def setUp(self):
super().setUp()
domain, geom = mesh.rectilinear([numpy.linspace(0,1,9)] * 2)
gauss = domain.sample('gauss', 5)
uin = geom[1] * (1-geom[1])
dx = function.J(geom, 2)
ubasis = domain.basis('std', degree=2)
pbasis = domain.basis('std', degree=1)
if self.single:
ubasis, pbasis = function.chain([ubasis.vector(2), pbasis])
dofs = function.Argument('dofs', [len(ubasis)])
u = ubasis.dot(dofs)
p = pbasis.dot(dofs)
dofs = 'dofs'
ures = gauss.integral((self.viscosity * (ubasis.grad(geom) * (u.grad(geom) + u.grad(geom).T)).sum([-1,-2]) - ubasis.div(geom) * p) * dx)
dres = gauss.integral((ubasis * (u.grad(geom) * u).sum(-1)).sum(-1) * dx)
else:
u = (ubasis[:,numpy.newaxis] * function.Argument('dofs', [len(ubasis), 2])).sum(0)
p = (pbasis * function.Argument('pdofs', [len(pbasis)])).sum(0)
dofs = 'dofs', 'pdofs'
ures = gauss.integral((self.viscosity * (ubasis[:,numpy.newaxis].grad(geom) * (u.grad(geom) + u.grad(geom).T)).sum(-1) - ubasis.grad(geom) * p) * dx)
dres = gauss.integral(ubasis[:,numpy.newaxis] * (u.grad(geom) * u).sum(-1) * dx)
pres = gauss.integral((pbasis * u.div(geom)) * dx)
cons = solver.optimize('dofs', domain.boundary['top,bottom'].integral((u**2).sum(), degree=4), droptol=1e-10)
cons = solver.optimize('dofs', domain.boundary['left'].integral((u[0]-uin)**2 + u[1]**2, degree=4), droptol=1e-10, constrain=cons)
self.cons = cons if self.single else {'dofs': cons}
stokes = solver.solve_linear(dofs, residual=ures + pres if self.single else [ures, pres], constrain=self.cons)
self.arguments = dict(dofs=stokes) if self.single else stokes
self.residual = ures + dres + pres if self.single else [ures + dres, pres]
inertia = gauss.integral(.5 * (u**2).sum(-1) * dx).derivative('dofs')
self.inertia = inertia if self.single else [inertia, None]
self.tol = 1e-10
self.dofs = dofs
self.frozen = types.frozenarray if self.single else solver.argdict
def main(nelems:int, etype:str, btype:str, degree:int, poisson:float, angle:float, restol:float, trim:bool):
'''
Deformed hyperelastic plate.
.. arguments::
nelems [10]
Number of elements along edge.
etype [square]
Type of elements (square/triangle/mixed).
btype [std]
Type of basis function (std/spline).
degree [1]
Polynomial degree.
poisson [.25]
Poisson's ratio, nonnegative and stricly smaller than 1/2.
angle [20]
Rotation angle for right clamp (degrees).
restol [1e-10]
Newton tolerance.
trim [no]
Create circular-shaped hole.
