LBE method based on the Compute Unified Device Architecture for NVIDIA GPUs. Using this method, we are able to efficiently model a number of phases and length scales, examples of which are presented.

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