In [1]:
from sympy import *
from sympy.vector import CoordSys3D
N = CoordSys3D('N')
x1, x2, x3 = symbols("x_1 x_2 x_3")
alpha1, alpha2, alpha3 = symbols("alpha_1 alpha_2 alpha_3")
R, L, ga, gv = symbols("R L g_a g_v")
init_printing()
In [2]:
a1 = pi / 2 + (L / 2 - alpha1)/R
x = (R + alpha3 + ga * cos(gv * a1)) * cos(a1)
y = alpha2
z = (R + alpha3 + ga * cos(gv * a1)) * sin(a1)
r = x*N.i + y*N.j + z*N.k
In [3]:
R1=r.diff(alpha1)
R2=r.diff(alpha2)
R3=r.diff(alpha3)
In [4]:
trigsimp(R1)
Out[4]:
In [5]:
R2
Out[5]:
In [6]:
R3
Out[6]:
In [7]:
eps=trigsimp(R1.dot(R2.cross(R3)))
R_1=simplify(trigsimp(R2.cross(R3)/eps))
R_2=simplify(trigsimp(R3.cross(R1)/eps))
R_3=simplify(trigsimp(R1.cross(R2)/eps))
In [8]:
R_1
Out[8]:
In [9]:
R_2
Out[9]:
In [10]:
R_3
Out[10]:
$ A = \left( \begin{array}{ccc} \frac{\partial x_1}{\partial \alpha_1} & \frac{\partial x_1}{\partial \alpha_2} & \frac{\partial x_1}{\partial \alpha_3} \\ \frac{\partial x_2}{\partial \alpha_1} & \frac{\partial x_2}{\partial \alpha_2} & \frac{\partial x_3}{\partial \alpha_3} \\ \frac{\partial x_3}{\partial \alpha_1} & \frac{\partial x_3}{\partial \alpha_2} & \frac{\partial x_3}{\partial \alpha_3} \\ \end{array} \right)$
$ \left[ \begin{array}{ccc} \vec{R}_1 & \vec{R}_2 & \vec{R}_3 \end{array} \right] = \left[ \begin{array}{ccc} \vec{e}_1 & \vec{e}_2 & \vec{e}_3 \end{array} \right] \cdot \left( \begin{array}{ccc} \frac{\partial x_1}{\partial \alpha_1} & \frac{\partial x_1}{\partial \alpha_2} & \frac{\partial x_1}{\partial \alpha_3} \\ \frac{\partial x_2}{\partial \alpha_1} & \frac{\partial x_2}{\partial \alpha_2} & \frac{\partial x_3}{\partial \alpha_3} \\ \frac{\partial x_3}{\partial \alpha_1} & \frac{\partial x_3}{\partial \alpha_2} & \frac{\partial x_3}{\partial \alpha_3} \\ \end{array} \right) = \left[ \begin{array}{ccc} \vec{e}_1 & \vec{e}_2 & \vec{e}_3 \end{array} \right] \cdot A$
$ \left[ \begin{array}{ccc} \vec{e}_1 & \vec{e}_2 & \vec{e}_3 \end{array} \right] =\left[ \begin{array}{ccc} \vec{R}_1 & \vec{R}_2 & \vec{R}_3 \end{array} \right] \cdot A^{-1}$
In [12]:
dx1da1=R1.dot(N.i)
dx1da2=R2.dot(N.i)
dx1da3=R3.dot(N.i)
dx2da1=R1.dot(N.j)
dx2da2=R2.dot(N.j)
dx2da3=R3.dot(N.j)
dx3da1=R1.dot(N.k)
dx3da2=R2.dot(N.k)
dx3da3=R3.dot(N.k)
A=Matrix([[dx1da1, dx1da2, dx1da3], [dx2da1, dx2da2, dx2da3], [dx3da1, dx3da2, dx3da3]])
simplify(A)
Out[12]:
In [13]:
w=Function('w')
z=Function('z')
a=Function('a')
dx1da1 = -z(alpha1)*sin((L/2-alpha1)/R)+w(alpha1, alpha3)*cos((L/2-alpha1)/R)
dx1da2 = 0
dx1da3 = -sin((L/2-alpha1)/R)
dx2da1 = 0
dx2da2 = 1
dx2da3 = 0
dx3da1 = z(alpha1)*cos((L/2-alpha1)/R)+w(alpha1, alpha3)*sin((L/2-alpha1)/R)