'''
domain, geom = mesh.unitsquare(nelems, etype)
if trim:
domain = domain.trim(function.norm2(geom-.5)-.2, maxrefine=2)
bezier = domain.sample('bezier', 5)
ns = function.Namespace(fallback_length=domain.ndims)
ns.x = geom
ns.angle = angle * numpy.pi / 180
ns.lmbda = 2 * poisson
ns.mu = 1 - 2 * poisson
ns.ubasis = domain.basis(btype, degree=degree)
ns.u_i = 'ubasis_k ?lhs_ki'
ns.X_i = 'x_i + u_i'
ns.strain_ij = '.5 (d(u_i, x_j) + d(u_j, x_i))'
ns.energy = 'lmbda strain_ii strain_jj + 2 mu strain_ij strain_ij'
sqr = domain.boundary['left'].integral('u_k u_k J(x)' @ ns, degree=degree*2)
sqr += domain.boundary['right'].integral('((u_0 - x_1 sin(2 angle) - cos(angle) + 1)^2 + (u_1 - x_1 (cos(2 angle) - 1) + sin(angle))^2) J(x)' @ ns, degree=degree*2)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
energy = domain.integral('energy J(x)' @ ns, degree=degree*2)
lhs0 = solver.optimize('lhs', energy, constrain=cons)
X, energy = bezier.eval(['X', 'energy'] @ ns, lhs=lhs0)
export.triplot('linear.png', X, energy, tri=bezier.tri, hull=bezier.hull)
ns.strain_ij = '.5 (d(u_i, x_j) + d(u_j, x_i) + d(u_k, x_i) d(u_k, x_j))'
ns.energy = 'lmbda strain_ii strain_jj + 2 mu strain_ij strain_ij'
energy = domain.integral('energy J(x)' @ ns, degree=degree*2)
lhs1 = solver.minimize('lhs', energy, arguments=dict(lhs=lhs0), constrain=cons).solve(restol)
X, energy = bezier.eval(['X', 'energy'] @ ns, lhs=lhs1)
export.triplot('nonlinear.png', X, energy, tri=bezier.tri, hull=bezier.hull)
return lhs0, lhs1
def test_unknowntarget(self):
err = self.domain.integral('(sqrt(1 - u) - .5)^2' @ self.ns, degree=2)
with self.assertRaises(ValueError):
dofs = solver.optimize(['dofs', 'other'], err, tol=1e-10)
dofs = solver.optimize(['dofs', 'other'], err, tol=1e-10, droptol=1e-10)
self.assertEqual(list(dofs), ['dofs'])
dofs = solver.optimize(['other'], err, tol=1e-10, droptol=1e-10)
self.assertEqual(dofs, {})
with self.assertRaises(KeyError):
dofs = solver.optimize('other', err, tol=1e-10, droptol=1e-10)
def main(nelems: int, etype: str, degree: int, reynolds: float):
'''
Driven cavity benchmark problem.
.. arguments::
nelems [12]
Number of elements along edge.
etype [square]
Element type (square/triangle/mixed).
degree [2]
Polynomial degree for velocity; the pressure space is one degree less.
reynolds [1000]
Reynolds number, taking the domain size as characteristic length.
'''
domain, geom = mesh.unitsquare(nelems, etype)
ns = function.Namespace()
ns.Re = reynolds
ns.x = geom
ns.ubasis, ns.pbasis = function.chain([
domain.basis('std', degree=degree).vector(2),
domain.basis('std', degree=degree - 1),
])
ns.u_i = 'ubasis_ni ?lhs_n'
ns.p = 'pbasis_n ?lhs_n'
ns.stress_ij = '(u_i,j + u_j,i) / Re - p δ_ij'
sqr = domain.boundary.integral('u_k u_k d:x' @ ns, degree=degree * 2)
wallcons = solver.optimize('lhs', sqr, droptol=1e-15)
sqr = domain.boundary['top'].integral('(u_0 - 1)^2 d:x' @ ns,
degree=degree * 2)
lidcons = solver.optimize('lhs', sqr, droptol=1e-15)
cons = numpy.choose(numpy.isnan(lidcons), [lidcons, wallcons])
cons[-1] = 0 # pressure point constraint
res = domain.integral('(ubasis_ni,j stress_ij + pbasis_n u_k,k) d:x' @ ns,
degree=degree * 2)
with treelog.context('stokes'):
lhs0 = solver.solve_linear('lhs', res, constrain=cons)
postprocess(domain, ns, lhs=lhs0)
res += domain.integral(
'.5 (ubasis_ni u_i,j - ubasis_ni,j u_i) u_j d:x' @ ns,
degree=degree * 3)
with treelog.context('navierstokes'):
lhs1 = solver.newton('lhs', res, lhs0=lhs0,
constrain=cons).solve(tol=1e-10)
postprocess(domain, ns, lhs=lhs1)
return lhs0, lhs1
def main(nelems:int, etype:str, degree:int, reynolds:float):
'''
Driven cavity benchmark problem.
