IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i21p3928-d951024.html
   My bibliography  Save this article

Low Dissipative Entropic Lattice Boltzmann Method

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/21/3928/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/21/3928/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rasti, Ehsan & Talebi, Farhad & Mazaheri, Kiumars, 2019. "Improvement of drag reduction prediction in viscoelastic pipe flows using proper low-Reynolds k-ε turbulence models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 412-422.
    2. Afrouzi, Hamid Hassanzadeh & Ahmadian, Majid & Moshfegh, Abouzar & Toghraie, Davood & Javadzadegan, Ashkan, 2019. "Statistical analysis of pulsating non-Newtonian flow in a corrugated channel using Lattice-Boltzmann method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    3. Dolatabadi, Peiman Davari & Khanlari, Karen & Ghafory Ashtiany, Mohsen & Hosseini, Mahmood, 2020. "System identification method by using inverse solution of equations of motion in time domain and noisy condition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    4. Ezzatneshan, Eslam & Vaseghnia, Hamed, 2020. "Evaluation of equations of state in multiphase lattice Boltzmann method with considering surface wettability effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    5. Jahangiri, Ali & Mohammadi, Samira & Akbari, Mohammad, 2019. "Modeling the one-dimensional inverse heat transfer problem using a Haar wavelet collocation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 13-26.
    6. Bagherzadeh, Seyed Amin & D’Orazio, Annunziata & Karimipour, Arash & Goodarzi, Marjan & Bach, Quang-Vu, 2019. "A novel sensitivity analysis model of EANN for F-MWCNTs–Fe3O4/EG nanofluid thermal conductivity: Outputs predicted analytically instead of numerically to more accuracy and less costs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 406-415.
    7. Tian, Zhe & Arasteh, Hossein & Parsian, Amir & Karimipour, Arash & Safaei, Mohammad Reza & Nguyen, Truong Khang, 2019. "Estimate the shear rate & apparent viscosity of multi-phased non-Newtonian hybrid nanofluids via new developed Support Vector Machine method coupled with sensitivity analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    8. Bahrami, Mehrdad & Akbari, Mohammad & Bagherzadeh, Seyed Amin & Karimipour, Arash & Afrand, Masoud & Goodarzi, Marjan, 2019. "Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: Measure MSEs between targets & ANN for Fe–CuO/Eg–Water nanofluid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 159-168.
    9. Alsarraf, Jalal & Moradikazerouni, Alireza & Shahsavar, Amin & Afrand, Masoud & Salehipour, Hamzeh & Tran, Minh Duc, 2019. "Hydrothermal analysis of turbulent boehmite alumina nanofluid flow with different nanoparticle shapes in a minichannel heat exchanger using two-phase mixture model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 275-288.
    10. Rostami, Sara & Afrand, Masoud & Shahsavar, Amin & Sheikholeslami, M. & Kalbasi, Rasool & Aghakhani, Saeed & Shadloo, Mostafa Safdari & Oztop, Hakan F., 2020. "A review of melting and freezing processes of PCM/nano-PCM and their application in energy storage," Energy, Elsevier, vol. 211(C).
    11. Garcia, Salvador, 2017. "Chaos in the lid-driven square cavity," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 142(C), pages 98-112.
    12. Khaje khabaz, Moahamad & Eftekhari, S. Ali & Hashemian, Mohamad & Toghraie, Davood, 2020. "Optimal vibration control of multi-layer micro-beams actuated by piezoelectric layer based on modified couple stress and surface stress elasticity theories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 546(C).
    13. Nafchi, Peyman Mirzakhani & Karimipour, Arash & Afrand, Masoud, 2019. "The evaluation on a new non-Newtonian hybrid mixture composed of TiO2/ZnO/EG to present a statistical approach of power law for its rheological and thermal properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 1-18.
    14. Ma, Yuan & Mohebbi, Rasul & Rashidi, M.M. & Yang, Zhigang & Sheremet, Mikhail, 2020. "Nanoliquid thermal convection in I-shaped multiple-pipe heat exchanger under magnetic field influence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    15. Mohsen Gorakifard & Clara Salueña & Ildefonso Cuesta & Ehsan Kian Far, 2021. "Analysis of Aeroacoustic Properties of the Local Radial Point Interpolation Cumulant Lattice Boltzmann Method," Energies, MDPI, vol. 14(5), pages 1-18, March.
    16. 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.
    17. Brownlee, R.A. & Gorban, A.N. & Levesley, J., 2008. "Nonequilibrium entropy limiters in lattice Boltzmann methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 385-406.
    18. Shahsavar, Amin & Bagherzadeh, Seyed Amin & Mahmoudi, Boshra & Hajizadeh, Ahmad & Afrand, Masoud & Nguyen, Truong Khang, 2019. "Robust Weighted Least Squares Support Vector Regression algorithm to estimate the nanofluid thermal properties of water/graphene Oxide–Silicon carbide mixture," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1418-1428.
    19. Laura Frouté & Yuhang Wang & Jesse McKinzie & Saman A. Aryana & Anthony R. Kovscek, 2020. "Transport Simulations on Scanning Transmission Electron Microscope Images of Nanoporous Shale," Energies, MDPI, vol. 13(24), pages 1-14, December.
    20. Karimipour, Arash & Bagherzadeh, Seyed Amin & Taghipour, Abdolmajid & Abdollahi, Ali & Safaei, Mohammad Reza, 2019. "A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO/water hybrid nanofluid based on empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 89-97.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:3928-:d:951024. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.