def __call__(self, rk=None):
self.niter += 1
if self._disp:
print('iter %3i\trk = %s' % (self.niter, str(rk)))
Lub_Mob = np.zeros((6,6))
for p in range(6):
#print(p)
force_torque *= 0.0
force_torque[p] = 1.0
#print force_torque
# Set right hand side
RHS = np.reshape(np.concatenate([slip, -force_torque]), (System_size))
# Solve preconditioned linear system # callback=make_callback()
counter = gmres_counter()
(sol_precond, info_precond) = utils.gmres(A, RHS, tol=read.solver_tolerance, M=PC, maxiter=1000, restart=1000, callback=counter)
# Extract velocities and constraint forces on blobs
velocity = sol_precond[3*Nblobs: 3*Nblobs + 6*num_bodies]
lambda_blobs = sol_precond[0: 3*Nblobs]
Lub_Mob[:,p] = velocity
Res_Mob = np.linalg.pinv(Lub_Mob)
mob_coefs = np.array([height, Lub_Mob[2,2], Lub_Mob[0,0], Lub_Mob[4,0], Lub_Mob[5,5], Lub_Mob[3,3]])
res_coefs = np.array([height, Res_Mob[2,2], Res_Mob[0,0], Res_Mob[4,0], Res_Mob[5,5], Res_Mob[3,3]])
mob_coef[d_count,:] = mob_coefs
res_coef[d_count,:] = res_coefs
#print(mob_coefs)
#print(res_coefs)
def compute_stochastic_velocity(self, dt):
'''
Compute stochastic torque and velocity. First,
solve for the torque
M_rr * T = -kT*div_t(M_rt) - sqrt(2*kT) * (N^{1/2}*W)_r,
then set linear velocity
v_stoch = M_tr * T + sqrt(2*kT) * (N^{1/2}*W)_t + kT*div_t(M_tt).
Here N = (M_tt M_tr; M_rt M_rr) is the grand mobility matrix.
We use random finite difference to compute the divergence
terms. Note that in principle we should include the term
div_r(M_rr) in the torque equation and div_r(M_tr) in the
velocity equation but they are zero for a roller.
This function returns the stochastic velocity v_stoch.
'''
# Create auxiliar variables
Nblobs = len(self.bodies)
blob_mass = 1.0
# Get blobs coordinates
r_vectors_blobs = np.empty((Nblobs, 3))
for k, b in enumerate(self.bodies):
r_vectors_blobs[k] = b.location
# Generate random vector
z = np.random.randn(6 * Nblobs)
# Define grand mobility matrix
def grand_mobility_matrix(force_torque, r_vectors = None, eta = None, a = None, periodic_length = None):
half_size = force_torque.size / 2
# velocity = self.mobility_trans_times_force_torque(r_vectors, force_torque[0:half_size], force_torque[half_size:], eta, a, periodic_length = periodic_length)
velocity = self.mobility_trans_times_force(r_vectors, force_torque[0:half_size], eta, a, periodic_length = periodic_length)
velocity += self.mobility_trans_times_torque(r_vectors, force_torque[half_size:], eta, a, periodic_length = periodic_length)
angular_velocity = self.mobility_rot_times_force(r_vectors, force_torque[0:half_size], eta, a, periodic_length = periodic_length)
angular_velocity += self.mobility_rot_times_torque(r_vectors, force_torque[half_size:], eta, a, periodic_length = periodic_length)
return np.concatenate([velocity, angular_velocity])
partial_grand_mobility_matrix = partial(grand_mobility_matrix,
r_vectors = r_vectors_blobs,
eta = self.eta,
a = self.a,
periodic_length = self.periodic_length)
# Generate noise term sqrt(2*kT) * N^{1/2} * z
velocities_noise, it_lanczos = stochastic.stochastic_forcing_lanczos(factor = np.sqrt(2*self.kT / dt),
tolerance = self.tolerance,
dim = self.Nblobs * 6,
mobility_mult = partial_grand_mobility_matrix,
z = z,
print_residual = self.print_residual)