.. arguments::
nelems [12]
Number of elements along edge.
etype [square]
Element type (square/triangle/mixed).
degree [2]
Polynomial degree for velocity; the pressure space is one degree less.
reynolds [1000]
Reynolds number, taking the domain size as characteristic length.
'''
domain, geom = mesh.unitsquare(nelems, etype)
ns = function.Namespace()
ns.Re = reynolds
ns.x = geom
ns.ubasis = domain.basis('std', degree=degree).vector(domain.ndims)
ns.pbasis = domain.basis('std', degree=degree-1)
ns.u_i = 'ubasis_ni ?u_n'
ns.p = 'pbasis_n ?p_n'
ns.stress_ij = '(d(u_i, x_j) + d(u_j, x_i)) / Re - p δ_ij'
usqr = domain.boundary.integral('u_k u_k d:x' @ ns, degree=degree*2)
wallcons = solver.optimize('u', usqr, droptol=1e-15)
usqr = domain.boundary['top'].integral('(u_0 - 1)^2 d:x' @ ns, degree=degree*2)
lidcons = solver.optimize('u', usqr, droptol=1e-15)
ucons = numpy.choose(numpy.isnan(lidcons), [lidcons, wallcons])
pcons = numpy.zeros(len(ns.pbasis), dtype=bool)
pcons[-1] = True # constrain pressure to zero in a point
cons = dict(u=ucons, p=pcons)
ures = domain.integral('d(ubasis_ni, x_j) stress_ij d:x' @ ns, degree=degree*2)
pres = domain.integral('pbasis_n d(u_k, x_k) d:x' @ ns, degree=degree*2)
with treelog.context('stokes'):
state0 = solver.solve_linear(('u', 'p'), (ures, pres), constrain=cons)
postprocess(domain, ns, **state0)
ures += domain.integral('.5 (ubasis_ni d(u_i, x_j) - d(ubasis_ni, x_j) u_i) u_j d:x' @ ns, degree=degree*3)
with treelog.context('navierstokes'):
state1 = solver.newton(('u', 'p'), (ures, pres), arguments=state0, constrain=cons).solve(tol=1e-10)
postprocess(domain, ns, **state1)
return state0, state1
def main(viscosity=1.3e-3, density=1e3, pout=223e5, uw=-0.01, nelems=10):
# viscosity = 1.3e-3
# density = 1e3
# pout = 223e5
# nelems = 10
# uw = -0.01
domain, geom = mesh.rectilinear([numpy.linspace(0, 1, nelems), numpy.linspace(1, 2, nelems), [0, 2 * numpy.pi]],
periodic=[2])
domain = domain.withboundary(inner='bottom', outer='top')
ns = function.Namespace()
ns.y, ns.r, ns.θ = geom
ns.x_i = '<r cos(θ), y, r sin(θ)>_i'
ns.uybasis, ns.urbasis, ns.pbasis = function.chain([
domain.basis('std', degree=3, removedofs=((0,-1), None, None)), # remove normal component at y=0 and y=1
domain.basis('std', degree=3, removedofs=((0,-1), None, None)), # remove tangential component at y=0 (no slip)
domain.basis('std', degree=2)])
ns.ubasis_ni = '<urbasis_n cos(θ), uybasis_n, urbasis_n sin(θ)>_i'
ns.viscosity = viscosity
ns.density = density
ns.u_i = 'ubasis_ni ?lhs_n'
ns.p = 'pbasis_n ?lhs_n'
ns.sigma_ij = 'viscosity (u_i,j + u_j,i) - p δ_ij'
ns.pout = pout
ns.uw = uw
ns.uw_i = 'uw <cos(phi), 0, sin(phi)>_i'
ns.tout_i = '-pout n_i'
ns.uw_i = 'uw n_i' # uniform outflow
res = domain.integral('(viscosity ubasis_ni,j u_i,j - p ubasis_ni,i + pbasis_n u_k,k) d:x' @ ns, degree=6)
# res -= domain[1].boundary['inner'].integral('ubasis_ni tout_i d:x' @ ns, degree=6)
sqr = domain.boundary['inner'].integral('(u_i - uw_i) (u_i - uw_i) d:x' @ ns, degree=6)
