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droplet_to_ring.py
451 lines (342 loc) · 15.4 KB
/
droplet_to_ring.py
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# -*- coding: utf-8 -*-
import numpy as np
from scipy.integrate import solve_bvp, odeint, solve_ivp, simps, romb, romberg, quad
from scipy.interpolate import interp1d, CubicSpline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from tqdm import trange
k = 100 #Reaction rate constant.
DG = 0.35 #Diffusion coefficient of Gallium.
DA = 0.11 #Diffusion coefficient of Arsenic.
F = 0.04 #Deposition flux of Arsenic.
tau = 1/F #Resdience time of Arsenic.
GaAs_normalisation = 500
y1a = 1.0 #Surface adatom concentration of Gallium at r = rD.
y3a = 0.0 #Surface adatom concentration of Arsenic at r = rD.
CAs_c = F * tau #Surface adatom concentration of Arsenic for large r.
kappa_0 = k * y1a * CAs_c / 500 #Normalisation constant
rDi = 1.0 #Initial droplet radius (first r-mesh point at t = 0).
rb = 10.0 #Last r-mesh point.
rdivs = 513 #Number of r-mesh points.#300 when tol=0.01
tolerance = 1e-7 #Accuracy to which CGa and CAs are found
max_mesh_nodes = 4e7 #Number of mesh nodes algorithm for bvp solver uses to find concentration profiles
tf = 0.34378 #final time
rD_cutoff = 0.01 #point at which radius stops decreasing to avoid small number errors
tdivs = 513 #Number of t-mesh points.
V_Ga = 1.0 #Ga primitive cell volume
V_GaAs = 1.0 #GaAs primitive cell volume
beta_theta = 1.0 #Contact angle constant
A = 2.57142857 #Constant coefficient of first term in drD/dt expression.
B = 1.5 #Constant coefficient of second term in drD/dt expression.
b = 1.0 * rDi #1.0 #Constant coefficient of Gaussian expression.
w = 0.15 * rDi #0.15 #Width of Gaussian.
hi = 0.0 #Initial height of inner/outer ring (at all points).
plt.rcParams['font.size'] = 30
def dydr(r, y):
return np.vstack((y[1],
-y[1]/r + k*y[0]*y[2]/DG,
y[3],
-y[3]/r + k*y[0]*y[2]/DA - F/DA + y[2]/(DA*tau)))
def y_bc(ya, yb):
return np.array([ya[0] - y1a, yb[0], ya[2] - y3a, yb[2] - F*tau])
rmeshi = np.linspace(rDi, rb, rdivs)
rmeshf = np.linspace(0, rb - rDi, rdivs)
y_at_ti_guess = np.zeros((4, rmeshi.size))
calc_y_at_ti_vs_r = solve_bvp(dydr, y_bc, rmeshi, y_at_ti_guess, tol = tolerance, max_nodes = max_mesh_nodes)
calc_y_at_ti_vs_r.status
def plot_y_at_ti_vs_r():
plt.figure(1, figsize = (11,11))
plt.plot(rmeshi, k * calc_y_at_ti_vs_r.sol(rmeshi)[0] * calc_y_at_ti_vs_r.sol(rmeshi)[2] / kappa_0, color = 'k', linewidth = 5, label = r'$k_rC_{Ga}C_{As} / \kappa_0$')
plt.plot(rmeshi, calc_y_at_ti_vs_r.sol(rmeshi)[0], 'r--', lw = '3', label = r'$C_{Ga}/C^0_{Ga}$')
plt.plot(rmeshi, calc_y_at_ti_vs_r.sol(rmeshi)[2]/(F * tau), 'g', linestyle = (0, (1, 1)), lw = '3', label = r'$C_{As}/C^c_{As}$')
plt.plot([0, rmeshi[-1]], [F / kappa_0, F / kappa_0], color = 'b', lw = '2', linestyle = (0, (1, 10)), label = r'$F_{As} / \kappa_0$')
plt.plot([rmeshi[0], rmeshi[0]], [0, y1a], color = 'k', lw = '2', linestyle = (0, (1, 10)))
plt.xlabel(r'$r/r_D(0)$')
plt.legend()
plot_y_at_ti_vs_r() #This produces a graph of the concentration profiles.
