def dvdn_exact(beta, mu0, r, R, n):
from scitools.std import log
term1 = (beta / (2. * mu0))**(1. / n)
term2 = n / (n + 1.)
term3 = (R**(1 + 1. / n) - r**(1 + 1. / n))
sum = 0
sum += -1./n**2 * log(beta/(2.*mu0)) * \
term1 * term2 * term3
sum += 1. / (n + 1)**2 * term1 * term3
sum += (-1./n**2) * \
(R**(1+1./n)*log(R) - r**(1+1./n)*log(r))\
* term1 * term2
return sum
def dvdn_exact(beta, mu0, r, R, n): from scitools.std import log term1 = (beta / (2.*mu0))**(1./n) term2 = n/(n+1.) term3 = (R**(1+1./n) - r**(1+1./n)) sum = 0 sum += -1./n**2 * log(beta/(2.*mu0)) * \ term1 * term2 * term3 sum += 1./(n+1)**2 * term1 * term3 sum += (-1./n**2) * \ (R**(1+1./n)*log(R) - r**(1+1./n)*log(r))\ * term1 * term2 return sum
def test_convergenceRates():
"""
Test for the convergence rates of the solver.
The expected result is that the variable r takes the value 2, because
the Crank-Nicolson scheme and the geometric average have errors of order
dt**2. The final error should then be O(dt**2) which gives r=2.
"""
dt_start = 1.0
num_dt = 10
E_values = zeros(num_dt)
T = 10.
g = 9.81
m = 50.
Cd = 1.2
rho = 1.0
A = 0.5
a = Cd * rho * A / (2. * m)
dt = zeros(num_dt)
dt[0] = dt_start
for i in range(1, len(dt)):
dt[i] = dt[i - 1] / 2.
D = -0.39
B = 0.76
C = -0.145
def exact(t):
return D * t**3 + B * t + C
def src(t):
return m * g + m * a * abs(
exact(t)) * exact(t) + m * (3 * D * t**2 + B)
# Simulate for different timesteps, and store the error in E_values
for i in range(num_dt):
v, t = solver(T, dt[i], exact(0), Cd, rho, A, m, src)
ve = exact(t)
diff = v - ve
E_values[i] = sqrt(dt[i] * sum(diff**2))
# Calculate r-values corresponding to the error with each different timestep
r = zeros(len(E_values) - 1)
for i in range(1, len(r)):
r[i] = (log(E_values[i - 1] / E_values[i])) / (log(dt[i - 1] / dt[i]))
import nose.tools as nt
nt.assert_almost_equal(r[-1], 2, delta=0.1)
def test_convergenceRates():
"""
Test for the convergence rates of the solver.
The expected result is that the variable r takes the value 2, because
the Crank-Nicolson scheme and the geometric average have errors of order
dt**2. The final error should then be O(dt**2) which gives r=2.
"""
dt_start = 1.0; num_dt = 10
E_values = zeros(num_dt)
T = 10.; g = 9.81; m = 50.; Cd = 1.2; rho = 1.0; A = 0.5;
a = Cd*rho*A/(2.*m)
dt = zeros(num_dt); dt[0] = dt_start
for i in range(1,len(dt)):
dt[i] = dt[i-1]/2.
D = -0.39; B = 0.76; C = -0.145
def exact(t):
return D*t**3 + B*t + C
def src(t):
return m*g + m*a*abs(exact(t))*exact(t) + m*(3*D*t**2 + B)
# Simulate for different timesteps, and store the error in E_values
for i in range(num_dt):
v, t = solver(T, dt[i], exact(0), Cd, rho, A, m, src)
ve = exact(t)
diff = v - ve
E_values[i] = sqrt(dt[i]*sum(diff**2))
# Calculate r-values corresponding to the error with each different timestep
r=zeros(len(E_values)-1)
for i in range(1, len(r)):
r[i] = (log(E_values[i-1]/E_values[i]))/(log(dt[i-1]/dt[i]))
import nose.tools as nt
nt.assert_almost_equal(r[-1], 2, delta=0.1)
def visualize(t):
plot(range(len(t)), log(abs(t)), ’ro’)
# or just plot(log(abs(t)), ’ro’)
show()
A1 = 15 # amplitude of the daily temperature variations (in C)
P1 = 24 * 60 * 60. # oscillation period of 24 h (in seconds)
# angular frequency of daily temperature variations (in rad/s)
omega1 = 2 * pi / P1
a1 = sqrt(omega1 / (2 * k))
A2 = 7 # amplitude of yearly temperature variations (in C)
P2 = 24 * 60 * 60 * 365. # oscillation period of 1 yr (in seconds)
# angular frequency of yearly temperature variations (in rad/s)
omega2 = 2 * pi / P2
a2 = sqrt(omega2 / (2 * k))
dt = P2 / 30 # time lag: 0.1 yr
tmax = 3 * P2 # 3 year simulation