dx3da2 = 0
dx3da3 = cos((L/2-alpha1)/R)
A_s=Matrix([[dx1da1, dx1da2, dx1da3], [dx2da1, dx2da2, dx2da3], [dx3da1, dx3da2, dx3da3]])
A_s
Out[13]:
In [19]:
A_inv = A_s**-1
A_inv=simplify(A_inv)
A_inv
Out[19]:
In [20]:
trigsimp(A_s.det())
Out[20]:
${\displaystyle \hat{G}=\sum_{i,j} g^{ij}\vec{R}_i\vec{R}_j}$
In [21]:
g11=R1.dot(R1)
g12=R1.dot(R2)
g13=R1.dot(R3)
g21=R2.dot(R1)
g22=R2.dot(R2)
g23=R2.dot(R3)
g31=R3.dot(R1)
g32=R3.dot(R2)
g33=R3.dot(R3)
G=Matrix([[g11, g12, g13],[g21, g22, g23], [g31, g32, g33]])
G=trigsimp(G)
G
Out[21]:
In [24]:
G_s=A_s.T*A_s
simplify(G_s)
Out[24]:
${\displaystyle \hat{G}=\sum_{i,j} g_{ij}\vec{R}^i\vec{R}^j}$
In [25]:
g_11=R_1.dot(R_1)
g_12=R_1.dot(R_2)
g_13=R_1.dot(R_3)
g_21=R_2.dot(R_1)
g_22=R_2.dot(R_2)
g_23=R_2.dot(R_3)
g_31=R_3.dot(R_1)
g_32=R_3.dot(R_2)
g_33=R_3.dot(R_3)
G_con=Matrix([[g_11, g_12, g_13],[g_21, g_22, g_23], [g_31, g_32, g_33]])
G_con=trigsimp(G_con)
G_con
Out[25]:
In [26]:
G_con_s=G_s**-1
simplify(G_con_s)
Out[26]:
In [17]:
dR1dalpha1 = trigsimp(R1.diff(alpha1))
dR1dalpha1
Out[17]:
$ \frac { d\vec{R_1} } { d\alpha_1} = -\frac {1}{R} \left( 1+\frac{\alpha_3}{R} \right) \vec{R_3} $
In [18]:
dR1dalpha2 = trigsimp(R1.diff(alpha2))
dR1dalpha2
Out[18]:
In [19]:
dR1dalpha3 = trigsimp(R1.diff(alpha3))
dR1dalpha3
Out[19]:
$ \frac { d\vec{R_1} } { d\alpha_3} = \frac {1}{R} \frac {1}{1+\frac{\alpha_3}{R}} \vec{R_1} $
In [20]:
dR2dalpha1 = trigsimp(R2.diff(alpha1))
dR2dalpha1
Out[20]:
In [21]:
dR2dalpha2 = trigsimp(R2.diff(alpha2))
dR2dalpha2
Out[21]:
In [22]:
dR2dalpha3 = trigsimp(R2.diff(alpha3))
dR2dalpha3
Out[22]:
In [23]:
dR3dalpha1 = trigsimp(R3.diff(alpha1))
dR3dalpha1
Out[23]:
$ \frac { d\vec{R_3} } { d\alpha_1} = \frac {1}{R} \frac {1}{1+\frac{\alpha_3}{R}} \vec{R_1} $
In [24]:
dR3dalpha2 = trigsimp(R3.diff(alpha2))
dR3dalpha2
Out[24]:
In [25]:
dR3dalpha3 = trigsimp(R3.diff(alpha3))
dR3dalpha3
Out[25]:
$ \frac { d\vec{R_3} } { d\alpha_3} = \vec{0} $
$ \frac { d\vec{u} } { d\alpha_1} = \frac { d(u^1\vec{R_1}) } { d\alpha_1} + \frac { d(u^2\vec{R_2}) } { d\alpha_1}+ \frac { d(u^3\vec{R_3}) } { d\alpha_1} = \frac { du^1 } { d\alpha_1} \vec{R_1} + u^1 \frac { d\vec{R_1} } { d\alpha_1} + \frac { du^2 } { d\alpha_1} \vec{R_2} + u^2 \frac { d\vec{R_2} } { d\alpha_1} + \frac { du^3 } { d\alpha_1} \vec{R_3} + u^3 \frac { d\vec{R_3} } { d\alpha_1} = \frac { du^1 } { d\alpha_1} \vec{R_1} - u^1 \frac {1}{R} \left( 1+\frac{\alpha_3}{R} \right) \vec{R_3} + \frac { du^2 } { d\alpha_1} \vec{R_2}+ \frac { du^3 } { d\alpha_1} \vec{R_3} + u^3 \frac {1}{R} \frac {1}{1+\frac{\alpha_3}{R}} \vec{R_1}$