"""
Droplet radius
"""
def y2_value_at_rD(rD):
def dydr(r, y):
return np.vstack((y[1],
-y[1]/r + k*y[0]*y[2]/DG,
y[3],
-y[3]/r + k*y[0]*y[2]/DA - F/DA + y[2]/(DA*tau)))
def y_bc(ya, yb):
return np.array([ya[0] - y1a, yb[0], ya[2] - y3a, yb[2] - F*tau])
rmesh = np.linspace(rD, rb - (rDi - rD), rdivs)
y_guess = np.zeros((4, rmesh.size))
return solve_bvp(dydr, y_bc, rmesh, y_guess, tol = tolerance, max_nodes = max_mesh_nodes).sol(rD)[1]
def drDdt(t, rD):
if rD > rD_cutoff:
return (1 / rD) * (V_Ga / beta_theta) * (DG * A * y2_value_at_rD(rD[0]) - B * b * w / V_GaAs) #(Sould include DG dependence later because this is one of the parameters varied in the paper.)
else:
return 0.0
tmesh = np.linspace(0, tf, tdivs)
calc_rD_vs_t = solve_ivp(drDdt, [0.0, tf], [rDi], dense_output = True, atol = 1e-6)
rD_vs_t = calc_rD_vs_t.sol(tmesh)
y2a_vs_t = [y2_value_at_rD(i) for i in rD_vs_t[0]]
plt.figure(652, figsize = (11,11))
plt.xlabel(r'$t / t_f$')
plt.ylabel(r'$-\frac{dC_{Ga}(r_D, t)}{dr}$')
plt.yscale('log')
plt.plot(tmesh / tf, [-i for i in y2a_vs_t], color = 'r', lw = 4)
def plot_rD_vs_t():
plt.figure(4, figsize = (11,11))
plt.xlabel(r'$t / t_f$')
plt.ylabel(r'$r_D(t) / r_D(0)$')
x = np.linspace(0, tf, tdivs*500)
x = tmesh
plt.plot(x/tf, calc_rD_vs_t.sol(x)[0], color = 'r', lw = 4)
plot_rD_vs_t() #This produces a graph of droplet radius as a function of time.
def rD_value_at_t(t):
if t <= tf:
return calc_rD_vs_t.sol(t)[0]
elif t > tf:
return 0
"""
Inner ring
"""
def dhIdt(t, r):
if rD_value_at_t(t) > rD_cutoff:
return b*np.exp(-((r - rD_value_at_t(t))**2)/(2*w**2))
else:
return 0.0
hIf_vs_r = []
for r in rmeshf:
hOf_at_r = romberg(dhIdt, 0, tf, args = (r,))
hIf_vs_r.append(hOf_at_r)
hI1_vs_r = []
for r in rmeshf:
hOf_at_r = romberg(dhIdt, 0, tf*(1/4), args = (r,))
hI1_vs_r.append(hOf_at_r)
hI2_vs_r = []
for r in rmeshf:
hOf_at_r = romberg(dhIdt, 0, tf*(2/4), args = (r,))
hI2_vs_r.append(hOf_at_r)
hI3_vs_r = []
for r in rmeshf:
hOf_at_r = romberg(dhIdt, 0, tf*(3/4), args = (r,))
hI3_vs_r.append(hOf_at_r)
rmesh_tot = list(-rmeshf)[::-1] + list(rmeshf)[1:]
plt.figure(908, figsize = (11,11))
plt.plot(rmesh_tot, hI1_vs_r[::-1] + hI1_vs_r[1:], color = 'r', label = r'$t = \frac{1}{4}t_f$')
plt.plot(rmesh_tot, hI2_vs_r[::-1] + hI2_vs_r[1:], color = 'g', label = r'$t = \frac{1}{2}t_f$')
