def compute_ext_state_visco(self, PD_deck, PD_problem, y, t_n):
e_visco = np.zeros(
(int(
PD_deck.Num_Nodes), int(
PD_deck.Num_Nodes), len(
self.Relaxation_Time)))
for x_i in range(0, len(PD_problem.x)):
index_x_family = PD_problem.get_index_x_family(PD_problem.x, x_i)
for x_p in index_x_family:
for k in range(1, len(self.Relaxation_Time)):
temp_exp = np.exp((- PD_deck.Delta_t) /
(self.Relaxation_Time[k]))
delta_e = self.e[x_i, x_p] - \
PD_problem.ext[x_i, x_p, t_n - 1]
beta = 1 - \
(self.Relaxation_Time[k] * (1 - temp_exp)) / PD_deck.Delta_t
e_visco[x_i,
x_p,
k] = PD_problem.ext[x_i,
x_p,
t_n - 1] * (1 - temp_exp) + PD_problem.ext_visco[x_i,
x_p,
k,
t_n - 1] * temp_exp + beta * delta_e
self.e_visco = e_visco
def compute_Ts(self, PD_deck, PD_problem):
Ts = np.zeros((int(PD_deck.Num_Nodes)))
for x_i in range(0, self.len_x):
index_x_family = PD_problem.get_index_x_family(PD_problem.x, x_i)
for x_p in index_x_family:
Ts[x_i] = Ts[x_i] + self.T[x_i, x_p] - self.T[x_p, x_i]
Ts[x_i] = Ts[x_i] * PD_deck.Volume
self.Ts = Ts
def compute_t_visco(self, PD_deck, PD_problem, y):
w = PD_deck.Influence_Function
M = PD_problem.compute_m(PD_deck.Num_Nodes, y)
t_visco = np.zeros((int(PD_deck.Num_Nodes), int(PD_deck.Num_Nodes)))
for x_i in range(0, len(PD_problem.x)):
index_x_family = PD_problem.get_index_x_family(PD_problem.x, x_i)
for x_p in index_x_family:
for k in range(1, len(self.Relaxation_Time)):
t_visco[x_i,
x_p] = t_visco[x_i,
x_p] + (w / PD_problem.weighted_function(PD_deck,
PD_problem.x,
x_i)) * self.Modulus[k] * (self.e[x_i,
x_p] - self.e_visco[x_i,
x_p,
k])
self.t_visco = t_visco
def compute_ext_state(self, PD_deck, PD_problem, y):
e = np.zeros((int(PD_deck.Num_Nodes), int(PD_deck.Num_Nodes)))
for x_i in range(0, len(PD_problem.x)):
index_x_family = PD_problem.get_index_x_family(PD_problem.x, x_i)
# print index_x_family
for x_p in index_x_family:
e[x_i, x_p] = np.absolute(
y[x_p] - y[x_i]) - np.absolute(PD_problem.x[x_p] - PD_problem.x[x_i])
self.e = e
def compute_T(self, PD_deck, PD_problem, y):
w = PD_deck.Influence_Function
M = PD_problem.compute_m(PD_deck.Num_Nodes, y)
tscal = np.zeros((int(PD_deck.Num_Nodes), int(PD_deck.Num_Nodes)))
for x_i in range(0, self.len_x):
index_x_family = PD_problem.get_index_x_family(PD_problem.x, x_i)
for x_p in index_x_family:
tscal[x_i,
x_p] = (w / PD_problem.weighted_function(PD_deck,
PD_problem.x,
x_i)) * self.Modulus[0] * self.e[x_i,
x_p] + self.t_visco[x_i,
x_p]
self.tscal = tscal
T = np.zeros((int(PD_deck.Num_Nodes), int(PD_deck.Num_Nodes)))
for x_i in range(0, self.len_x):
index_x_family = PD_problem.get_index_x_family(PD_problem.x, x_i)
for x_p in index_x_family:
T[x_i, x_p] = tscal[x_i, x_p] * M[x_i, x_p]
self.T = T
