class KinematicAnalysis(DynamicAnalysis):
"""
classdocs
"""
def __init__(self, MBD_system, parent=None):
"""
Constructor
"""
super(KinematicAnalysis, self).__init__(MBD_system, parent)
# parent
self._parent = parent
# DAE fun object
self.DAE_fun = DAEfun(self._MBD_system, parent=self)
def solve(self):
"""
Solves a kinematic system
"""
print "solve()"
# pprint(vars(self))
# start solve
self.start_solve()
self.simulation_id = 0
self.FLAG = 1
# scipy.optimize.newton()
t = 0.
h = self._MBD_system.Hmax
self.t_n = self.MBD_system.t_n
# initial approximation
q = self.q_sol = self.MBD_system.evaluate_q0()
# use positions vector (translational and rotational)
q = q[0:len(q) / 2]
# us
# solution containers
self._solution_containers()
# size of C, C_q, C_t vectors, matrix
self.DAE_fun._C_q_size()
# options to track data
self.q_sol_matrix = np.array([q])
self._energy_t = 0
self._energy_delta = np.nan
# check before run
if not self.DAE_fun.check_C(q, t):
self.FLAG = 1
else:
print "Error found before simulation run!"
self.FLAG = 0
self.finished_solve()
# kinematic analysis
while self.FLAG == 1:
# print "----------------------------------------"
# print "t =", t
if self.stopped:
# self.update_GL_(t=t, q=w)
self.stop_solve()
self.FLAG = 0
if t >= self.t_n:
self.FLAG = 0
self.finished = True
print "finished!"
# self.update_GL_(t=t, q=w)
if self.finished or self.failed:
self.finished_solve()
# evaluate C vector
# C = self.DAE_fun.evaluate_C(q, t)
# print "C =", len(C)
# print C
# C_q = self.DAE_fun.evaluate_C_q(q)
# print "C_q ="
# print C_q
# print "q (in) =", q
q = scipy.optimize.fsolve(self.DAE_fun.evaluate_C,
q,
args=(t),
fprime=self.DAE_fun.evaluate_C_q,
xtol=self.MBD_system.TOL_dq_i,
maxfev=50)
# q = scipy.optimize.newton(self.DAE_fun.evaluate_C, q, fprime=self.DAE_fun.evaluate_C_q, tol=self.MBD_system.TOL_dq_i, maxiter=50)
# print "q (out) =", q
# evaluate C_q matrix
C_q = self.DAE_fun.evaluate_C_q(q, t)
# print "C_q ="
# print C_q
# evaluate C_t vector
C_t = self.DAE_fun.evaluate_C_t(q, t)
# print "C_t =", C_t
# solve linear system of equations for dq
dq = np.linalg.solve(C_q, -C_t)
# assemble new q vector q=[q, dq]
_q = np.concatenate([q, dq])
self.q_sol = _q
# evaluate vector Q_d
Q_d = self.DAE_fun.evaluate_Q_d(_q)
# solve linear system of equations for ddq
ddq = np.linalg.solve(C_q, Q_d)
# track data
self.R = np.nan
self._track_data(h, t, q)
self._info(t, q)
# increase time step
t = t + h
self.t = t
# integration finished - end time reached successfully
if self.finished or self.failed:
self.finished_solve()
# save data to file
self.write_simulation_solution_to_file()