T0 = 10 # mean surface temperature in Celsius
D = -(1 / a1) * log(0.001) # max depth
n = 501 # no of points in the z direction
z = linspace(0, D, n)
def z_scaled(x, s):
a = x[0]
b = x[-1]
return a + (b - a) * ((x - a) / (b - a))**s
zs = z_scaled(z, 3)
animate(tmax, dt, zs, T, T0 - A2 - A1, T0 + A2 + A1, 0, 'z', 'T')
k = 1E-6 # thermal diffusivity (in m**2/s)
A1 = 15 # amplitude of the daily temperature variations (in C)
P1 = 24 * 60 * 60. # oscillation period of 24 h (in seconds)
omega1 = 2 * pi / P1 # angular freq of daily temp variations (in rad/s)
a1 = sqrt(omega1 / (2 * k))
A2 = 7 # amplitude of yearly temperature variations (in C)
P2 = 24 * 60 * 60 * 365. # oscillation period of 1 yr (in seconds)
omega2 = 2 * pi / P2 # angular freq of yearly temp variations (in rad/s)
a2 = sqrt(omega2 / (2 * k))
dt = P2 / 20 # time lag: 0.1 yr
tmax = 3 * P2 # 3 year simulation
T0 = 10 # mean surface temperature in Celsius
D = -(1 / a1) * log(0.001) # max depth
n = 501 # no of points in the z direction
# set T0, A, k, omega, D, n, tmax, dt
z = linspace(0, D, n)
animate(tmax, dt, z, T, T0 - A2 - A1, T0 + A2 + A1, 0, 'z', 'T')
movie('tmp_*.png', encoder='convert', fps=6, outputfile='tmp_heatwave.gif')
import glob
import os
# Remove frames
for filename in glob.glob('tmp_*.png'):
os.remove(filename)
def f(x): return log(x)
def test_undampedWaves():
#define constants given in exercise
A = 1
mx=7.
my=2.
#define function that give
q=lambda x,y: 1
#define some variables
Lx = 3
Ly = 1.3
T = 1
C = 0.5
dt= 0.1
#define omega so equation holds
w=pi*sqrt((mx/Lx)**2 +(my/Ly)**2)
#help varabeles
kx = pi*mx/Lx
ky = pi*my/Ly
#Exact solution
ue = lambda x,y,t: A*cos(x*kx)*cos(y*ky)*cos(t*w)
#initial condition so we get result we want.
I = lambda x,y: A*cos(x*kx)*cos(y*ky)
#factor dt decreeses per step
step=0.5
#number of steps I want to do
val=5
#array to store errors
E=zeros(val)
for i in range(val):
v='vector'
#solve eqation
u,x,y,t,e=solver(I,None,None,q,0,Lx,Ly,dt*step**(i),T,C,1,mode=v,ue=ue)
E[i]=e
#find convergence rate between diffrent dt values
r =zeros(val-1)
r = log(E[1:]/E[:-1])/log(step)
print "Test convergence for undamped waves:"
for i in range(val):
if i==0:
print "dt: ",dt," Error: ",E[i]
else:
print "dt: ",dt*step**(i)," Error: ",E[i], "rate of con.: ", r[i-1]
#requiere close to 2 in convergence rate for last r.
assert abs(r[-1]-2)<0.01
def test_manufactured():
A=1
B=0
mx=1
my=1
b=1
c=1.1
#define some variables
Lx = 2.
Ly = 2.
T = 1
C = 0.3
dt= 0.1
#help varabeles
kx = pi*mx/Lx
ky = pi*my/Ly
w=1
#Exact solution
ue = lambda x,y,t: A*cos(x*kx)*cos(y*ky)*cos(t*w)*exp(-c*t)
I = lambda x,y: A*cos(x*kx)*cos(y*ky)
V = lambda x,y: -c*A*cos(x*kx)*cos(y*ky)
q = lambda x,y: x**2+y**2
f = lambda x,y,t:A*(-b*(c*cos(t*w) + w*sin(t*w))*cos(kx*x)*cos(ky*y) + c**2*cos(kx*x)*cos(ky*y)*cos(t*w) + 2*c*w*sin(t*w)*cos(kx*x)*cos(ky*y) + kx**2*(x**2 + y**2)*cos(kx*x)*cos(ky*y)*cos(t*w) + 2*kx*x*sin(kx*x)*cos(ky*y)*cos(t*w) + ky**2*(x**2 + y**2)*cos(kx*x)*cos(ky*y)*cos(t*w) + 2*ky*y*sin(ky*y)*cos(kx*x)*cos(t*w) - w**2*cos(kx*x)*cos(ky*y)*cos(t*w))*exp(-c*t)
#factor dt decreeses per step
step=0.5
#number of steps I want to do
val=5
#array to store errors
E=zeros(val)
for i in range(val):
v='vector'
#solve eqation
u,x,y,t,e=solver(I,V,f,q,b,Lx,Ly,dt*step**(i),T,C,8,mode=v,ue=ue)
E[i]=e
#find convergence rate between diffrent dt values
r =zeros(val-1)
r = log(E[1:]/E[:-1])/log(step)
print
print "Convergence rates for manufactured solution:"
for i in range(val):
if i==0:
print "dt: ",dt," Error: ",E[i]
else:
print "dt: ",dt*step**(i)," Error: ",E[i], "rate of con.: ", r[i-1]
#requiere "close" to 2 in convergence rate for last r.