plt.plot(rmesh_tot, hI3_vs_r[::-1] + hI3_vs_r[1:], color = 'b', label = r'$t = \frac{3}{4}t_f$')
plt.plot(rmesh_tot, hIf_vs_r[::-1] + hIf_vs_r[1:], color = 'k', label = r'$t = t_f$')
plt.xlabel(r'$r/r_D(0)$')
plt.ylabel(r'Inner ring height ($r_D(0)$)')
plt.yticks([])
plt.legend()
"""
Outer ring
"""
y_vs_r_at_all_t = []
def Append_y1y3_vs_r_at_t(rD):
def dydr(r, y):
return np.vstack((y[1],
-y[1]/r + k*y[0]*y[2]/DG,
y[3],
-y[3]/r + k*y[0]*y[2]/DA - F/DA + y[2]/(DA*tau)))
def y_bc(ya, yb):
return np.array([ya[0] - y1a, yb[0], ya[2] - y3a, yb[2] - F*tau])
rmesh = np.linspace(rD, rb - (rDi - rD), rdivs)
y_guess = np.zeros((4, rmesh.size))
calc_y_vs_r_at_t = solve_bvp(dydr, y_bc, rmesh, y_guess, tol = tolerance, max_nodes = max_mesh_nodes).sol
y_vs_r_at_all_t.append(calc_y_vs_r_at_t)
return None
for i in trange(len(rD_vs_t[0])):
Append_y1y3_vs_r_at_t(rD_vs_t[0][i])
y1y3_values_mesh_points = []
for t in range(len(tmesh)):
y1y3_over_rmesh_fixed_t = []
for r in rmeshf:
y1 = y_vs_r_at_all_t[t](r)[0]
y3 = y_vs_r_at_all_t[t](r)[2]
y1y3 = k * V_GaAs * y1 * y3
if y1y3 > 1e-17 and rD_value_at_t(tmesh[t]) > rD_cutoff:
y1y3_over_rmesh_fixed_t.append(y1y3)
else:
y1y3_over_rmesh_fixed_t.append(0.0)
y1y3_values_mesh_points.append(y1y3_over_rmesh_fixed_t)
plt.figure(903)
for i in range(len(tmesh)):
plt.plot(rmeshf, y1y3_values_mesh_points[i])
y1y3_values_mesh_points_r_vs_t = []
for r in rmeshf:
y1y3_over_rmesh_fixed_r = []
for t in range(len(tmesh)):
y1 = y_vs_r_at_all_t[t](r)[0]
y3 = y_vs_r_at_all_t[t](r)[2]
y1y3 = k * V_GaAs * y1 * y3
if y1y3 > 0 and rD_value_at_t(tmesh[t]) > rD_cutoff: #1e-17:
y1y3_over_rmesh_fixed_r.append(y1y3)
else:
y1y3_over_rmesh_fixed_r.append(0.0)
y1y3_values_mesh_points_r_vs_t.append(y1y3_over_rmesh_fixed_r)
degree = 3
coeff = np.polyfit(tmesh, y1y3_values_mesh_points_r_vs_t[150], degree)
p = np.poly1d(coeff)
def polyfit(x):
value = 0
for i in range(degree + 1):
value += coeff[i] * x ** (degree - i)
return value
test_mesh = tmesh
#plt.figure(99)
#plt.plot(test_mesh, [polyfit(j) for j in test_mesh])
#plt.plot(tmesh, y1y3_values_mesh_points_r_vs_t[150])
max([polyfit(test_mesh[i]) - y1y3_values_mesh_points_r_vs_t[150][i] for i in range(tdivs)])
max(y1y3_values_mesh_points_r_vs_t[150])
