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Galilean-invariant preconditioned central-moment lattice Boltzmann method without cubic velocity errors for efficient
Farzaneh Hajabdollahi1, Kannan N Premnath1
1Department of Mechanical Engineering, University of Colorado Denver, 1200 Larimer Street, Denver, Colorado 80124, USA.
This study introduces a Galilean-invariant (GI) preconditioned cascaded central-moment Lattice Boltzmann (LB) method to solve fluid flow problems. The new method eliminates cubic velocity errors in viscous stress for steady flows, improving accuracy and convergence.
Area of Science:
- Computational Fluid Dynamics
- Numerical Analysis
- Fluid Mechanics
Background:
- Standard Lattice Boltzmann (LB) models for Navier-Stokes (NS) equations can produce non-Galilean-invariant (GI) viscous stress with cubic velocity errors.
- Existing methods to restore GI often involve modified collision operators, affecting relaxation times or moment equilibria.
Purpose of the Study:
- To present a GI formulation of the preconditioned cascaded central-moment LB method for steady flows.
- To eliminate cubic velocity errors in the preconditioned NS equations solved by the LB method on standard lattices.
- To validate the method's effectiveness in convergence acceleration and accuracy improvement.
Main Methods:
- Developed a GI formulation for the preconditioned cascaded central-moment LB method.
- Employed Chapman-Enskog analysis to identify and analyze spurious non-GI defect terms.
- Introduced corrections to cubic velocity terms and extended moment equilibria, dependent on the preconditioning parameter.
Main Results:
- The proposed method is free of cubic velocity errors on a standard lattice for steady flows.
- Anisotropy of viscous stress depends on the preconditioning parameter and fluid velocity.
- Corrections based on scaling and extended moment equilibria fully restore Galilean invariance without cubic defects.
Conclusions:
- The preconditioning parameter significantly influences non-GI errors and their corrections.
- The GI preconditioned central-moment LB method effectively accelerates convergence and enhances accuracy.
- Validated the method on complex flow benchmark problems, demonstrating its robustness and efficacy.
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