Then $ \frac { d\vec{u} } { d\alpha_1} = \left( \frac { du^1 } { d\alpha_1} + u^3 \frac {1}{R} \frac {1}{1+\frac{\alpha_3}{R}} \right) \vec{R_1} + \frac { du^2 } { d\alpha_1} \vec{R_2} + \left( \frac { du^3 } { d\alpha_1} - u^1 \frac {1}{R} \left( 1+\frac{\alpha_3}{R} \right) \right) \vec{R_3}$
$ \frac { d\vec{u} } { d\alpha_2} = \frac { d(u^1\vec{R_1}) } { d\alpha_2} + \frac { d(u^2\vec{R_2}) } { d\alpha_2}+ \frac { d(u^3\vec{R_3}) } { d\alpha_2} = \frac { du^1 } { d\alpha_2} \vec{R_1} + \frac { du^2 } { d\alpha_2} \vec{R_2} + \frac { du^3 } { d\alpha_2} \vec{R_3} $
Then $ \frac { d\vec{u} } { d\alpha_2} = \frac { du^1 } { d\alpha_2} \vec{R_1} + \frac { du^2 } { d\alpha_2} \vec{R_2} + \frac { du^3 } { d\alpha_2} \vec{R_3} $
$ \frac { d\vec{u} } { d\alpha_3} = \frac { d(u^1\vec{R_1}) } { d\alpha_3} + \frac { d(u^2\vec{R_2}) } { d\alpha_3}+ \frac { d(u^3\vec{R_3}) } { d\alpha_3} = \frac { du^1 } { d\alpha_3} \vec{R_1} + u^1 \frac { d\vec{R_1} } { d\alpha_3} + \frac { du^2 } { d\alpha_3} \vec{R_2} + u^2 \frac { d\vec{R_2} } { d\alpha_3} + \frac { du^3 } { d\alpha_3} \vec{R_3} + u^3 \frac { d\vec{R_3} } { d\alpha_3} = \frac { du^1 } { d\alpha_3} \vec{R_1} + u^1 \frac {1}{R} \frac {1}{1+\frac{\alpha_3}{R}} \vec{R_1} + \frac { du^2 } { d\alpha_3} \vec{R_2}+ \frac { du^3 } { d\alpha_3} \vec{R_3} $
Then $ \frac { d\vec{u} } { d\alpha_3} = \left( \frac { du^1 } { d\alpha_3} + u^1 \frac {1}{R} \frac {1}{1+\frac{\alpha_3}{R}} \right) \vec{R_1} + \frac { du^2 } { d\alpha_3} \vec{R_2}+ \frac { du^3 } { d\alpha_3} \vec{R_3}$
$\nabla_1 u^1 = \frac { \partial u^1 } { \partial \alpha_1} + u^3 \frac {1}{R} \frac {1}{1+\frac{\alpha_3}{R}}$
$\nabla_1 u^2 = \frac { \partial u^2 } { \partial \alpha_1} $
$\nabla_1 u^3 = \frac { \partial u^3 } { \partial \alpha_1} - u^1 \frac {1}{R} \left( 1+\frac{\alpha_3}{R} \right) $
$\nabla_2 u^1 = \frac { \partial u^1 } { \partial \alpha_2}$
$\nabla_2 u^2 = \frac { \partial u^2 } { \partial \alpha_2}$
$\nabla_2 u^3 = \frac { \partial u^3 } { \partial \alpha_2}$
$\nabla_3 u^1 = \frac { \partial u^1 } { \partial \alpha_3} + u^1 \frac {1}{R} \frac {1}{1+\frac{\alpha_3}{R}}$
$\nabla_3 u^2 = \frac { \partial u^2 } { \partial \alpha_3} $
$\nabla_3 u^3 = \frac { \partial u^3 } { \partial \alpha_3}$
$ \nabla \vec{u} = \left( \begin{array}{ccc} \nabla_1 u^1 & \nabla_1 u^2 & \nabla_1 u^3 \\ \nabla_2 u^1 & \nabla_2 u^2 & \nabla_2 u^3 \\ \nabla_3 u^1 & \nabla_3 u^2 & \nabla_3 u^3 \\ \end{array} \right)$
In [37]:
u1=Function('u^1')
u2=Function('u^2')
u3=Function('u^3')
q=Function('q') # q(alpha3) = 1+alpha3/R
K = Symbol('K') # K = 1/R
u1_nabla1 = u1(alpha1, alpha2, alpha3).diff(alpha1) + u3(alpha1, alpha2, alpha3) * K / q(alpha3)