# sqr = domain.boundary['outer'].integral('(u_i - uin_i) (u_i - uin_i) d:x' @ ns, degree=6)
sqr -= domain.boundary['outer'].integral('(p - pout) (p - pout) d:x' @ ns, degree=6)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
lhs = solver.solve_linear('lhs', res, constrain=cons)
plottopo = domain[:, :, 0:].boundary['back']
bezier = plottopo.sample('bezier', 10)
r, y, p, u = bezier.eval([ns.r, ns.y, ns.p, function.norm2(ns.u)], lhs=lhs)
with export.mplfigure('pressure.png', dpi=800) as fig:
ax = fig.add_subplot(111, title='pressure', aspect=1)
ax.autoscale(enable=True, axis='both', tight=True)
im = ax.tripcolor(r, y, bezier.tri, p, shading='gouraud', cmap='jet')
ax.add_collection(
collections.LineCollection(numpy.array([y, r]).T[bezier.hull], colors='k', linewidths=0.2, alpha=0.2))
fig.colorbar(im)
uniform = plottopo.sample('uniform', 1)
r_, y_, uv = uniform.eval([ns.r, ns.y, ns.u], lhs=lhs)
with export.mplfigure('velocity.png', dpi=800) as fig:
ax = fig.add_subplot(111, title='Velocity', aspect=1)
ax.autoscale(enable=True, axis='both', tight=True)
im = ax.tripcolor(r, y, bezier.tri, u, shading='gouraud', cmap='jet')
ax.quiver(r_, y_, uv[:, 0], uv[:, 1], angles='xy', scale_units='xy')
fig.colorbar(im)
def setUp(self):
super().setUp()
domain, geom = mesh.rectilinear([numpy.linspace(0, 1, 9)] * 2)
ubasis = domain.basis('std', degree=2)
if self.vector:
u = ubasis.vector(2).dot(
function.Argument('dofs', [len(ubasis) * 2]))
else:
u = (ubasis[:, numpy.newaxis] *
function.Argument('dofs', [len(ubasis), 2])).sum(0)
Geom = geom * [1.1, 1] + u
self.cons = solver.optimize('dofs',
domain.boundary['left,right'].integral(
(u**2).sum(0), degree=4),
droptol=1e-15)
self.boolcons = ~numpy.isnan(self.cons)
strain = .5 * (function.outer(Geom.grad(geom), axis=1).sum(0) -
function.eye(2))
self.energy = domain.integral(
((strain**2).sum([0, 1]) + 20 *
(function.determinant(Geom.grad(geom)) - 1)**2) *
function.J(geom),
degree=6)
self.residual = self.energy.derivative('dofs')
self.tol = 1e-10
def postprocess(domain, ns, every=.05, spacing=.01, **arguments):
div = domain.integral('d(u_k, x_k)^2 J(x)' @ ns, degree=1).eval(**arguments)**.5
treelog.info('velocity divergence: {:.2e}'.format(div)) # confirm that velocity is pointwise divergence-free
ns = ns.copy_() # copy namespace so that we don't modify the calling argument
ns.streambasis = domain.basis('std', degree=2)[1:] # remove first dof to obtain non-singular system
ns.stream = 'streambasis_n ?streamdofs_n' # stream function
ns.ε = function.levicivita(2)
sqr = domain.integral('sum:i((u_i - ε_ij d(stream, x_j))^2) J(x)' @ ns, degree=4)
arguments['streamdofs'] = solver.optimize('streamdofs', sqr, arguments=arguments) # compute streamlines
bezier = domain.sample('bezier', 9)
x, u, p, stream = bezier.eval(['x', 'norm2(u)', 'p', 'stream'] @ ns, **arguments)
with export.mplfigure('flow.png') as fig: # plot velocity as field, pressure as contours, streamlines as dashed
