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_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