Irrotational random flow¶

Last updated: March 3, 2022

Setup

In [1]:

import numpy as np
import skimage.filters
import scipy.ndimage
import scipy.sparse

# Plotting
import matplotlib as mpl
import matplotlib.pyplot as plt
import proplot as pplt

# Lima
import lima

import numpy as np import skimage.filters import scipy.ndimage import scipy.sparse # Plotting import matplotlib as mpl import matplotlib.pyplot as plt import proplot as pplt # Lima import lima

In [2]:

lima.plot.init_mpl_style()

lima.plot.init_mpl_style()

In [3]:

debug = True

debug = True

In [4]:

subplotgrid = [  
    [1, 1, 2, 2, 3, 3],
    [0, 4, 4, 5, 5, 0],
]

subplotgrid = [ [1, 1, 2, 2, 3, 3], [0, 4, 4, 5, 5, 0], ]

Functions

In [5]:

def vortZ(u, v):
    dudy, dudx = np.gradient(u)
    dvdy, dvdx = np.gradient(v)
    return dvdx - dudy

def divUV(u, v):
    dudy, dudx = np.gradient(u)
    dvdy, dvdx = np.gradient(v)
    return dudx + dvdy

def random_dataset(vscale=1.0, height=256, width=256, filter_size=5, seed=None):
    if seed:
        np.random.seed(seed)
    # Coordinates
    x, y = np.meshgrid(np.arange(height, dtype='float32'),
                       np.arange(width, dtype='float32'))

    #u, v = np.random.rand(2, height, width).astype('float32') - 0.5
    u, v = np.random.uniform(-1, 1, size=(2, height, width)).astype('float32')
    u = skimage.filters.gaussian(u, filter_size, mode='reflect')
    v = skimage.filters.gaussian(v, filter_size, mode='reflect')
    u = u/np.abs(u).max() * vscale
    v = v/np.abs(v).max() * vscale
    # Parameters
    #print(np.max(u), np.max(v))
    return x, y, u, v

def quiver(ax, x, y, u, v, skip=1, *args, **kwargs):
    return ax.quiver(
        x[::skip, ::skip], y[::skip, ::skip], u[::skip, ::skip], v[::skip, ::skip],
        *args, **kwargs
    )

norm = lambda u,v: np.sqrt(u**2 + v**2)

def vortZ(u, v): dudy, dudx = np.gradient(u) dvdy, dvdx = np.gradient(v) return dvdx - dudy def divUV(u, v): dudy, dudx = np.gradient(u) dvdy, dvdx = np.gradient(v) return dudx + dvdy def random_dataset(vscale=1.0, height=256, width=256, filter_size=5, seed=None): if seed: np.random.seed(seed) # Coordinates x, y = np.meshgrid(np.arange(height, dtype='float32'), np.arange(width, dtype='float32')) #u, v = np.random.rand(2, height, width).astype('float32') - 0.5 u, v = np.random.uniform(-1, 1, size=(2, height, width)).astype('float32') u = skimage.filters.gaussian(u, filter_size, mode='reflect') v = skimage.filters.gaussian(v, filter_size, mode='reflect') u = u/np.abs(u).max() * vscale v = v/np.abs(v).max() * vscale # Parameters #print(np.max(u), np.max(v)) return x, y, u, v def quiver(ax, x, y, u, v, skip=1, *args, **kwargs): return ax.quiver( x[::skip, ::skip], y[::skip, ::skip], u[::skip, ::skip], v[::skip, ::skip], *args, **kwargs ) norm = lambda u,v: np.sqrt(u**2 + v**2)