ax = fig.add_axes([.1,.1,.8,.8], yticks=[], aspect='equal')
import matplotlib.collections
ax.add_collection(matplotlib.collections.LineCollection(x[bezier.hull], colors='w', linewidths=.5, alpha=.2))
ax.tricontour(x[:,0], x[:,1], bezier.tri, stream, 16, colors='k', linestyles='dotted', linewidths=.5, zorder=9)
caxu = fig.add_axes([.1,.1,.03,.8], title='velocity')
imu = ax.tripcolor(x[:,0], x[:,1], bezier.tri, u, shading='gouraud', cmap='jet')
fig.colorbar(imu, cax=caxu)
caxu.yaxis.set_ticks_position('left')
caxp = fig.add_axes([.87,.1,.03,.8], title='pressure')
imp = ax.tricontour(x[:,0], x[:,1], bezier.tri, p, 16, cmap='gray', linestyles='solid')
fig.colorbar(imp, cax=caxp)
def test_nonlinear(self):
err = self.domain.boundary['bottom'].integral(
'(u + .25 u^3 - 1.25)^2 d:geom' @ self.ns, degree=6)
cons = solver.optimize('dofs', err, droptol=1e-15, tol=1e-15)
numpy.testing.assert_almost_equal(
cons,
numpy.take([1, numpy.nan], [0, 1, 1, 0, 1, 1, 0, 1, 1]),
decimal=15)
def test_linear(self):
ns = self.ns
ns.u = 'ubasis_n ?dofs_n'
err = self.domain.boundary['bottom'].integral(ns.eval_('(u - 1)^2'), geometry=ns.geom, degree=2)
cons = solver.optimize('dofs', err, droptol=1e-15)
isnan = numpy.isnan(cons)
self.assertTrue(numpy.equal(isnan, [0,1,1,0,1,1,0,1,1]).all())
numpy.testing.assert_almost_equal(cons[~isnan], 1, decimal=15)
def test_nonlinear_multipleroots(self):
ns = self.ns
ns.u = 'ubasis_n ?dofs_n'
ns.gu = 'u + u^2'
err = self.domain.boundary['bottom'].integral(ns.eval_('(gu - .75)^2'), geometry=ns.geom, degree=2)
cons = solver.optimize('dofs', err, droptol=1e-15, lhs0=numpy.ones(len(ns.ubasis)), newtontol=1e-10)
isnan = numpy.isnan(cons)
self.assertTrue(numpy.equal(isnan, [0,1,1,0,1,1,0,1,1]).all())
numpy.testing.assert_almost_equal(cons[~isnan], .5, decimal=15)
def test_linear(self):
ns = self.ns
ns.u = 'ubasis_n ?dofs_n'
err = self.domain.boundary['bottom'].integral(ns.eval_('(u - 1)^2'),
geometry=ns.geom,
degree=2)
cons = solver.optimize('dofs', err, droptol=1e-15)
isnan = numpy.isnan(cons)
self.assertTrue(numpy.equal(isnan, [0, 1, 1, 0, 1, 1, 0, 1, 1]).all())
numpy.testing.assert_almost_equal(cons[~isnan], 1, decimal=15)
def test_nonlinear_multipleroots(self):
err = self.domain.boundary['bottom'].integral(
'(u + u^2 - .75)^2' @ self.ns, degree=2)
cons = solver.optimize('dofs',
err,
droptol=1e-15,
lhs0=numpy.ones(len(self.ns.ubasis)),
tol=1e-10)
numpy.testing.assert_almost_equal(
cons,
numpy.take([.5, numpy.nan], [0, 1, 1, 0, 1, 1, 0, 1, 1]),
decimal=15)
def postprocess(domain, ns, every=.05, spacing=.01, **arguments):
ns = ns.copy_(
) # copy namespace so that we don't modify the calling argument
ns.streambasis = domain.basis(
'std', degree=2)[1:] # remove first dof to obtain non-singular system
ns.stream = 'streambasis_n ?streamdofs_n' # stream function
sqr = domain.integral(
'((u_0 - stream_,1)^2 + (u_1 + stream_,0)^2) d:x' @ ns, degree=4)
arguments['streamdofs'] = solver.optimize(