np.argmax([polyfit(test_mesh[i]) - y1y3_values_mesh_points_r_vs_t[150][i] for i in range(tdivs)])
y1y3_values_mesh_points_r_vs_t[150][np.argmax([polyfit(test_mesh[i]) - y1y3_values_mesh_points_r_vs_t[150][i] for i in range(tdivs)])]
print(coeff)
plt.figure(904)
for i in range(len(rmeshf)):
plt.plot(tmesh, y1y3_values_mesh_points_r_vs_t[i])
delta_t = tf/(tdivs - 1)
hOf_vs_r = []
for i in range(len(rmeshf)):
hOf_at_r = simps(y1y3_values_mesh_points_r_vs_t[i], tmesh)
hOf_vs_r.append(hOf_at_r)
hO1_vs_r = []
for i in range(len(rmeshf)):
hOf_at_r = simps(y1y3_values_mesh_points_r_vs_t[i][:int((1/4) * tdivs)], tmesh[:int((1/4) * tdivs)])
hO1_vs_r.append(hOf_at_r)
hO2_vs_r = []
for i in range(len(rmeshf)):
hOf_at_r = simps(y1y3_values_mesh_points_r_vs_t[i][:int((2/4) * tdivs)], tmesh[:int((2/4) * tdivs)])
hO2_vs_r.append(hOf_at_r)
hO3_vs_r = []
for i in range(len(rmeshf)):
hOf_at_r = simps(y1y3_values_mesh_points_r_vs_t[i][:int((3/4) * tdivs)], tmesh[:int((3/4) * tdivs)])
hO3_vs_r.append(hOf_at_r)
plt.figure(905)
plt.plot(rmesh_tot, hO1_vs_r[::-1] + hO1_vs_r[1:], color = 'r', label = r'$t = \frac{1}{4}t_f$')
plt.plot(rmesh_tot, hO2_vs_r[::-1] + hO2_vs_r[1:], color = 'g', label = r'$t = \frac{1}{2}t_f$')
plt.plot(rmesh_tot, hO3_vs_r[::-1] + hO3_vs_r[1:], color = 'b', label = r'$t = \frac{3}{4}t_f$')
plt.plot(rmesh_tot, hOf_vs_r[::-1] + hOf_vs_r[1:], color = 'k', label = r'$t = t_f$')
plt.xlabel(r'$r/r_D(0)$')
plt.ylabel(r'$h_O(r/r_D(0), t)/r_D(0)$')
plt.yticks([])
plt.legend()
normalise = 5
h1_vs_r = np.array(hI1_vs_r) + normalise * np.array(hO1_vs_r)
h2_vs_r = np.array(hI2_vs_r) + normalise * np.array(hO2_vs_r)
h3_vs_r = np.array(hI3_vs_r) + normalise * np.array(hO3_vs_r)
hf_vs_r = np.array(hIf_vs_r) + normalise * np.array(hOf_vs_r)
h1_vs_r_T = np.insert(h1_vs_r, 0, h1_vs_r[::-1][:-1])
h2_vs_r_T = np.insert(h2_vs_r, 0, h2_vs_r[::-1][:-1])
h3_vs_r_T = np.insert(h3_vs_r, 0, h3_vs_r[::-1][:-1])
hf_vs_r_T = np.insert(hf_vs_r, 0, hf_vs_r[::-1][:-1])
rmeshf_T = np.linspace(-rmeshf[-1], rmeshf[-1], rdivs * 2 - 1)
plt.figure(906)
plt.plot(rmesh_tot, h1_vs_r_T, color = 'r', label = r'$t = \frac{1}{4}t_f$')
plt.plot(rmesh_tot, h2_vs_r_T, color = 'g', label = r'$t = \frac{1}{2}t_f$')
plt.plot(rmesh_tot, h3_vs_r_T, color = 'b', label = r'$t = \frac{3}{4}t_f$')
plt.plot(rmesh_tot, hf_vs_r_T, color = 'k', label = r'$t = t_f$')
