LBM Flashcards

1
Q

f(c, x, t)

A

represents the probability at time t, of a particle being positioned at position x with velocity c

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2
Q

DnQm teminology

A
  • n refers to the number of physical dimensions
  • m refers to the number of discretised velocities, more relevant as one considers particle energies
  • D2Q9 has 3 speeds {0, 1, sqrt(2)}
  • D3Q19 has 4 speeds {0, 1, sqrt{2), sqrt(3)}
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3
Q

BGK model

A
  • in the collision operator, current particle distribution is relaxed over time (tau) back towards the equilibrium distribution (f^eq) for a given set of conditions
  • equilibrium distribution is computed directly from the discretised Maxwell-boltzmann function
  • BGK model limited to isothermal flows at low Reynolds number
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4
Q

LBM advantages

A
  • efficient for parallel computation due to local nature of computation and algorithmic simplicity
  • faster computation per iteration due to linear nature of LBM equation and absence of pressure term which is expensive in FVM
  • solid boundary conditions are very easy to implement
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5
Q

Unknown components

A
  • after the streaming step the unknown components are those which point back into the domain since they have streamed from non-existent lattice sites
  • therefore f1, f5 and f7 are the unknown lattice components
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6
Q

Bounceback boundary condition disadvantages

A
  • only 1st order accurate

- cannot represent curved surfaces

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7
Q

The Mesoscale

A
  • based on kinetic theory
  • instead of a single particle we consider a distribution function
  • distribution function represents a collection of particles
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8
Q

LBM disadvantages

A
  • regular grids are a limitation
  • subject to instabilities with low viscosity
  • limited to low Reynolds and Mach numbers
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9
Q

Pressure Eq from probability function

A

p=(Cs)^2.rho

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10
Q

LBM algorithm steps

A
  • Initialise: all population distribution functions set to equilibrium
  • Equilibrium: equilibrium recomputed at all points based on updated velocity field
  • Stream: particles are convected along all discrete velocity paths in a single step
  • Collide: distribution functions recomputed at all sites as a function of the difference between current and equilibrium state
  • Macroscopic: macroscopic quantities are recomputed based on summations of the particle distributions
  • Boundary conditions: particle states at boundaries are updated
  • End: calculation completed and results saved
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11
Q

Recovering quantities from distribution function

A

Density - recovered by summing all components of the population density function

Velocity in x-direction - recovered by summing those components with a component in the x-direction

Pressure - a thermodynamic quantity non recovered directly, can be approximated from density

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