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Fokker–Planck model for binary mixtures

Published online by Cambridge University Press:  24 July 2020

Samarth Agrawal
Affiliation:
Engineering Mechanics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bangalore560064, India
S. K. Singh
Affiliation:
Engineering Mechanics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bangalore560064, India
S. Ansumali*
Affiliation:
Engineering Mechanics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bangalore560064, India
*
Email address for correspondence: [email protected]

Abstract

The Fokker–Planck approximation to the Boltzmann equation has emerged as an efficient alternative to the discrete simulation Monte Carlo method for various flow simulations. This method has been mostly limited to simulating single-component rarefied gas flows. In the present paper, we propose two models based on the Fokker–Planck equation and quasi-equilibrium models that are capable of describing the dynamics of rarefied binary gas mixtures over a large range of Schmidt numbers. We first prove that these models satisfy the necessary conservation laws and the $H$-theorem. We validate the model by simulating three benchmark problems – Graham's law for effusion, Couette flow and binary diffusion.

Type
JFM Papers
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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References

REFERENCES

Agrawal, S., Bhattacharya, S. & Ansumali, S. 2018 Molecular dice: random number generators á la Boltzmann. Phys. Rev. E 98 (6), 063315.CrossRefGoogle Scholar
Arcidiacono, S., Karlin, I., Mantzaras, J. & Frouzakis, C. 2007 Lattice Boltzmann model for the simulation of multicomponent mixtures. Phys. Rev. E 76 (4), 046703.CrossRefGoogle ScholarPubMed
Arcidiacono, S., Mantzaras, J., Ansumali, S., Karlin, I., Frouzakis, C. & Boulouchos, K. 2006 Simulation of binary mixtures with the lattice Boltzmann method. Phys. Rev. E 74 (5), 056707.CrossRefGoogle Scholar
Bergman, T. L., Incropera, F. P., DeWitt, D. P. & Lavine, A. S. 2011 Fundamentals of Heat and Mass Transfer. Wiley.Google Scholar
Bhatnagar, P. L., Gross, E. P. & Krook, M. 1954 A model for collision processes in gases. I. Small amplitude processes in charged and neutral one-component systems. Phys. Rev. 94 (3), 511.CrossRefGoogle Scholar
Bird, R., Stewart, W. & Lightfoot, E. 1960 Transport Phenomena. Wiley.Google Scholar
Chapman, S. & Cowling, T. G. 1970 The Mathematical Theory of Non-uniform Gases: An Account of the Kinetic Theory of Viscosity, Thermal Conduction and Diffusion in Gases. Cambridge University Press.Google Scholar
Gardiner, C. W. 1985 Handbook of Stochastic Methods. Springer.Google Scholar
Gorban, A. N. & Karlin, I. V. 1994 a General approach to constructing models of the Boltzmann equation. Physica 206 (3–4), 401420.CrossRefGoogle Scholar
Gorban, A. N. & Karlin, I. V. 1994 b Method of invariant manifolds and regularization of acoustic spectra. Transp. Theory Stat. Phys. 23 (5), 559632.CrossRefGoogle Scholar
Gorji, H. & Jenny, P. 2012 A kinetic model for gas mixtures based on a Fokker–Planck equation. J.Phys.: Conf. Ser. 362, 012042.Google Scholar
Gorji, M. H. & Jenny, P. 2014 An efficient particle Fokker–Planck algorithm for rarefied gas flows. J.Comput. Phys. 262, 325343.CrossRefGoogle Scholar
Gorji, M. H., Torrilhon, M. & Jenny, P. 2011 Fokker–Planck model for computational studies of monatomic rarefied gas flows. J. Fluid Mech. 680, 574601.CrossRefGoogle Scholar
Grad, H. 1949 On the kinetic theory of rarefied gases. Commun. Pure Appl. Maths 2 (4), 331407.CrossRefGoogle Scholar
Hamel, B. B. 1965 Kinetic model for binary gas mixtures. Phys. Fluids 8 (3), 418425.CrossRefGoogle Scholar
Holway, L. H. Jr. 1966 New statistical models for kinetic theory: methods of construction. Phys. Fluids 9 (9), 16581673.CrossRefGoogle Scholar
Kloeden, P. E. & Platen, E. 2013 Numerical Solution of Stochastic Differential Equations, vol. 23. Springer Science & Business Media.Google Scholar
Lebowitz, J. L., Frisch, H. L. & Helfand, E. 1960 Nonequilibrium distribution functions in a fluid. Phys. Fluids 3 (3), 325338.CrossRefGoogle Scholar
Levermore, C. D. 1996 Moment closure hierarchies for kinetic theories. J. Stat. Phys. 83 (5–6), 10211065.CrossRefGoogle Scholar
Liboff, R. L. 2003 Kinetic Theory: Classical, Quantum, and Relativistic Descriptions. Springer Science & Business Media.Google Scholar
Lifschitz, E. & Pitaevskii, L. 1981 Physical Kinetics. Pergamon.Google Scholar
Mason, E. & Kronstadt, B. 1967 Graham's laws of diffusion and effusion. J. Chem. Educ. 44 (12), 740.CrossRefGoogle Scholar
Risken, H. 1996 The Fokker–Planck Equation, pp. 6395. Springer.CrossRefGoogle Scholar
Sharipov, F., Cumin, L. M. G. & Kalempa, D. 2004 Plane couette flow of binary gaseous mixture in the whole range of the Knudsen number. Eur. J. Mech. B/Fluids 23 (6), 899906.CrossRefGoogle Scholar
Singh, S. K. & Ansumali, S. 2015 Fokker–Planck model of hydrodynamics. Phys. Rev. E 91 (3), 033303.CrossRefGoogle ScholarPubMed
Singh, S. K., Thantanapally, C. & Ansumali, S. 2016 Gaseous microflow modeling using the Fokker–Planck equation. Phys. Rev. E 94 (6), 063307.CrossRefGoogle ScholarPubMed