Example of a high-order approximation
[1]:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.dpi']= 300
from sympy import *
[2]:
# parameters
c = 5 # velocity
K = 1 # diffusion constant
nx = 100 # grid points
x0 = 0 # left boundary
x1 = 1 # right boundary
# make the mesh and the grid spacing
X = np.linspace(x0,x1, nx)
[3]:
# analytical solution
C2 = 1/(1-np.exp(c/K))
C1 = -C2
u_ex = C1*np.exp(c/K*X) + C2
[4]:
# hide: the code in this cell is hidden by the author
[5]:
# collocation method
#a0,a1,a2,a3,x = symbols('a0 a1 a2 a3 x')
#x1 = 1/3 #collocation point
#x2 = 2/3 #collocation point
#R1 = ???
#R2 = ???
#R3 = ???
#R4 = ???
#sol = linsolve([?,?,?,?], (a0,a1,a2,a3))
#a0 = float(list(sol)[0][0].n()) # convert result to float
#a1 = float(list(sol)[0][1].n()) # convert result to float
#a2 = float(list(sol)[0][2].n()) # convert result to float
#a3 = float(list(sol)[0][3].n()) # convert result to float
#u_c2 = a0 + a1*X + a2*np.square(X) + a3*np.power(X, 3)
#compute the RMS error
#E_rms_c2 = sqrt(np.sum(np.square(u_ex - u_c2))/nx)
#E_x_c2 = np.sqrt(np.square(u_ex - u_c2)/nx)
[6]:
# plotting
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,7.5))
# prepare axis
# function value
ax1.set_xlim(0, 1)
ax1.set_ylim(0, 1)
ax1.plot(X, u_ex, lw=1, label='Analytical solution')
ax1.plot(X, u_c2, lw=1, label='Collocation method')
ax1.legend()
ax1.set_xlabel('Distance')
ax1.set_ylabel('Function value')
# error estimate
ax2.set_xlim(0, 1)
ax2.set_ylim(0, 0.01)
ax2.plot([],[],lw=1)
ax2.plot(X, E_x_c2, lw=1, label='Higher-order Collocation method')
ax2.legend()
ax2.set_xlabel('Distance')
ax2.set_ylabel('RMS Error')
[6]:
Text(0, 0.5, 'RMS Error')
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