u2_nabla1 = u2(alpha1, alpha2, alpha3).diff(alpha1)
u3_nabla1 = u3(alpha1, alpha2, alpha3).diff(alpha1) - u1(alpha1, alpha2, alpha3) * K * q(alpha3)
u1_nabla2 = u1(alpha1, alpha2, alpha3).diff(alpha2)
u2_nabla2 = u2(alpha1, alpha2, alpha3).diff(alpha2)
u3_nabla2 = u3(alpha1, alpha2, alpha3).diff(alpha2)
u1_nabla3 = u1(alpha1, alpha2, alpha3).diff(alpha3) + u1(alpha1, alpha2, alpha3) * K / q(alpha3)
u2_nabla3 = u2(alpha1, alpha2, alpha3).diff(alpha3)
u3_nabla3 = u3(alpha1, alpha2, alpha3).diff(alpha3)
# $\nabla_2 u^2 = \frac { \partial u^2 } { \partial \alpha_2}$
grad_u = Matrix([[u1_nabla1, u2_nabla1, u3_nabla1],[u1_nabla2, u2_nabla2, u3_nabla2], [u1_nabla3, u2_nabla3, u3_nabla3]])
grad_u
Out[37]:
In [38]:
G_s = Matrix([[q(alpha3)**2, 0, 0],[0, 1, 0], [0, 0, 1]])
grad_u_down=grad_u*G_s
expand(simplify(grad_u_down))
Out[38]:
$ \left( \begin{array}{c} \nabla_1 u_1 \\ \nabla_2 u_1 \\ \nabla_3 u_1 \\ \nabla_1 u_2 \\ \nabla_2 u_2 \\ \nabla_3 u_2 \\ \nabla_1 u_3 \\ \nabla_2 u_3 \\ \nabla_3 u_3 \\ \end{array}
\left( \begin{array}{c} \left( 1+\frac{\alpha_2}{R} \right)^2 \frac { \partial u^1 } { \partial \alpha_1} + u^3 \frac {\left( 1+\frac{\alpha_3}{R} \right)}{R} \\ \left( 1+\frac{\alpha_2}{R} \right)^2 \frac { \partial u^1 } { \partial \alpha_2} \\ \left( 1+\frac{\alpha_3}{R} \right)^2 \frac { \partial u^1 } { \partial \alpha_3} + u^1 \frac {\left( 1+\frac{\alpha_3}{R} \right)}{R} \\ \frac { \partial u^2 } { \partial \alpha_1} \\ \frac { \partial u^2 } { \partial \alpha_2} \\ \frac { \partial u^2 } { \partial \alpha_3} \\ \frac { \partial u^3 } { \partial \alpha_1} - u^1 \frac {\left( 1+\frac{\alpha_3}{R} \right)}{R} \\ \frac { \partial u^3 } { \partial \alpha_2} \\ \frac { \partial u^3 } { \partial \alpha_3} \\ \end{array} \right) $
$ \left( \begin{array}{c} \nabla_1 u_1 \\ \nabla_2 u_1 \\ \nabla_3 u_1 \\ \nabla_1 u_2 \\ \nabla_2 u_2 \\ \nabla_3 u_2 \\ \nabla_1 u_3 \\ \nabla_2 u_3 \\ \nabla_3 u_3 \\ \end{array}
B \cdot \left( \begin{array}{c} u^1 \\ \frac { \partial u^1 } { \partial \alpha_1} \\ \frac { \partial u^1 } { \partial \alpha_2} \\ \frac { \partial u^1 } { \partial \alpha_3} \\ u^2 \\ \frac { \partial u^2 } { \partial \alpha_1} \\ \frac { \partial u^2 } { \partial \alpha_2} \\ \frac { \partial u^2 } { \partial \alpha_3} \\ u^3 \\ \frac { \partial u^3 } { \partial \alpha_1} \\ \frac { \partial u^3 } { \partial \alpha_2} \\ \frac { \partial u^3 } { \partial \alpha_3} \\ \end{array} \right) $
In [39]:
B = zeros(9, 12)
B[0,1] = (1+alpha3/R)**2
B[0,8] = (1+alpha3/R)/R
B[1,2] = (1+alpha3/R)**2
B[2,0] = (1+alpha3/R)/R
B[2,3] = (1+alpha3/R)**2
B[3,5] = S(1)
B[4,6] = S(1)
B[5,7] = S(1)