In [6]:

def construct_LHS(nx, ny, h=1):
    n = nx * ny 
    B = np.zeros((5, n))

    for i in range(ny):
        for j in range(nx):
            k = j*nx + i
            if i == 0:
                if j == 0:
                    B[2, k]    =  2*h/(h + h) + 2*h/(h + h)
                    B[3, k+1]  = -2*h/(h + h)
                    B[4, k+nx] = -2*h/(h + h)
                elif j == nx-1:
                    B[0, k-nx] = -2*h/(h + h)
                    B[2, k]    =  2*h/(h + h) + 2*h/(h + h)
                    B[3, k+1]  = -2*h/(h + h)
                else:
                    B[0, k-nx] = -2*h/(h + h)
                    B[2, k]    =  2*h/(h + h) + 2*h/(h + h) + 2*h/(h + h)
                    B[3, k+1]  = -2*h/(h + h)
                    B[4, k+nx] = -2*h/(h + h)
            elif i == nx-1:
                if j == 0:
                    B[1, k-1]  = -2*h/(h + h)
                    B[2, k]    =  2*h/(h + h) + 2*h/(h + h)
                    B[4, k+nx] = -2*h/(h + h)
                elif j == nx-1:
                    B[0, k-nx] = -2*h/(h + h)
                    B[1, k-1]  = -2*h/(h + h)
                    B[2, k]    =  2*h/(h + h) + 2*h/(h + h)
                else:
                    B[0, k-nx] = -2*h/(h + h)
                    B[1, k-1]  = -2*h/(h + h)
                    B[2, k]    =  2*h/(h + h) + 2*h/(h + h) + 2*h/(h + h)
                    B[4, k+nx] = -2*h/(h + h)
            elif j == 0:
                if ( i > 0 and i < nx-1 ):
                    B[1, k-1]  = -2*h/(h + h)
                    B[2, k]    =  2*h/(h + h) + 2*h/(h + h) + 2*h/(h + h)
                    B[3, k+1]  = -2*h/(h + h)
                    B[4, k+nx] = -2*h/(h + h)
            elif j == nx-1:
                if ( i > 0 and i < nx-1 ):
                    B[0, k-nx] = -2*h/(h + h)
                    B[1, k-1]  = -2*h/(h + h)
                    B[2, k]    =  2*h/(h + h) + 2*h/(h + h) + 2*h/(h + h)
                    B[3, k+1]  = -2*h/(h + h)
            else:
                B[0, k-nx]     = -2*h/(h + h)
                B[1, k-1]      = -2*h/(h + h)
                B[2, k]        =  2*h/(h + h) + 2*h/(h + h) + 2*h/(h + h) + 2*h/(h + h)
                B[3, k+1]      = -2*h/(h + h)
                B[4, k+nx]     = -2*h/(h + h)
    
    # Diagonal indices
    diags = np.array([-nx, -1, 0, 1, nx])

    # Construct sparse diagonal matrix
    L = scipy.sparse.spdiags(B, diags, n, n).tocsr()
    return L

def construct_RHS(u, v):
    f = -divUV(u, v)
    return f.ravel()

def solve_poisson_equation(u, v, L=None, debug=False, maxiter=100):
    if L is None:
        L = construct_LHS(u.shape[0], u.shape[1])
    f = construct_RHS(u, v)
    # Solve using gmres
    phi, info = scipy.sparse.linalg.gmres(L, f, maxiter=maxiter)
    if debug:
        print(f"Residual: {np.sum(np.abs(L.dot(phi) - f))}")
    phi = phi.reshape(u.shape[0], u.shape[1])
    return phi
    
def calculate_irrotational_flow(u, v, L=None, debug=False, maxiter=100):
    # Solve poisson equation: $\nabla^2 \phi = -\nabla \cdot u$
    phi = solve_poisson_equation(u, v, L, debug, maxiter)
    
