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Fokker–Planck model for computational studies of monatomic rarefied gas flows

Published online by Cambridge University Press:  31 May 2011

M. H. GORJI*
Affiliation:
Institute of Fluid Dynamics, ETH Zentrum, Sonneggstrasse 3, 8092 Zürich, Switzerland
M. TORRILHON
Affiliation:
Department of Mathematics, RWTH Aachen University, Schinkestrasse 2, D-52062 Aachen, Germany
P. JENNY
Affiliation:
Institute of Fluid Dynamics, ETH Zentrum, Sonneggstrasse 3, 8092 Zürich, Switzerland
*
Email address for correspondence: [email protected]

Abstract

In this study, we propose a non-linear continuous stochastic velocity process for simulations of monatomic gas flows. The model equation is derived from a Fokker–Planck approximation of the Boltzmann equation. By introducing a cubic non-linear drift term, the model leads to the correct Prandtl number of 2/3 for monatomic gas, which is crucial to study heat transport phenomena. Moreover, a highly accurate scheme to evolve the computational particles in velocity- and physical space is devised. An important property of this integration scheme is that it ensures energy conservation and honours the tortuosity of particle trajectories. Especially in situations with small to moderate Knudsen numbers, this allows to proceed with much larger time steps than with direct simulation Monte Carlo (DSMC), i.e. the mean collision time not necessarily has to be resolved, and thus leads to more efficient simulations. Another computational advantage is that no direct collisions have to be calculated in the proposed algorithm. For validation, different micro-channel flow test cases in the near continuum and transitional regimes were considered. Detailed comparisons with DSMC for Knudsen numbers between 0.07 and 2 reveal that the new solution algorithm based on the Fokker–Planck approximation for the collision operator can accurately predict molecular stresses and heat flux and thus also gas velocity and temperature profiles. Moreover, for the Knudsen Paradox, it is shown that good agreement with DSMC is achieved up to a Knudsen number of about 5.

Type
Papers
Copyright
Copyright © Cambridge University Press 2011

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References

REFERENCES

Agarwal, R. K., Yun, K. & Balakrishnan, R. 2001 Beyond Navier–Stokes: Burnett equations for flows in the continuum-transition regime. Phys. Fluids 13, 30613085.CrossRefGoogle Scholar
Beskok, A. & Karniadakis, G. E. 1999 A model for flows in channels and ducts at micro and nano scales. Microscale Thermophys. Engng 3, 4377.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, 511525.Google Scholar
Bird, G. 1994 Molecular Gas Dynamics and the Direct Simulation of Gas Flows. Calerndon.CrossRefGoogle Scholar
Cercignani, C. 1964 Higher order slip according to the linearized Boltzmann equation. Res. Rep. AS-64-19. University of California, Berkeley, Institute of Engineering.Google Scholar
Cercignani, C. 1988 The Boltzmann Equation and Its Applications. Springer-Verlag.Google Scholar
Chandrasekhar, S. 1943 Stochastic problems in physics and astronomy. Rev. Mod. Phys. 15, 189.CrossRefGoogle Scholar
Dagum, L. 1991 Three-Dimensional Direct Particle Simulation on the Connection Machine. AIAA Paper No. 91-1365, Reston, VA.Google Scholar
Dong, W. 1956 From stochastic processes to the hydrodynamic equations. Res. Rep. UCRL-3353. University of California.Google Scholar
Gardiner, C. W. 1985 Handbook of Stochastic Methods. Springer-Verlag.Google Scholar
Hadjiconstantinou, N. G. 2003 Comment on Cercignani's second-order slip coefficient. Phys. Fluids 15, 23522354.CrossRefGoogle Scholar
Heinz, S. 2004 Molecular to fluid dynamics: the consequences of stochastic molecular motion. Phys. Rev. E 70, 036308.Google ScholarPubMed
Holway, L. H. 1966 New statistical models for kinetic theory: methods of construction. Phys. Fluids 9, 16581673.CrossRefGoogle Scholar
Jenny, P., Pope, S. B., Muradoglu, M. & Caughey, D. 2001 A hybrid algorithm for the joint pdf equation of turbulent reactive flows. J. Comput. Phys. 166, 218252.CrossRefGoogle Scholar
Jenny, P., Torrilhon, M. & Heinz, S. 2010 A solution algorithm for the fluid dynamics equations based on a stochastic model for molecular motion. J. Comput. Phys. 229, 10771098.CrossRefGoogle Scholar
Lebowitz, J. L., Frisch, H. L. & Helfand, E. 1960 Nonequilibrium distribution functions in a fluid. Phys. Fluids 3, 325338.CrossRefGoogle Scholar
Ohwada, T., Sone, Y. & Aoki, K. 1989 Numerical analysis of the Poiseuille flow and thermal transpiration flows between two parallel plates on the basis of the linearized Boltzmann equation for hard-sphere molecules. Phys. Fluids 1, 20422049.Google Scholar
Pawula, R. F. 1967 Approximation of the linear Boltzmann equation by the Fokker–Planck equation. Phys. Rev. 162, 186188.CrossRefGoogle Scholar
Risken, H. 1989 The Fokker–Planck Equation, Methods of Solution and Applications. Springer-Verlag.Google Scholar
Shakhov, E. M. 1968 Generalization of the Krook kinetic relaxation equation. Fluid Dynamics 3, 142145.Google Scholar
Sharipov, F. 2002 Application of the Cercignani–Lampis scattering kernel to calculations of rarefied gas flows. I. Plane flow between two parallel plates. Eur. J. Mech. (B/Fluids) 21, 113123.CrossRefGoogle Scholar
Sharipov, F. & Seleznev, V. 1998 Data on internal Rarefied gas flows. J. Phys. Chem. 27, 657706.Google Scholar
Shiino, M. 1987 Dynamical behavior of stochastic systems of infinitely many coupled nonlinear oscillators exhibiting phase transitions of mean-field type: H theorem on asymptotic approach to equilibrium and critical slowing down of order-parameter fluctuations. Phys. Rev. E 36, 23932412.CrossRefGoogle ScholarPubMed
Springer, G. S. 1971 Heat transfer in rarefied gases. Adv. Heat Transfer 7, 163218.Google Scholar
Struchtrup, H. 2005 Macroscopic Transport Equations for Rarefied Gas Flows. Springer.CrossRefGoogle Scholar
Taheri, P., Torrilhon, M. & Struchtrup, H. 2009 Couette and Poiseuille microflows: Analytical solutions for regularized 13-moment equations. Phys. Fluids 21, 017102.CrossRefGoogle Scholar
Torrilhon, M. & Struchtrup, H. 2004 Regularized 13-moment equations: shock structure calculations and comparison to Burnett models. J. Fluid Mech. 513, 171198.CrossRefGoogle Scholar
Torrilhon, M. & Struchtrup, H. 2008 Boundary conditions for regularized 13-moment-equations for microchannel-flows. J. Comput. Phys. 227, 19822011.CrossRefGoogle Scholar
Torrilhon, M. & Struchtrup, H. 2009 Modeling micro mass and heat transfer for gases using extended continuum equations. J. Heat Transfer 131, 033103.CrossRefGoogle Scholar
Truesdell, C. & Muncaster, R. G. 1980 Fundamentals of Maxwell's Kinetic Theory of a Simple Monatomic Gas. Academic.Google Scholar
Xu, K. 2003 Super-Burnett solutions of Poiseuille flow. Phys. Fluids 15, 2077.CrossRefGoogle Scholar
Xu, K. 2004 Microchannel flow in the slip regime: gas-kinetic BGK–Burnett solutions. J. Fluid Mech. 513, 87110.CrossRefGoogle Scholar
Yano, R., Suzuki, K. & Kuroda, H. 2009 Analytical and numerical study on the nonequilibrium relaxation by the simplified Fokker–Planck equation. Phys. Fluids 21, 047104.CrossRefGoogle Scholar
Zheng, Y., Garcia, A. L. & Alder, B. J. 2002 A comparison of kinetic theory and hydrodynamics for Poiseuille flow. J. Stat. Phys. 109, 495505.CrossRefGoogle Scholar
Zheng, Y., Reese, J. M. & Struchtrup, H. 2006 Comparing macroscopic continuum models for rarefied gas dynamics: A new test method. J. Comput. Phys. 218, 748769.CrossRefGoogle Scholar