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Lattice Boltzmann Simulation of Fluid Flows. M.J. Pattison & S. Banerjee MetaHeuristics LLC Santa Barbara, CA 93105. Main Topics. Objectives Lattice Boltzmann method Complex geometry Multicomponent flow Turbulence modelling Parallelisation. NSTX Lithium Free Surface Module (ORNL).
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Lattice Boltzmann Simulation of Fluid Flows M.J. Pattison & S. Banerjee MetaHeuristics LLC Santa Barbara, CA 93105
Main Topics • Objectives • Lattice Boltzmann method • Complex geometry • Multicomponent flow • Turbulence modelling • Parallelisation
NSTX Lithium Free Surface Module (ORNL) Objectives – Phase 1 • Complex geometry • Multiphase flow • Heat transport • Turbulence • Fluid-wall interactions • Parallelisation capability
Objectives – Phase II • MHD • Chemical reactions • Parallel code • Input/output processing
Lattice Boltzmann Method Solve for velocity distribution a is force term is a relaxation time (function of viscosity)
Projection Method Predictor 1. 2. Poisson eqn 3. Corrector Poisson equation is elliptic. Can solve using spectral method (FFT) for simple geometry or by iterative method. Methods use non-local data so making parallel processing less efficient.
Capabilities of LB code • Can handle complex geometry easily • Multicomponent/multiphase flows • Turbulence models – LES or algebraic • Well suited to parallel processing – almost linear scaling with number of CPUs
Complex Geometry No need for body-fitted grid but need distributions at point b Fluid a Wall b is function of distance from wall is an equilibrium distribution
Backward Facing Step Velocity profiles downstream of step. Left at x/S = 6, right at at x/S = 20
Multicomponent Flows Model interactions between components using a force term Where summation is over nearest neighbours and the different components. is a function of density Can model effects of: - surface tension - phase change (i.e. condensation) - immiscible fluids
Movement of Droplet down Wall Drop is initially semi-circular, with surrounding fluid stationary Drop spreads due to surface tension, then moves down wall
Turbulence Modelling • Use Baldwin-Lomax algebraic model • Smagorinski type LES model • Models use an “eddy viscosity” to account for effects of turbulence • Both models only require local data, so are suited for parallel processing
Parallelisation Split domain up into slabs or blocks Assign each one to a different processor Speed of computation for different numbers of CPUs used – plane Poiseuille flow problem
Conclusions • 3-D transient Lattice Boltzmann code with following capabilities developed: • Multicomponent flow • Complex geometry • Turbulence modelling • Efficient parallel processing with almost linear scaling