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Low Dissipative Entropic Lattice Boltzmann Method

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  • Oleg Ilyin

    (Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Vavilova-44,2, 119333 Moscow, Russia)

Abstract

In the entropic lattice Boltzmann approach, the stability properties are governed by the parameter α , which in turn affects the viscosity of a flow. The variation of this parameter allows one to guarantee the fulfillment of the discrete H -theorem for all spatial nodes. In the ideal case, the alteration of α from its normal value in the conventional lattice Boltzmann method ( α = 2 ) should be as small as possible. In the present work, the problem of the evaluation of α securing the H -theorem and having an average value close to α = 2 is addressed. The main idea is to approximate the H -function by a quadratic function on the parameter α around α = 2 . The entropy balance requirement leads to a closed form expression for α depending on the values of the H -function and its derivatives. To validate the proposed method, several benchmark problems are considered: the Sod shock tube, the propagation of shear, acoustic waves, and doubly shear layer. It is demonstrated that the obtained formula for α yields solutions that show very small excessive dissipation. The simulation results are also compared with the essentially entropic and Zhao–Yong lattice Boltzmann approaches.

Suggested Citation

  • Oleg Ilyin, 2022. "Low Dissipative Entropic Lattice Boltzmann Method," Mathematics, MDPI, vol. 10(21), pages 1-22, October.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:3928-:d:951024
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    References listed on IDEAS

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    1. Ahmadi Balootaki, Azam & Karimipour, Arash & Toghraie, Davood, 2018. "Nano scale lattice Boltzmann method to simulate the mixed convection heat transfer of air in a lid-driven cavity with an endothermic obstacle inside," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 681-701.
    2. Latt, Jonas & Chopard, Bastien, 2006. "Lattice Boltzmann method with regularized pre-collision distribution functions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 72(2), pages 165-168.
    3. Tosi, F. & Ubertini, S. & Succi, S. & Chen, H. & Karlin, I.V., 2006. "Numerical stability of Entropic versus positivity-enforcing Lattice Boltzmann schemes," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 72(2), pages 227-231.
    4. Toghaniyan, Abolfazl & Zarringhalam, Majid & Akbari, Omid Ali & Sheikh Shabani, Gholamreza Ahmadi & Toghraie, Davood, 2018. "Application of lattice Boltzmann method and spinodal decomposition phenomenon for simulating two-phase thermal flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 673-689.
    5. Yue-Hong Qian, 1997. "Fractional Propagation and the Elimination of Staggered Invariants in Lattice-BGK Models," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 753-761.
    6. Jourabian, Mahmoud & Darzi, A. Ali Rabienataj & Toghraie, Davood & Akbari, Omid ali, 2018. "Melting process in porous media around two hot cylinders: Numerical study using the lattice Boltzmann method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 316-335.
    7. Chen, H. & Zhang, R. & Staroselsky, I. & Jhon, M., 2006. "Recovery of full rotational invariance in lattice Boltzmann formulations for high Knudsen number flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(1), pages 125-131.
    8. Gorban, A.N. & Packwood, D.J., 2014. "Enhancement of the stability of lattice Boltzmann methods by dissipation control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 285-299.
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