In [2]:
from sympy import *
from geom_util import *
from sympy.vector import CoordSys3D
N = CoordSys3D('N')
alpha1, alpha2, alpha3 = symbols("alpha_1 alpha_2 alpha_3", real = True, positive=True)
init_printing()
%matplotlib inline
%reload_ext autoreload
%autoreload 2
%aimport geom_util
In [3]:
A,K = symbols('A K')
dA1 = Symbol('A_{{,1}}')
dK1 = Symbol('K_{{,1}}')
H1=A*(1+alpha3*K)
H2=S(1)
H3=S(1)
H=[H1, H2, H3]
DIM=3
dH = zeros(DIM,DIM)
dH[0,0]=dA1+alpha3*(dA1*K+A*dK1)
dH[0,2]=A*K
dH
Out[3]:
${\displaystyle \hat{G}=\sum_{i,j} g^{ij}\vec{R}_i\vec{R}_j}$
In [4]:
G_up = getMetricTensorUpLame(H1, H2, H3)
${\displaystyle \hat{G}=\sum_{i,j} g_{ij}\vec{R}^i\vec{R}^j}$
In [5]:
G_down = getMetricTensorDownLame(H1, H2, H3)
In [6]:
DIM=3
G_down_diff = MutableDenseNDimArray.zeros(DIM, DIM, DIM)
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
G_down_diff[i,i,k]=2*H[i]*dH[i,k]
GK = getChristoffelSymbols2(G_up, G_down_diff, (alpha1, alpha2, alpha3))
GK
Out[6]:
$ \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) = B \cdot D \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 [7]:
def row_index_to_i_j_grad(i_row):
return i_row // 3, i_row % 3
B = zeros(9, 12)
B[0,1] = S(1)
B[1,2] = S(1)
B[2,3] = S(1)
B[3,5] = S(1)
B[4,6] = S(1)
B[5,7] = S(1)
B[6,9] = S(1)
B[7,10] = S(1)
B[8,11] = S(1)
for row_index in range(9):
i,j=row_index_to_i_j_grad(row_index)
B[row_index, 0] = -GK[i,j,0]
B[row_index, 4] = -GK[i,j,1]
B[row_index, 8] = -GK[i,j,2]
B
Out[7]:
$u_i=u_{[i]} H_i$
In [8]:
P=zeros(12,12)
P[0,0]=H[0]
P[1,0]=dH[0,0]
P[1,1]=H[0]
P[2,0]=dH[0,1]
P[2,2]=H[0]
P[3,0]=dH[0,2]
P[3,3]=H[0]
P[4,4]=H[1]
P[5,4]=dH[1,0]
P[5,5]=H[1]
P[6,4]=dH[1,1]
P[6,6]=H[1]
P[7,4]=dH[1,2]
P[7,7]=H[1]
P[8,8]=H[2]
P[9,8]=dH[2,0]
P[9,9]=H[2]
P[10,8]=dH[2,1]
P[10,10]=H[2]
P[11,8]=dH[2,2]
P[11,11]=H[2]
P=simplify(P)
P
Out[8]:
In [23]:
B_P = zeros(9,9)
for i in range(3):
for j in range(3):
row_index = i*3+j
B_P[row_index, row_index] = 1/(H[i]*H[j])
Grad_U_P = simplify(B_P*B*P)
Grad_U_P
# print(Grad_U_P)
Out[23]:
$ \left( \begin{array}{c} \varepsilon_{11} \\ \varepsilon_{22} \\ \varepsilon_{33} \\ 2\varepsilon_{12} \\ 2\varepsilon_{13} \\ 2\varepsilon_{23} \\ \end{array}
\left(E + E_{NL} \left( \nabla \vec{u} \right) \right) \cdot \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} \right)$
In [10]:
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[10]:
In [11]:
StrainL=simplify(E*Grad_U_P)
StrainL
Out[11]:
In [12]:
def E_NonLinear(grad_u):
N = 3
du = zeros(N, N)
# print("===Deformations===")
for i in range(N):
for j in range(N):
index = i*N+j
du[j,i] = grad_u[index]
# print("========")
I = eye(3)
a_values = S(1)/S(2) * du * G_up
E_NL = zeros(6,9)
E_NL[0,0] = a_values[0,0]
E_NL[0,3] = a_values[0,1]
E_NL[0,6] = a_values[0,2]
E_NL[1,1] = a_values[1,0]
E_NL[1,4] = a_values[1,1]
E_NL[1,7] = a_values[1,2]
E_NL[2,2] = a_values[2,0]
E_NL[2,5] = a_values[2,1]
E_NL[2,8] = a_values[2,2]
E_NL[3,1] = 2*a_values[0,0]
E_NL[3,4] = 2*a_values[0,1]
E_NL[3,7] = 2*a_values[0,2]
E_NL[4,0] = 2*a_values[2,0]
E_NL[4,3] = 2*a_values[2,1]
E_NL[4,6] = 2*a_values[2,2]
E_NL[5,2] = 2*a_values[1,0]
E_NL[5,5] = 2*a_values[1,1]
E_NL[5,8] = 2*a_values[1,2]
return E_NL
%aimport geom_util
u=getUHat3DPlane(alpha1, alpha2, alpha3)
# u=getUHatU3Main(alpha1, alpha2, alpha3)
gradu=B*u
E_NL = E_NonLinear(gradu)*B
E_NL
Out[12]:
In [18]:
%aimport geom_util
mu = Symbol('mu')
la = Symbol('lambda')
C_tensor = getIsotropicStiffnessTensor(mu, la)
C = convertStiffnessTensorToMatrix(C_tensor)
C
Out[18]:
In [19]:
S=StrainL.T*C*StrainL*H1*H2*H3
S
Out[19]:
In [24]:
rho=Symbol('rho')
B_h=zeros(3,12)
B_h[0,0]=1
B_h[1,4]=1
B_h[2,8]=1
M=simplify(rho*P.T*B_h.T*G_up*B_h*P)
print(M)
In [21]:
stiffness_matrix_func = lambdify([A, K, mu, la, alpha3], S, "numpy")
%store stiffness_matrix_func
mass_matrix_func = lambdify([rho], M, "numpy")
%store mass_matrix_func
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