'streamdofs', sqr, arguments=arguments) # compute streamlines
bezier = domain.sample('bezier', 9)
x, u, p, stream = bezier.eval(['x_i', 'sqrt(u_k u_k)', 'p', 'stream'] @ ns,
**arguments)
with export.mplfigure(
'flow.png'
) as fig: # plot velocity as field, pressure as contours, streamlines as dashed
ax = fig.add_axes([.1, .1, .8, .8], yticks=[], aspect='equal')
import matplotlib.collections
ax.add_collection(
matplotlib.collections.LineCollection(x[bezier.hull],
colors='w',
linewidths=.5,
alpha=.2))
ax.tricontour(x[:, 0],
x[:, 1],
bezier.tri,
stream,
16,
colors='k',
linestyles='dotted',
linewidths=.5,
zorder=9)
caxu = fig.add_axes([.1, .1, .03, .8], title='velocity')
imu = ax.tripcolor(x[:, 0],
x[:, 1],
bezier.tri,
u,
shading='gouraud',
cmap='jet')
fig.colorbar(imu, cax=caxu)
caxu.yaxis.set_ticks_position('left')
caxp = fig.add_axes([.87, .1, .03, .8], title='pressure')
imp = ax.tricontour(x[:, 0],
x[:, 1],
bezier.tri,
p,
16,
cmap='gray',
linestyles='solid')
fig.colorbar(imp, cax=caxp)
def main(
nelems: 'number of elements' = 12,
lmbda: 'first lamé constant' = 1.,
mu: 'second lamé constant' = 1.,
degree: 'polynomial degree' = 2,
withplots: 'create plots' = True,
solvetol: 'solver tolerance' = 1e-10,
):
# construct mesh
verts = numpy.linspace(0, 1, nelems + 1)
domain, geom = mesh.rectilinear([verts, verts])
# create namespace
ns = function.Namespace(default_geometry_name='x0')
ns.x0 = geom
ns.basis = domain.basis('spline', degree=degree).vector(2)
ns.u_i = 'basis_ni ?lhs_n'
ns.x_i = 'x0_i + u_i'
ns.lmbda = lmbda
ns.mu = mu
ns.strain_ij = '(u_i,j + u_j,i) / 2'
ns.stress_ij = 'lmbda strain_kk δ_ij + 2 mu strain_ij'
# construct dirichlet boundary constraints
sqr = domain.boundary['left'].integral('u_k u_k' @ ns,
geometry=ns.x0,
degree=2)
sqr += domain.boundary['right'].integral('(u_0 - .5)^2' @ ns,
geometry=ns.x0,
degree=2)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
# construct residual
res = domain.integral('basis_ni,j stress_ij' @ ns,
geometry=ns.x0,
degree=2)
# solve system and substitute the solution in the namespace
lhs = solver.solve_linear('lhs', res, constrain=cons)
ns |= dict(lhs=lhs)
# plot solution
if withplots:
points, colors = domain.elem_eval([ns.x, ns.stress[0, 1]],
ischeme='bezier3',
separate=True)
with plot.PyPlot('stress', ndigits=0) as plt:
plt.mesh(points, colors, tight=False)
plt.colorbar()
return lhs, cons
def test_nonlinear_multipleroots(self):
ns = self.ns
ns.u = 'ubasis_n ?dofs_n'
ns.gu = 'u + u^2'
err = self.domain.boundary['bottom'].integral(ns.eval_('(gu - .75)^2'),
geometry=ns.geom,
degree=2)
cons = solver.optimize('dofs',
err,
droptol=1e-15,
lhs0=numpy.ones(len(ns.ubasis)),
newtontol=1e-10)
isnan = numpy.isnan(cons)
self.assertTrue(numpy.equal(isnan, [0, 1, 1, 0, 1, 1, 0, 1, 1]).all())
numpy.testing.assert_almost_equal(cons[~isnan], .5, decimal=15)
def main(nelems: int, etype: str, btype: str, degree: int, poisson: float):
'''
Horizontally loaded linear elastic plate.