plt.xlabel(r'$r/r_D(0)$')
plt.ylabel(r'$h(r/r_D(0), t)/r_D(0)$')
plt.yticks([])
plt.legend()
hI1_vs_r_T = np.insert(np.array(hI1_vs_r), 0, np.array(hI1_vs_r)[::-1][:-1])
hI2_vs_r_T = np.insert(np.array(hI2_vs_r), 0, np.array(hI2_vs_r)[::-1][:-1])
hI3_vs_r_T = np.insert(np.array(hI3_vs_r), 0, np.array(hI3_vs_r)[::-1][:-1])
hIf_vs_r_T = np.insert(np.array(hIf_vs_r), 0, np.array(hIf_vs_r)[::-1][:-1])
hO1_vs_r_T = normalise * np.insert(np.array(hO1_vs_r), 0, np.array(hO1_vs_r)[::-1][:-1])
hO2_vs_r_T = normalise * np.insert(np.array(hO2_vs_r), 0, np.array(hO2_vs_r)[::-1][:-1])
hO3_vs_r_T = normalise * np.insert(np.array(hO3_vs_r), 0, np.array(hO3_vs_r)[::-1][:-1])
hOf_vs_r_T = normalise * np.insert(np.array(hOf_vs_r), 0, np.array(hOf_vs_r)[::-1][:-1])
plt.figure(888, figsize = (11,11))
plt.plot(rmesh_tot, hf_vs_r_T, color = 'k', lw = 8, label = r'$h(r/r_D(0), t = t_f)$')
plt.plot(rmesh_tot, hIf_vs_r_T, color = 'g', lw = 5, label = r'$h_I(r/r_D(0), t = t_f)$')
plt.plot(rmesh_tot, hOf_vs_r_T, color = 'r', lw = 5, label = r'$h_O(r/r_D(0), t = t_f)$')
plt.xlabel(r'$r/r_D(0)$')
plt.ylabel(r'Height $(r_D(0))$')
plt.yticks([])
plt.legend()
plt.figure(889, figsize = (11,11))
plt.plot(rmeshf_T, hf_vs_r_T, color = 'k', lw = 8, label = r'$h(r/r_D(0), t = t_f)$')
plt.ylim(0, 1.7 * max(hf_vs_r_T))
plt.xlabel(r'$r/r_D(0)$')
plt.ylabel(r'Height $(r_D(0))$')
plt.yticks([])
cut = 285
rmesh_tot = rmesh_tot[cut:][:-cut]
hI1_vs_r_T = hI1_vs_r_T[cut:][:-cut]
hI2_vs_r_T = hI2_vs_r_T[cut:][:-cut]
hI3_vs_r_T = hI3_vs_r_T[cut:][:-cut]
hIf_vs_r_T = hIf_vs_r_T[cut:][:-cut]
hO1_vs_r_T = hO1_vs_r_T[cut:][:-cut]
hO2_vs_r_T = hO2_vs_r_T[cut:][:-cut]
hO3_vs_r_T = hO3_vs_r_T[cut:][:-cut]
hOf_vs_r_T = hOf_vs_r_T[cut:][:-cut]
h1_vs_r_T = h1_vs_r_T[cut:][:-cut]
h2_vs_r_T = h2_vs_r_T[cut:][:-cut]
h3_vs_r_T = h3_vs_r_T[cut:][:-cut]
hf_vs_r_T = hf_vs_r_T[cut:][:-cut]
lw = 4
k_lw = lw * 2.5
fs = 30
fig, axs = plt.subplots(4, 3, sharex='col', sharey='row',
gridspec_kw={'hspace': 0, 'wspace': 0})
(ax1, ax2, ax3), (ax4, ax5, ax6), (ax7, ax8, ax9), (ax10, ax11, ax12) = axs
#fig.suptitle('Sharing x per column, y per row')
ax1.plot(rmesh_tot, hI1_vs_r_T, 'g', linewidth = lw)
ax2.plot(rmesh_tot, hO1_vs_r_T, 'r', linewidth = lw)
ax3.plot(rmesh_tot, h1_vs_r_T, 'k', linewidth = k_lw)