    # Calculate irrotation flow
    v_phi, u_phi = np.gradient(phi)
    
    return u_phi, v_phi

def construct_LHS(nx, ny, h=1): n = nx * ny B = np.zeros((5, n)) for i in range(ny): for j in range(nx): k = j*nx + i if i == 0: if j == 0: B[2, k] = 2*h/(h + h) + 2*h/(h + h) B[3, k+1] = -2*h/(h + h) B[4, k+nx] = -2*h/(h + h) elif j == nx-1: B[0, k-nx] = -2*h/(h + h) B[2, k] = 2*h/(h + h) + 2*h/(h + h) B[3, k+1] = -2*h/(h + h) else: B[0, k-nx] = -2*h/(h + h) B[2, k] = 2*h/(h + h) + 2*h/(h + h) + 2*h/(h + h) B[3, k+1] = -2*h/(h + h) B[4, k+nx] = -2*h/(h + h) elif i == nx-1: if j == 0: B[1, k-1] = -2*h/(h + h) B[2, k] = 2*h/(h + h) + 2*h/(h + h) B[4, k+nx] = -2*h/(h + h) elif j == nx-1: B[0, k-nx] = -2*h/(h + h) B[1, k-1] = -2*h/(h + h) B[2, k] = 2*h/(h + h) + 2*h/(h + h) else: B[0, k-nx] = -2*h/(h + h) B[1, k-1] = -2*h/(h + h) B[2, k] = 2*h/(h + h) + 2*h/(h + h) + 2*h/(h + h) B[4, k+nx] = -2*h/(h + h) elif j == 0: if ( i > 0 and i < nx-1 ): B[1, k-1] = -2*h/(h + h) B[2, k] = 2*h/(h + h) + 2*h/(h + h) + 2*h/(h + h) B[3, k+1] = -2*h/(h + h) B[4, k+nx] = -2*h/(h + h) elif j == nx-1: if ( i > 0 and i < nx-1 ): B[0, k-nx] = -2*h/(h + h) B[1, k-1] = -2*h/(h + h) B[2, k] = 2*h/(h + h) + 2*h/(h + h) + 2*h/(h + h) B[3, k+1] = -2*h/(h + h) else: B[0, k-nx] = -2*h/(h + h) B[1, k-1] = -2*h/(h + h) B[2, k] = 2*h/(h + h) + 2*h/(h + h) + 2*h/(h + h) + 2*h/(h + h) B[3, k+1] = -2*h/(h + h) B[4, k+nx] = -2*h/(h + h) # Diagonal indices diags = np.array([-nx, -1, 0, 1, nx]) # Construct sparse diagonal matrix L = scipy.sparse.spdiags(B, diags, n, n).tocsr() return L def construct_RHS(u, v): f = -divUV(u, v) return f.ravel() def solve_poisson_equation(u, v, L=None, debug=False, maxiter=100): if L is None: L = construct_LHS(u.shape[0], u.shape[1]) f = construct_RHS(u, v) # Solve using gmres phi, info = scipy.sparse.linalg.gmres(L, f, maxiter=maxiter) if debug: print(f"Residual: {np.sum(np.abs(L.dot(phi) - f))}") phi = phi.reshape(u.shape[0], u.shape[1]) return phi def calculate_irrotational_flow(u, v, L=None, debug=False, maxiter=100): # Solve poisson equation: $\nabla^2 \phi = -\nabla \cdot u$ phi = solve_poisson_equation(u, v, L, debug, maxiter) # Calculate irrotation flow v_phi, u_phi = np.gradient(phi) return u_phi, v_phi

Pure-random flow¶

In [7]:

filter_size = 5
vscale = 1
height = width = 64

filter_size = 5 vscale = 1 height = width = 64

In [8]:

# Random dist.
np.random.seed(234)

x, y, _,_ = random_dataset(vscale=1, height=height, width=width, filter_size=filter_size)
u, v = np.random.uniform(-1, 1, size=(2, height, width)).astype('float32')

# Filtered
uf = skimage.filters.gaussian(u, filter_size, mode="reflect")#, preserve_range=True) 
vf = skimage.filters.gaussian(v, filter_size, mode="reflect")#, preserve_range=True)
uf = uf/np.abs(uf).max() * vscale
vf = vf/np.abs(vf).max() * vscale
# Scaled
us = uf
vs = vf