.. arguments::
nelems [10]
Number of elements along edge.
etype [square]
Type of elements (square/triangle/mixed).
btype [std]
Type of basis function (std/spline), with availability depending on the
configured element type.
degree [1]
Polynomial degree.
poisson [.25]
Poisson's ratio, nonnegative and strictly smaller than 1/2.
'''
domain, geom = mesh.unitsquare(nelems, etype)
ns = function.Namespace()
ns.x = geom
ns.basis = domain.basis(btype, degree=degree).vector(2)
ns.u_i = 'basis_ni ?lhs_n'
ns.X_i = 'x_i + u_i'
ns.lmbda = 2 * poisson
ns.mu = 1 - 2 * poisson
ns.strain_ij = '(d(u_i, x_j) + d(u_j, x_i)) / 2'
ns.stress_ij = 'lmbda strain_kk δ_ij + 2 mu strain_ij'
sqr = domain.boundary['left'].integral('u_k u_k J(x)' @ ns,
degree=degree * 2)
sqr += domain.boundary['right'].integral('(u_0 - .5)^2 J(x)' @ ns,
degree=degree * 2)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
res = domain.integral('d(basis_ni, x_j) stress_ij J(x)' @ ns,
degree=degree * 2)
lhs = solver.solve_linear('lhs', res, constrain=cons)
bezier = domain.sample('bezier', 5)
X, sxy = bezier.eval(['X', 'stress_01'] @ ns, lhs=lhs)
export.triplot('shear.png', X, sxy, tri=bezier.tri, hull=bezier.hull)
return cons, lhs
def main(
nelems: 'number of elements' = 10,
degree: 'polynomial degree' = 1,
basistype: 'basis function' = 'spline',
solvetol: 'solver tolerance' = 1e-10,
figures: 'create figures' = True,
):
# construct mesh
verts = numpy.linspace(0, numpy.pi/2, nelems+1)
domain, geom = mesh.rectilinear([verts, verts])
# create namespace
ns = function.Namespace()
ns.x = geom
ns.basis = domain.basis(basistype, degree=degree)
ns.u = 'basis_n ?lhs_n'
ns.fx = 'sin(x_0)'
ns.fy = 'exp(x_1)'
# construct residual
res = domain.integral('-basis_n,i u_,i' @ ns, geometry=ns.x, degree=degree*2)
res += domain.boundary['top'].integral('basis_n fx fy' @ ns, geometry=ns.x, degree=degree*2)
# construct dirichlet constraints
sqr = domain.boundary['left'].integral('u^2' @ ns, geometry=ns.x, degree=degree*2)
sqr += domain.boundary['bottom'].integral('(u - fx)^2' @ ns, geometry=ns.x, degree=degree*2)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
# find lhs such that res == 0 and substitute this lhs in the namespace
lhs = solver.solve_linear('lhs', res, constrain=cons)
ns = ns(lhs=lhs)
# plot solution
if figures:
points, colors = domain.elem_eval([ns.x, ns.u], ischeme='bezier9', separate=True)
with plot.PyPlot('solution', index=nelems) as plt:
plt.mesh(points, colors, cmap='jet')
plt.colorbar()
# evaluate error against exact solution fx fy
err = domain.integrate('(u - fx fy)^2' @ ns, geometry=ns.x, degree=degree*2)**.5
log.user('L2 error: {:.2e}'.format(err))
return cons, lhs, err
def main(
nelems: 'number of elements' = 12,