ax3.plot(rmesh_tot, hI1_vs_r_T, 'g', linewidth = lw)
ax3.plot(rmesh_tot, hO1_vs_r_T, 'r', linewidth = lw)
ax1.set(title = r'$h_I(r, t)$')
ax2.set(title = r'$h_O(r, t)$')
ax3.set(title = r'$h(r, t)$')
ax1.text(-4.2, 0.2, '(a)', fontsize=fs)
ax4.plot(rmesh_tot, hI2_vs_r_T, 'g', linewidth = lw)
ax5.plot(rmesh_tot, hO2_vs_r_T, 'r', linewidth = lw)
ax6.plot(rmesh_tot, h2_vs_r_T, 'k', linewidth = k_lw)
ax6.plot(rmesh_tot, hI2_vs_r_T, 'g', linewidth = lw)
ax6.plot(rmesh_tot, hO2_vs_r_T, 'r', linewidth = lw)
ax4.text(-4.2, 0.2, '(b)', fontsize=fs)
ax7.plot(rmesh_tot, hI3_vs_r_T, 'g', linewidth = lw)
ax8.plot(rmesh_tot, hO3_vs_r_T, 'r', linewidth = lw)
ax9.plot(rmesh_tot, h3_vs_r_T, 'k', linewidth = k_lw)
ax9.plot(rmesh_tot, hI3_vs_r_T, 'g', linewidth = lw)
ax9.plot(rmesh_tot, hO3_vs_r_T, 'r', linewidth = lw)
ax7.text(-4.2, 0.2, '(c)', fontsize=fs)
ax7.set(ylabel=r'Height ($r_D(0)$)')
ax10.plot(rmesh_tot, hIf_vs_r_T, 'g', linewidth = lw)
ax11.plot(rmesh_tot, hOf_vs_r_T, 'r', linewidth = lw)
ax11.set(xlabel=r'$r/r_D(0)$')
ax12.plot(rmesh_tot, hf_vs_r_T, 'k', linewidth = k_lw)
ax12.plot(rmesh_tot, hIf_vs_r_T, 'g', linewidth = lw)
ax12.plot(rmesh_tot, hOf_vs_r_T, 'r', linewidth = lw)
ax10.text(-4.2, 0.2, '(d)', fontsize=fs)
for ax in axs.flat:
ax.set(ylim = (0, 1.1 * max(hf_vs_r_T)))
for ax in axs.flat:
ax.set(yticks = [])
for ax in axs.flat:
ax.label_outer()
#Following piece of code for 3D plot adapted from code written by ImportanceOfBeingErnest on https://stackoverflow.com/questions/47333811/how-do-i-create-a-surface-plot-with-matplotlib-of-a-closed-loop-revolve-about-an
x_values = rmeshf
# input xy coordinates
x_y_coordinates = [[x_values[i], hf_vs_r[i]] for i in range(len(x_values))]
xy = np.array(x_y_coordinates)
# radial component is x values of input
r = xy[:,0]
# angular component is one revolution of 60 steps
phi = np.linspace(0, 2*np.pi, 60)
# create grid
R,Phi = np.meshgrid(r,phi)
# transform to cartesian coordinates
X = R*np.cos(Phi)
Y = R*np.sin(Phi)
# Z values are y values, repeated 60 times
Z = np.tile(xy[:,1],len(Y)).reshape(Y.shape)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax2 = fig.add_axes([0.05,0.7,0.15,.2])
ax2.plot(xy[:,0],xy[:,1], color="k")
ax.plot_surface(X, Y, Z, alpha=0.5, color='gold', rstride=1, cstride=1)
plt.show()