B[6,9] = S(1)
B[6,0] = -(1+alpha3/R)/R
B[7,10] = S(1)
B[8,11] = S(1)
B
Out[39]:
In [40]:
E=zeros(6,9)
E[0,0]=1
E[1,4]=1
E[2,8]=1
E[3,1]=1
E[3,3]=1
E[4,2]=1
E[4,6]=1
E[5,5]=1
E[5,7]=1
E
Out[40]:
In [41]:
Q=E*B
Q=simplify(Q)
Q
Out[41]:
$u^1 \left( \alpha_1, \alpha_2, \alpha_3 \right)=u\left( \alpha_1 \right)+\alpha_3\gamma \left( \alpha_1 \right) $
$u^2 \left( \alpha_1, \alpha_2, \alpha_3 \right)=0 $
$u^3 \left( \alpha_1, \alpha_2, \alpha_3 \right)=w\left( \alpha_1 \right) $
$ \left( \begin{array}{c} u^1 \\ \frac { \partial u^1 } { \partial \alpha_1} \\ \frac { \partial u^1 } { \partial \alpha_2} \\ \frac { \partial u^1 } { \partial \alpha_3} \\ u^2 \\ \frac { \partial u^2 } { \partial \alpha_1} \\ \frac { \partial u^2 } { \partial \alpha_2} \\ \frac { \partial u^2 } { \partial \alpha_3} \\ u^3 \\ \frac { \partial u^3 } { \partial \alpha_1} \\ \frac { \partial u^3 } { \partial \alpha_2} \\ \frac { \partial u^3 } { \partial \alpha_3} \\ \end{array} \right) = T \cdot \left( \begin{array}{c} u \\ \frac { \partial u } { \partial \alpha_1} \\ \gamma \\ \frac { \partial \gamma } { \partial \alpha_1} \\ w \\ \frac { \partial w } { \partial \alpha_1} \\ \end{array} \right) $
In [42]:
T=zeros(12,6)
T[0,0]=1
T[0,2]=alpha3
T[1,1]=1
T[1,3]=alpha3
T[3,2]=1
T[8,4]=1
T[9,5]=1
T
Out[42]:
In [43]:
Q=E*B*T
Q=simplify(Q)
Q
Out[43]:
In [27]:
from sympy import MutableDenseNDimArray
C_x = MutableDenseNDimArray.zeros(3, 3, 3, 3)
for i in range(3):
for j in range(3):
for k in range(3):
for l in range(3):
elem_index = 'C^{{{}{}{}{}}}'.format(i+1, j+1, k+1, l+1)
el = Symbol(elem_index)
C_x[i,j,k,l] = el
C_x
Out[27]:
In [28]:
C_x_symmetry = MutableDenseNDimArray.zeros(3, 3, 3, 3)
def getCIndecies(index):
if (index == 0):
return 0, 0
elif (index == 1):
return 1, 1
elif (index == 2):
return 2, 2
elif (index == 3):
return 0, 1
elif (index == 4):
return 0, 2
elif (index == 5):
return 1, 2
for s in range(6):
for t in range(s, 6):
i,j = getCIndecies(s)
k,l = getCIndecies(t)
elem_index = 'C^{{{}{}{}{}}}'.format(i+1, j+1, k+1, l+1)
el = Symbol(elem_index)
C_x_symmetry[i,j,k,l] = el
C_x_symmetry[i,j,l,k] = el
C_x_symmetry[j,i,k,l] = el
C_x_symmetry[j,i,l,k] = el
C_x_symmetry[k,l,i,j] = el
C_x_symmetry[k,l,j,i] = el
C_x_symmetry[l,k,i,j] = el
C_x_symmetry[l,k,j,i] = el
C_x_symmetry
Out[28]:
In [29]:
C_isotropic = MutableDenseNDimArray.zeros(3, 3, 3, 3)
C_isotropic_matrix = zeros(6)
mu = Symbol('mu')
la = Symbol('lambda')
for s in range(6):
for t in range(s, 6):
if (s < 3 and t < 3):
if(t != s):
C_isotropic_matrix[s,t] = la
C_isotropic_matrix[t,s] = la
else:
C_isotropic_matrix[s,t] = 2*mu+la
C_isotropic_matrix[t,s] = 2*mu+la
