Abstract

In this article, we analyze the stability, convergence, and accuracy of the constrained runs initialization scheme for a mesoscale lattice Boltzmann model (LBM). This type of initialization scheme was proposed by Gear and Kevrekidis in [J. Sci. Comput., 25 (2005), pp. 17–28] in the context of both singularly perturbed ordinary differential equations and equation-free computing. It maps the given macroscopic initial variables to the higher-dimensional space of microscopic/mesoscopic variables. The scheme performs short runs with the microscopic/mesoscopic simulator and resets the macroscopic variables (typically the lower order moments of the microscopic/mesoscopic variables), while leaving the higher order moments unchanged. We use the LBM Bhatnagar–Gross–Krook (BGK) model for one-dimensional reaction-diffusion systems as the microscopic/mesoscopic model. For such systems, we prove that the constrained runs scheme is unconditionally stable and that it converges to an approximation of the slaved state, i.e., the mesoscopic state which is consistent with the macroscopic initial condition. This approximation is correct up to and including the first order terms in the Chapman–Enskog expansion of the LBM. The asymptotic convergence factor is $|1-\omega|$ with $\omega$ the BGK relaxation parameter. The results are illustrated numerically for the FitzHugh–Nagumo system. Furthermore, we use the constrained runs scheme to perform a coarse equation-free bifurcation analysis of this model. Finally, we show that the constrained runs scheme is very similar to the LBM initialization scheme proposed by Mei et al. in [Comput. Fluids, 35 (2006), pp. 855–862] when implemented for our model problem, and that our numerical analysis applies to the latter scheme also.

MSC codes

  1. 76M28
  2. 35K45
  3. 35K57

Keywords

  1. initialization
  2. lifting
  3. equation-free computing
  4. lattice Boltzmann model

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References

1.
A. Caiazzo, Analysis of lattice Boltzmann initialization routines, J. Statist. Phys., 121 (2005), pp. 37–48.
2.
B. Chopard, A. Dupuis, A. Masselot, and P. Luthi, Cellular automata and lattice Boltzmann techniques: An approach to model and simulate complex systems, Adv. Complex Syst., 5 (2002), pp. 103–246.
3.
W. E, Analysis of the heterogeneous multiscale method for ordinary differential equations, Commun. Math. Sci., 1 (2003), pp. 423–436.
4.
C. W. Gear, T. J. Kaper, I. G. Kevrekidis, and A. Zagaris, Projecting to a slow manifold: Singularly perturbed systems and legacy codes, SIAM J. Appl. Dyn. Syst., 4 (2005), pp. 711–732.
5.
C. W. Gear and I. G. Kevrekidis, Projective methods for stiff differential equations: Problems with gaps in their eigenvalue spectrum, SIAM J. Sci. Comput., 24 (2003), pp. 1091–1106.
6.
C. W. Gear and I. G. Kevrekidis, Constraint-defined manifolds: A legacy code approach to low-dimensional computation, J. Sci. Comput., 25 (2005), pp. 17–28.
7.
I. Ginzbourg and P. M. Adler, Boundary flow condition analysis for the three-dimensional lattice Boltzmann model, J. Phys. II France, 4 (1994), pp. 191–214.
8.
M. Junk, A finite difference interpretation of the lattice Boltzmann method, Numer. Methods Partial Differential Equations, 17 (2001), pp. 383–402.
9.
M. Junk and A. Klar, Discretizations for the incompressible Navier–Stokes equations based on the lattice Boltzmann method, SIAM J. Sci. Comput., 22 (2000), pp. 1–19.
10.
I. G. Kevrekidis, C. W. Gear, J. M. Hyman, P. G. Kevrekidis, O. Runborg, and C. Theodoropoulos, Equation-free, coarse-grained multiscale computation: Enabling microscopic simulators to perform system-level analysis, Commun. Math. Sci., 1 (2003), pp. 715–762.
11.
K. Lust, D. Roose, A. Spence, and A. Champneys, An adaptive Newton–Picard algorithm with subspace iteration for computing periodic solutions, SIAM J. Sci. Comput., 19 (1998), pp. 1188–1209.
12.
A. G. Makeev, D. Maroudas, and I. G. Kevrekidis, “Coarse” stability and bifurcation analysis using stochastic simulators: Kinetic Monte Carlo examples, J. Chem. Phys., 116 (2002), pp. 10083–10091.
13.
R. Mei, L.-S. Luo, P. Lallemand, and D. d'Humières, Consistent initial conditions for lattice Boltzmann simulations, Comput. & Fluids, 35 (2006), pp. 855–862.
14.
Y. H. Qian and S. A. Orszag, Scalings in diffusion-driven reaction $A + B \rightarrow C$: Numerical simulations by lattice BGK models, J. Statist. Phys., 81 (1995), pp. 237–253.
15.
G. Samaey, D. Roose, and I. G. Kevrekidis, The gap-tooth scheme for homogenization problems, Multiscale Model. Simul., 4 (2005), pp. 278–306.
16.
P. A. Skordos, Initial and boundary conditions for the lattice Boltzmann method, Phys. Rev. E (3), 48 (1993), pp. 4823–4842.
17.
C. Theodoropoulos, Y. H. Qian, and I. G. Kevrekidis, “Coarse” stability and bifurcation analysis using time-steppers: A reaction-diffusion example, Proc. Natl. Acad. Sci. USA, 97 (2000), pp. 9840–9843.
18.
P. Van Leemput, Multiscale and Equation-Free Computing for Lattice Boltzmann Models, Ph.D. thesis, Katholieke Universiteit Leuven, Belgium, 2007.
19.
P. Van Leemput and K. Lust, Numerical bifurcation analysis of lattice Boltzmann models: A reaction-diffusion example, in Computational Science—ICCS 2004, Lecture Notes in Comput. Sci. 3039, M. Bubak, G. D. van Albada, P. M. A. Sloot, and J. Dongarra, eds., Springer-Verlag, Berlin, 2004, pp. 572–579.
20.
P. Van Leemput, K. Lust, and I. G. Kevrekidis, Coarse-grained numerical bifurcation analysis of lattice Boltzmann models, Phys. D, 210 (2005), pp. 58–76.
21.
P. Van Leemput, M. Rheinländer, and M. Junk, Smooth initialization of lattice Boltzmann schemes, Comput. Math. Appl., submitted.
22.
P. Van Leemput, C. Vandekerckhove, W. Vanroose, and D. Roose, Accuracy of hybrid lattice Boltzmann/finite difference schemes for reaction-diffusion systems, Multiscale Model. Simul., 6 (2007), pp. 838–857.
23.
P. Van Leemput, W. Vanroose, and D. Roose, Initialization of a Lattice Boltzmann Model with Constrained Runs, Technical report TW 444, Department of Computer Science, Katholieke Universiteit Leuven, Belgium, 2005.
24.
C. Vandekerckhove, I. G. Kevrekidis, and D. Roose, An efficient Newton-Krylov implementation of the constrained runs scheme for initializing on a slow manifold, J. Sci. Comput., submitted.
25.
C. Vandekerckhove, D. Roose, and K. Lust, Numerical stability analysis of an acceleration scheme for step size constrained time integrators, J. Comput. Appl. Math., 200 (2007), pp. 761–777.
26.
C. Vandekerckhove, P. Van Leemput, and D. Roose, Accuracy and stability of the coarse time-stepper for a lattice Boltzmann model, J. Algorithms & Computational Technology, to appear.
27.
A. Zagaris, C. W. Gear, T. J. Kaper, and I. G. Kevrekidis, Analysis of the accuracy and convergence of equation-free projection to a slow manifold, SIAM J. Numer. Anal., submitted.
28.
A. Zagaris, H. G. Kaper, and T. J. Kaper, Two perspectives on reduction of ordinary differential equations, Math. Nachr., 278 (2005), pp. 1629–1642.

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Published In

cover image Multiscale Modeling & Simulation
Multiscale Modeling & Simulation
Pages: 1234 - 1255
ISSN (online): 1540-3467

History

Submitted: 8 June 2007
Accepted: 5 October 2007
Published online: 1 February 2008

MSC codes

  1. 76M28
  2. 35K45
  3. 35K57

Keywords

  1. initialization
  2. lifting
  3. equation-free computing
  4. lattice Boltzmann model

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