if ( liveUpdate | savePlot ):
pyplot.ion()
fig, ax = pyplot.subplots(1)
###### Main time loop ##########################################################
for time in range(maxIterations):
# bounce back distributions at walls
for i in range(q):
fin[i, noSlipBoundary] = fin[noslip[i], noSlipBoundary]
# Calculate macroscopic density and velocity
(rho, u) = getMacroValues(fin)
#right wall: pressure equal to one
fin[:,nxl,:] = cumulantBoundary(1 - pressureDrop, u[:,nxl,:])
#left wall:
fin[:,0,:] = cumulantBoundary(1 + pressureDrop, u[:,0,:])
# Collision step.
fpost = collisionFunction(fin, rho, u, omega )
# Streaming step
fin = stream(fpost)
# Visualization
if ( (time % plotEveryN == 0) & (liveUpdate | savePlot) & (time > skipFirstN) ):
if ( liveUpdate | savePlot ):
ax.clear()
###### Main time loop ##########################################################
for time in range(maxIterations):
# bounce back distributions at obstacle
for i in range(q):
fin[i, obstacle] = fin[noslip[i], obstacle]
# and walls
for i in range(q):
fin[i, noSlipBoundary] = fin[noslip[i], noSlipBoundary]
# Calculate macroscopic density and velocity
(rho, u) = getMacroValues(fin)
#right wall: pressure equal to one
fin[:,nxl,:] = cumulantBoundary(1, u[:,nxl-1,:])
#left wall:
fin[:,0,:] = cumulantBoundary(rho[0,:], vel[:,0,:])
# Collision step.
fpost[:,1:nx-1,1:ny-1] = collisionFunction(fin[:,1:nx-1,1:ny-1], rho[1:nx-1,1:ny-1], u[:,1:nx-1,1:ny-1], omega )
# Streaming step
fin = stream(fpost)
# Visualization
if ( (time % plotEveryN == 0) & (analysis | liveUpdate | savePlot) & (time > skipFirstN) ):
# Here, distributions are streamed into the obstacle -> compute drag and lift
scaling = preComputeFactorForScaling/sumPopulations(fin[:,completeBoundStencil])
scaledFin = fin.copy()