elif (s == t):
C_isotropic_matrix[s,t] = mu
C_isotropic_matrix[t,s] = mu
for s in range(6):
for t in range(s, 6):
i,j = getCIndecies(s)
k,l = getCIndecies(t)
el = C_isotropic_matrix[s, t]
C_isotropic[i,j,k,l] = el
C_isotropic[i,j,l,k] = el
C_isotropic[j,i,k,l] = el
C_isotropic[j,i,l,k] = el
C_isotropic[k,l,i,j] = el
C_isotropic[k,l,j,i] = el
C_isotropic[l,k,i,j] = el
C_isotropic[l,k,j,i] = el
C_isotropic
Out[29]:
In [34]:
def getCalpha(C, A, q, p, s, t):
res = S(0)
for i in range(3):
for j in range(3):
for k in range(3):
for l in range(3):
res += C[i,j,k,l]*A[q,i]*A[p,j]*A[s,k]*A[t,l]
return simplify(res)
C_isotropic_alpha = MutableDenseNDimArray.zeros(3, 3, 3, 3)
for i in range(3):
for j in range(3):
for k in range(3):
for l in range(3):
c = getCalpha(C_isotropic, A_inv, i, j, k, l)
C_isotropic_alpha[i,j,k,l] = c
In [35]:
C_isotropic_matrix_alpha = zeros(6)
for s in range(6):
for t in range(6):
i,j = getCIndecies(s)
k,l = getCIndecies(t)
C_isotropic_matrix_alpha[s,t] = simplify(C_isotropic_alpha[i,j,k,l])
C_isotropic_matrix_alpha
Out[35]:
In [36]:
C_orthotropic = MutableDenseNDimArray.zeros(3, 3, 3, 3)
C_orthotropic_matrix = zeros(6)
for s in range(6):
for t in range(s, 6):
elem_index = 'C^{{{}{}}}'.format(s+1, t+1)
el = Symbol(elem_index)
if ((s < 3 and t < 3) or t == s):
C_orthotropic_matrix[s,t] = el
C_orthotropic_matrix[t,s] = el
for s in range(6):
for t in range(s, 6):
i,j = getCIndecies(s)
k,l = getCIndecies(t)
el = C_orthotropic_matrix[s, t]
C_orthotropic[i,j,k,l] = el
C_orthotropic[i,j,l,k] = el
C_orthotropic[j,i,k,l] = el
C_orthotropic[j,i,l,k] = el
C_orthotropic[k,l,i,j] = el
C_orthotropic[k,l,j,i] = el
C_orthotropic[l,k,i,j] = el
C_orthotropic[l,k,j,i] = el
C_orthotropic
Out[36]:
In [37]:
def getCalpha(C, A, q, p, s, t):
res = S(0)
for i in range(3):
for j in range(3):
for k in range(3):
for l in range(3):
res += C[i,j,k,l]*A[q,i]*A[p,j]*A[s,k]*A[t,l]
return simplify(res)
C_orthotropic_alpha = MutableDenseNDimArray.zeros(3, 3, 3, 3)
for i in range(3):
for j in range(3):
for k in range(3):
for l in range(3):
c = getCalpha(C_orthotropic, A_inv, i, j, k, l)
C_orthotropic_alpha[i,j,k,l] = c
In [38]:
C_orthotropic_matrix_alpha = zeros(6)
for s in range(6):
for t in range(6):
i,j = getCIndecies(s)
k,l = getCIndecies(t)
C_orthotropic_matrix_alpha[s,t] = C_orthotropic_alpha[i,j,k,l]
C_orthotropic_matrix_alpha
Out[38]:
${\displaystyle A=\int_{0}^{L}\int_{h_1}^{h_2} \left( 1+\frac{\alpha_3}{R} \right) d \alpha_1 d \alpha_3}, L=R \theta$
In [88]:
square_int=integrate(integrate(1+alpha3/R, (alpha3, h1, h2)), (alpha1, 0, theta*R))
expand(simplify(square_int))
Out[88]: