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Volume 9, Issue 2
Compressible Lattice Boltzmann Method and Applications

B. He, Y. Chen, W. Feng, Q. Li, A. Song, Y. Wang, M. Zhang & W. Zhang

Int. J. Numer. Anal. Mod., 9 (2012), pp. 410-418.

Published online: 2012-09

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  • Abstract

Lattice Boltzmann Method (LBM) is a novel numerical method for flows simulations. Compared with classic methods of Finite Difference Method, Finite Volume Method and Finite Element Method, LBM has numerous advantages, including inherent parallelization and simplicity of boundary condition treatment. The LBM usually has a constraint of incompressible fluid (Mach number less than 0.4). A variant of the LBM is studied and used to deal with compressible fluid with Mach number up to 0.9 in this paper. Special emphasis is placed on mesh generation of 3-D complete geometry in Cartesian coordinate system. Numerical experiments are fulfilled in 2-D and 3-D compressible flows. Performance evaluation of the algorithm demonstrates high parallel efficiency and prefect scalability. Numerical results indicate that the LBM is successful with the simulation of compressible fluid.

  • Keywords

Lattice Boltzmann Method, Compressible fluid, Cartesian Mesh generation.

  • AMS Subject Headings

65Y05, 68U20, 76H05, 76G25

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{IJNAM-9-410, author = {B. He, Y. Chen, W. Feng, Q. Li, A. Song, Y. Wang, M. Zhang and W. Zhang}, title = {Compressible Lattice Boltzmann Method and Applications}, journal = {International Journal of Numerical Analysis and Modeling}, year = {2012}, volume = {9}, number = {2}, pages = {410--418}, abstract = {

Lattice Boltzmann Method (LBM) is a novel numerical method for flows simulations. Compared with classic methods of Finite Difference Method, Finite Volume Method and Finite Element Method, LBM has numerous advantages, including inherent parallelization and simplicity of boundary condition treatment. The LBM usually has a constraint of incompressible fluid (Mach number less than 0.4). A variant of the LBM is studied and used to deal with compressible fluid with Mach number up to 0.9 in this paper. Special emphasis is placed on mesh generation of 3-D complete geometry in Cartesian coordinate system. Numerical experiments are fulfilled in 2-D and 3-D compressible flows. Performance evaluation of the algorithm demonstrates high parallel efficiency and prefect scalability. Numerical results indicate that the LBM is successful with the simulation of compressible fluid.

}, issn = {2617-8710}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnam/638.html} }
TY - JOUR T1 - Compressible Lattice Boltzmann Method and Applications AU - B. He, Y. Chen, W. Feng, Q. Li, A. Song, Y. Wang, M. Zhang & W. Zhang JO - International Journal of Numerical Analysis and Modeling VL - 2 SP - 410 EP - 418 PY - 2012 DA - 2012/09 SN - 9 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnam/638.html KW - Lattice Boltzmann Method, Compressible fluid, Cartesian Mesh generation. AB -

Lattice Boltzmann Method (LBM) is a novel numerical method for flows simulations. Compared with classic methods of Finite Difference Method, Finite Volume Method and Finite Element Method, LBM has numerous advantages, including inherent parallelization and simplicity of boundary condition treatment. The LBM usually has a constraint of incompressible fluid (Mach number less than 0.4). A variant of the LBM is studied and used to deal with compressible fluid with Mach number up to 0.9 in this paper. Special emphasis is placed on mesh generation of 3-D complete geometry in Cartesian coordinate system. Numerical experiments are fulfilled in 2-D and 3-D compressible flows. Performance evaluation of the algorithm demonstrates high parallel efficiency and prefect scalability. Numerical results indicate that the LBM is successful with the simulation of compressible fluid.

B. He, Y. Chen, W. Feng, Q. Li, A. Song, Y. Wang, M. Zhang and W. Zhang. (2012). Compressible Lattice Boltzmann Method and Applications. International Journal of Numerical Analysis and Modeling. 9 (2). 410-418. doi:
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