self.stoch_iterations_count += it_lanczos
# Compute divergence terms div_t(M_rt) and div_t(M_tt)
if self.kT > 0.0 and self.domain != 'no_wall':
# 1. Generate random displacement
dx_stoch = np.reshape(np.random.randn(Nblobs * 3), (Nblobs, 3))
# 2. Displace blobs
r_vectors_blobs += dx_stoch * (self.rf_delta * self.a * 0.5)
# 3. Compute M_rt(r+0.5*dx) * dx_stoch
div_M_rt = self.mobility_rot_times_force(r_vectors_blobs, np.reshape(dx_stoch, dx_stoch.size), self.eta, self.a,
periodic_length = self.periodic_length)
div_M_tt = self.mobility_trans_times_force(r_vectors_blobs, np.reshape(dx_stoch, dx_stoch.size), self.eta, self.a,
periodic_length = self.periodic_length)
# 4. Displace blobs in the other direction
r_vectors_blobs -= dx_stoch * self.rf_delta * self.a
# 5. Compute -M_rt(r-0.5*dx) * dx_stoch
div_M_rt -= self.mobility_rot_times_force(r_vectors_blobs, np.reshape(dx_stoch, dx_stoch.size), self.eta, self.a,
periodic_length = self.periodic_length)
div_M_tt -= self.mobility_trans_times_force(r_vectors_blobs, np.reshape(dx_stoch, dx_stoch.size), self.eta, self.a,
periodic_length = self.periodic_length)
# Reset blobs location
r_vectors_blobs += dx_stoch * (self.rf_delta * self.a * 0.5)
else:
div_M_rt = np.zeros(Nblobs * 3)
div_M_tt = np.zeros(Nblobs * 3)
# Use constraint motion or free kinematics
if self.free_kinematics == 'False':
# Set RHS = -kT*div_t(M_rt) - sqrt(2*kT) * (N^{1/2}*W)_r,
RHS = -velocities_noise[velocities_noise.size / 2:] - div_M_rt * (self.kT / (self.rf_delta * self.a))
# Set linear operator
system_size = 3 * len(self.bodies)
def mobility_rot_torque(torque, r_vectors = None, eta = None, a = None, periodic_length = None):
return self.mobility_rot_times_torque(r_vectors, torque, eta, a, periodic_length = periodic_length)
linear_operator_partial = partial(mobility_rot_torque, r_vectors = r_vectors_blobs, eta = self.eta, a = self.a, periodic_length = self.periodic_length)
A = spla.LinearOperator((system_size, system_size), matvec = linear_operator_partial, dtype='float64')
# Scale RHS to norm 1
RHS_norm = np.linalg.norm(RHS)
if RHS_norm > 0:
RHS = RHS / RHS_norm
# Solve linear system
counter = gmres_counter(print_residual = self.print_residual)
(sol_precond, info_precond) = utils.gmres(A,
RHS,
tol=self.tolerance,
maxiter=1000,
callback=counter)
self.det_iterations_count += counter.niter
# Scale solution with RHS norm
if RHS_norm > 0:
sol_precond = sol_precond * RHS_norm
else:
# This is free kinematics, stochastic torque set to zero
# because we only care for the translational degrees of freedom
sol_precond = np.zeros(3 * Nblobs)
# Compute stochastic velocity v_stoch = M_tr * T + sqrt(2*kT) * (N^{1/2}*W)_t + kT*div_t(M_tt).
v_stoch = self.mobility_trans_times_torque(r_vectors_blobs, sol_precond, self.eta, self.a, periodic_length = self.periodic_length)
v_stoch += velocities_noise[0 : velocities_noise.size / 2] + (self.kT / (self.rf_delta * self.a)) * div_M_tt
return v_stoch
def compute_deterministic_velocity_and_torque(self):
'''
Compute the torque on bodies rotating with a prescribed
angular velocity and subject to forces, i.e., solve the
linear system
M_rr * T = omega - M_rt * forces
Then compute the translational velocity
v = M_tr * T + M_tt * forces
It returns the velocities and torques (v,T).