# Random dist. np.random.seed(234) x, y, _,_ = random_dataset(vscale=1, height=height, width=width, filter_size=filter_size) u, v = np.random.uniform(-1, 1, size=(2, height, width)).astype('float32') # Filtered uf = skimage.filters.gaussian(u, filter_size, mode="reflect")#, preserve_range=True) vf = skimage.filters.gaussian(v, filter_size, mode="reflect")#, preserve_range=True) uf = uf/np.abs(uf).max() * vscale vf = vf/np.abs(vf).max() * vscale # Scaled us = uf vs = vf

Rotational and irrotational component¶

In [9]:

fig = pplt.figure(refwidth='1.8', span=False)
axes = fig.subplots(subplotgrid)

s = 5
# u
ax = axes[0]
im = ax.imshow(us,  interpolation='none', cmap='turbo')
cb = ax.colorbar(im, width='0.75em')
ax.format(title=r"U-Velocity: $u$")

# v
ax = axes[1]
im = ax.imshow(vs,  interpolation='none', cmap='turbo')
cb = ax.colorbar(im, width='0.75em')
ax.format(title=r"V-Velocity: $v$")

# |u| 
ax = axes[2]
im = ax.imshow((us**2 + vs**2)**0.5,  interpolation='none', cmap='turbo')
cb = ax.colorbar(im, width='0.75em')
quiver(ax, x, y, us, vs, skip=2, color='k', scale=s)
ax.format(title=r"Velocity mag.: $|u|$")

# vorticity |u| 
ax = axes[3]
im = ax.imshow(np.abs(vortZ(us, vs)), interpolation='none', cmap='turbo')
cb = ax.colorbar(im, width='0.75em')
ax.format(title=fr"Vorticity mag.: $|\nabla \times u|$ (sum={np.abs(vortZ(us, vs)).sum():2g})")
ax.streamplot(x, y, us, vs, c='k', lw=0.5)
ax.axis([0, height, 0, width])

# divergence |u| 
ax = axes[4]
im = ax.imshow(divUV(us, vs), interpolation='none', cmap='turbo')
cb = ax.colorbar(im, width='0.75em')
ax.format(title=r"Divergence: $\nabla \cdot u$")

# Quiver

# # Format
axes.format(
    abc='(a)', abcloc='ul', abcbbox=True,
    xlabel='$x$ (px)', ylabel='$y$ (px)',
    suptitle='Pure random vector field',
)

fig = pplt.figure(refwidth='1.8', span=False) axes = fig.subplots(subplotgrid) s = 5 # u ax = axes[0] im = ax.imshow(us, interpolation='none', cmap='turbo') cb = ax.colorbar(im, width='0.75em') ax.format(title=r"U-Velocity: $u$") # v ax = axes[1] im = ax.imshow(vs, interpolation='none', cmap='turbo') cb = ax.colorbar(im, width='0.75em') ax.format(title=r"V-Velocity: $v$") # |u| ax = axes[2] im = ax.imshow((us**2 + vs**2)**0.5, interpolation='none', cmap='turbo') cb = ax.colorbar(im, width='0.75em') quiver(ax, x, y, us, vs, skip=2, color='k', scale=s) ax.format(title=r"Velocity mag.: $|u|$") # vorticity |u| ax = axes[3] im = ax.imshow(np.abs(vortZ(us, vs)), interpolation='none', cmap='turbo') cb = ax.colorbar(im, width='0.75em') ax.format(title=fr"Vorticity mag.: $|\nabla \times u|$ (sum={np.abs(vortZ(us, vs)).sum():2g})") ax.streamplot(x, y, us, vs, c='k', lw=0.5) ax.axis([0, height, 0, width]) # divergence |u| ax = axes[4] im = ax.imshow(divUV(us, vs), interpolation='none', cmap='turbo') cb = ax.colorbar(im, width='0.75em') ax.format(title=r"Divergence: $\nabla \cdot u$") # Quiver # # Format axes.format( abc='(a)', abcloc='ul', abcbbox=True, xlabel='$x$ (px)', ylabel='$y$ (px)', suptitle='Pure random vector field', )