assert abs(r[-1]-2)<0.5
def test_manufactured():
A = 1
B = 0
mx = 1
my = 1
b = 1
c = 1.1
#define some variables
Lx = 2.
Ly = 2.
T = 1
C = 0.3
dt = 0.1
#help varabeles
kx = pi * mx / Lx
ky = pi * my / Ly
w = 1
#Exact solution
ue = lambda x, y, t: A * cos(x * kx) * cos(y * ky) * cos(t * w) * exp(-c *
t)
I = lambda x, y: A * cos(x * kx) * cos(y * ky)
V = lambda x, y: -c * A * cos(x * kx) * cos(y * ky)
q = lambda x, y: x**2 + y**2
f = lambda x, y, t: A * (
-b * (c * cos(t * w) + w * sin(t * w)) * cos(kx * x) * cos(ky * y) + c
**2 * cos(kx * x) * cos(ky * y) * cos(t * w) + 2 * c * w * sin(
t * w) * cos(kx * x) * cos(ky * y) + kx**2 *
(x**2 + y**2) * cos(kx * x) * cos(ky * y) * cos(t * w) + 2 * kx * x *
sin(kx * x) * cos(ky * y) * cos(t * w) + ky**2 *
(x**2 + y**2) * cos(kx * x) * cos(ky * y) * cos(t * w) + 2 * ky * y *
sin(ky * y) * cos(kx * x) * cos(t * w) - w**2 * cos(kx * x) * cos(
ky * y) * cos(t * w)) * exp(-c * t)
#factor dt decreeses per step
step = 0.5
#number of steps I want to do
val = 5
#array to store errors
E = zeros(val)
for i in range(val):
v = 'vector'
#solve eqation
u, x, y, t, e = solver(I,
V,
f,
q,
b,
Lx,
Ly,
dt * step**(i),
T,
C,
8,
mode=v,
ue=ue)
E[i] = e
#find convergence rate between diffrent dt values
r = zeros(val - 1)
r = log(E[1:] / E[:-1]) / log(step)
print
print "Convergence rates for manufactured solution:"
for i in range(val):
if i == 0:
print "dt: ", dt, " Error: ", E[i]
else:
print "dt: ", dt * step**(
i), " Error: ", E[i], "rate of con.: ", r[i - 1]
#requiere "close" to 2 in convergence rate for last r.
assert abs(r[-1] - 2) < 0.5
def test_undampedWaves():
#define constants given in exercise
A = 1
mx = 7.
my = 2.
#define function that give
q = lambda x, y: 1
#define some variables
Lx = 3
Ly = 1.3
T = 1
C = 0.5
dt = 0.1
#define omega so equation holds
w = pi * sqrt((mx / Lx)**2 + (my / Ly)**2)
#help varabeles
kx = pi * mx / Lx
ky = pi * my / Ly
#Exact solution
ue = lambda x, y, t: A * cos(x * kx) * cos(y * ky) * cos(t * w)
#initial condition so we get result we want.
I = lambda x, y: A * cos(x * kx) * cos(y * ky)
#factor dt decreeses per step
step = 0.5
#number of steps I want to do
val = 5
#array to store errors
E = zeros(val)
for i in range(val):
v = 'vector'
#solve eqation
u, x, y, t, e = solver(I,
None,
None,
q,
0,
Lx,
Ly,
dt * step**(i),
T,
C,
1,
mode=v,
ue=ue)
E[i] = e
#find convergence rate between diffrent dt values
r = zeros(val - 1)
r = log(E[1:] / E[:-1]) / log(step)
print "Test convergence for undamped waves:"
for i in range(val):
if i == 0:
print "dt: ", dt, " Error: ", E[i]
else:
print "dt: ", dt * step**(
i), " Error: ", E[i], "rate of con.: ", r[i - 1]
#requiere close to 2 in convergence rate for last r.
assert abs(r[-1] - 2) < 0.01