'''
# Create auxiliar variables
Nblobs = len(self.bodies)
blob_mass = 1.0
# Get blobs coordinates
r_vectors_blobs = np.empty((Nblobs, 3))
for k, b in enumerate(self.bodies):
r_vectors_blobs[k] = b.location
# Compute one-blob forces (same function for all blobs)
# force = np.zeros(r_vectors_blobs.size)
force = self.calc_one_blob_forces(r_vectors_blobs, blob_radius = self.a, blob_mass = blob_mass)
# Compute blob-blob forces (same function for all pair of blobs)
force += self.calc_blob_blob_forces(r_vectors_blobs, blob_radius = self.a)
force = np.reshape(force, force.size)
# Use constraint motion or free kinematics
if self.free_kinematics == 'False':
# Set rollers angular velocity
omega = np.empty(3 * len(self.bodies))
for i in range(len(self.bodies)):
omega[3*i : 3*(i+1)] = self.get_omega_one_roller()
# Set RHS = omega - M_rt * force
RHS = omega - self.mobility_rot_times_force(r_vectors_blobs, force, self.eta, self.a, periodic_length = self.periodic_length)
# Set linear operator
system_size = 3 * len(self.bodies)
def mobility_rot_torque(torque, r_vectors = None, eta = None, a = None, periodic_length = None):
return self.mobility_rot_times_torque(r_vectors, torque, eta, a, periodic_length = periodic_length)
linear_operator_partial = partial(mobility_rot_torque, r_vectors = r_vectors_blobs, eta = self.eta, a = self.a, periodic_length = self.periodic_length)
A = spla.LinearOperator((system_size, system_size), matvec = linear_operator_partial, dtype='float64')
# Scale RHS to norm 1
RHS_norm = np.linalg.norm(RHS)
if RHS_norm > 0:
RHS = RHS / RHS_norm
# Solve linear system
counter = gmres_counter(print_residual = self.print_residual)
(sol_precond, info_precond) = utils.gmres(A,
RHS,
x0=self.deterministic_torque_previous_step,
tol=self.tolerance,
maxiter=1000,
callback = counter)
self.det_iterations_count += counter.niter
self.deterministic_torque_previous_step = sol_precond
# Scale solution with RHS norm
if RHS_norm > 0:
sol_precond = sol_precond * RHS_norm
else:
# This is free kinematics, compute torque
sol_precond = self.get_torque()
# Compute linear velocity
velocity = self.mobility_trans_times_force(r_vectors_blobs, force, self.eta, self.a, periodic_length = self.periodic_length)
if np.any(sol_precond):
velocity += self.mobility_trans_times_torque(r_vectors_blobs, sol_precond, self.eta, self.a, periodic_length = self.periodic_length)
# Return linear velocity and torque
return velocity, sol_precond
read.blob_radius,
M_inv=M_inv)
mobility_bodies[k] = N
# 4. Pack preconditioner
PC_partial = partial(block_diagonal_preconditioner, bodies=bodies, mobility_bodies=mobility_bodies, \
mobility_inv_blobs=mobility_inv_blobs, Nblobs=Nblobs)
PC = spla.LinearOperator((System_size, System_size),
matvec=PC_partial,
dtype='float64')
# Solve preconditioned linear system # callback=make_callback()
(sol_precond,
info_precond) = utils.gmres(A,
RHS,
tol=read.solver_tolerance,
M=PC,
maxiter=1000,
restart=60)
# Extract velocities and constraint forces on blobs
velocity = np.reshape(
sol_precond[3 * Nblobs:3 * Nblobs + 6 * num_bodies],
(num_bodies, 6))
lambda_blobs = np.reshape(sol_precond[0:3 * Nblobs], (Nblobs, 3))
if d_count == 0:
f = open(
"tetra_particle_2_velocities_12_dist_" + str(dist) +
".dat", "w")
np.savetxt(f, np.array([height]), newline=" ")
f = open(
for k, b in enumerate(bodies):
# 1. Compute blobs mobility and invert it
M = b.calc_mobility_blobs(read.eta, read.blob_radius)
M_inv = np.linalg.inv(M)
mobility_inv_blobs.append(M_inv)
# 2. Compute body mobility
N = b.calc_mobility_body(read.eta, read.blob_radius, M_inv = M_inv)
mobility_bodies[k] = N
# 4. Pack preconditioner
PC_partial = partial(multi_bodies.block_diagonal_preconditioner, bodies=bodies, mobility_bodies=mobility_bodies, \
mobility_inv_blobs=mobility_inv_blobs, Nblobs=Nblobs)
PC = spla.LinearOperator((System_size, System_size), matvec = PC_partial, dtype='float64')
# Solve preconditioned linear system # callback=make_callback()
(sol_precond, info_precond) = utils.gmres(A, RHS, tol=read.solver_tolerance, M=PC, maxiter=1000, restart=60)
# Extract velocities and constraint forces on blobs
velocity = np.reshape(sol_precond[3*Nblobs: 3*Nblobs + 6*num_bodies], (num_bodies, 6))
lambda_blobs = np.reshape(sol_precond[0: 3*Nblobs], (Nblobs, 3))
# Save velocity
name = read.output_name + '.velocity.dat'
np.savetxt(name, velocity, delimiter=' ')
print('Time to solve mobility problem =', time.time() - start_time )
# Plot velocity field
if read.plot_velocity_field.size > 1:
print('plot_velocity_field')
plot_velocity_field(read.plot_velocity_field, r_vectors_blobs, lambda_blobs, read.blob_radius, read.eta, read.output_name, read.tracer_radius,
mobility_vector_prod_implementation = read.mobility_vector_prod_implementation)
def solve_mobility_problem(self,
RHS=None,
noise=None,
noise_FT=None,
AB=None,
x0=None,
save_first_guess=False,
PC_partial=None,
*args,
**kwargs):
'''
Solve the mobility problem using preconditioned GMRES. Compute
velocities on the bodies subject to active slip and enternal
forces-torques.
The linear and angular velocities are sorted like
velocities = (v_1, w_1, v_2, w_2, ...)
where v_i and w_i are the linear and angular velocities of body i.
'''
while True:
System_size = self.Nblobs * 3 + len(self.bodies) * 3
# Get blobs coordinates
r_vectors_blobs = self.get_blobs_r_vectors(self.bodies,
self.Nblobs)
# If RHS = None set RHS = [slip, -force_torque]
if RHS is None:
# Calculate slip on blobs
if self.calc_slip is not None:
slip = self.calc_slip(self.bodies, self.Nblobs)
else:
slip = np.zeros((self.Nblobs, 3))
# Calculate force-torque on bodies
force_torque = np.zeros(
(self.Nblobs, 3)
) ######self.force_torque_calculator(self.bodies, r_vectors_blobs)
# Add noise to the force/torque
if noise_FT is not None:
force_torque += noise_FT
# Set right hand side
RHS = np.reshape(np.concatenate([slip, -force_torque]),
(System_size))
# Add noise to the slip
if noise is not None:
RHS[0:System_size] -= noise
# Calculate K matrix
K = self.calc_K_matrix_bodies(self.bodies, self.Nblobs)
# Set linear operators
linear_operator_partial = partial(
self.linear_operator,
bodies=self.bodies,
r_vectors=r_vectors_blobs,
eta=self.eta,
a=self.a,
K_bodies=K,
periodic_length=self.periodic_length)
A = spla.LinearOperator((System_size, System_size),
matvec=linear_operator_partial,
dtype='float64')
# e_i = np.identity(System_size)
# for k in range(System_size):
# print A.matvec(e_i[:,k])
# Set preconditioner
if PC_partial is None:
PC_partial = self.build_block_diagonal_preconditioner(
self.bodies, r_vectors_blobs, self.Nblobs, self.eta,
self.a, *args, **kwargs)
PC = spla.LinearOperator((System_size, System_size),
matvec=PC_partial,
dtype='float64')
# Scale RHS to norm 1
RHS_norm = np.linalg.norm(RHS)
if RHS_norm > 0:
RHS = RHS / RHS_norm
# Solve preconditioned linear system
counter = gmres_counter(print_residual=self.print_residual)
(sol_precond, info_precond) = utils.gmres(A,
RHS,
x0=x0,
tol=self.tolerance,
M=PC,
maxiter=1000,
restart=60,
callback=counter)
self.det_iterations_count += counter.niter
if save_first_guess:
self.first_guess = sol_precond
# Scale solution with RHS norm
if RHS_norm > 0:
sol_precond = sol_precond * RHS_norm
else:
sol_precond[:] = 0.0
# Return solution
return sol_precond