lmbda: 'first lamé constant' = 1.,
mu: 'second lamé constant' = 1.,
degree: 'polynomial degree' = 2,
withplots: 'create plots' = True,
solvetol: 'solver tolerance' = 1e-10,
):
# construct mesh
verts = numpy.linspace(0, 1, nelems+1)
domain, geom = mesh.rectilinear([verts,verts])
# create namespace
ns = function.Namespace(default_geometry_name='x0')
ns.x0 = geom
ns.basis = domain.basis('spline', degree=degree).vector(2)
ns.u_i = 'basis_ni ?lhs_n'
ns.x_i = 'x0_i + u_i'
ns.lmbda = lmbda
ns.mu = mu
ns.strain_ij = '(u_i,j + u_j,i) / 2'
ns.stress_ij = 'lmbda strain_kk δ_ij + 2 mu strain_ij'
# construct dirichlet boundary constraints
sqr = domain.boundary['left'].integral('u_k u_k' @ ns, geometry=ns.x0, degree=2)
sqr += domain.boundary['right'].integral('(u_0 - .5)^2' @ ns, geometry=ns.x0, degree=2)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
# construct residual
res = domain.integral('basis_ni,j stress_ij' @ ns, geometry=ns.x0, degree=2)
# solve system and substitute the solution in the namespace
lhs = solver.solve_linear('lhs', res, constrain=cons)
ns |= dict(lhs=lhs)
# plot solution
if withplots:
points, colors = domain.elem_eval([ns.x, ns.stress[0,1]], ischeme='bezier3', separate=True)
with plot.PyPlot('stress', ndigits=0) as plt:
plt.mesh(points, colors, tight=False)
plt.colorbar()
return lhs, cons
def main(
nelems: 'number of elements' = 20,
epsilon: 'epsilon, 0 for automatic (based on nelems)' = 0,
timestep: 'time step' = .01,
maxtime: 'end time' = 1.,
theta: 'contact angle (degrees)' = 90,
init: 'initial condition (random/bubbles)' = 'random',
figures: 'create figures' = True,
):
mineps = 1. / nelems
if not epsilon:
log.info('setting epsilon={}'.format(mineps))
epsilon = mineps
elif epsilon < mineps:
log.warning('epsilon under crititical threshold: {} < {}'.format(
epsilon, mineps))
# create namespace
ns = function.Namespace()
ns.epsilon = epsilon
ns.ewall = .5 * numpy.cos(theta * numpy.pi / 180)
# construct mesh
xnodes = ynodes = numpy.linspace(0, 1, nelems + 1)
domain, ns.x = mesh.rectilinear([xnodes, ynodes])
# prepare bases
ns.cbasis, ns.mubasis = function.chain(
[domain.basis('spline', degree=2),
domain.basis('spline', degree=2)])
# polulate namespace
ns.c = 'cbasis_n ?lhs_n'
ns.c0 = 'cbasis_n ?lhs0_n'
ns.mu = 'mubasis_n ?lhs_n'
ns.f = '(6 c0 - 2 c0^3 - 4 c) / epsilon^2'
# construct initial condition
if init == 'random':
numpy.random.seed(0)
lhs0 = numpy.random.normal(0, .5, ns.cbasis.shape)
elif init == 'bubbles':
R1 = .25
R2 = numpy.sqrt(.5) * R1 # area2 = .5 * area1
ns.cbubble1 = function.tanh(
(R1 - function.norm2(ns.x -
(.5 + R2 / numpy.sqrt(2) + .8 * ns.epsilon)))
/ ns.epsilon)
ns.cbubble2 = function.tanh(
(R2 - function.norm2(ns.x -
(.5 - R1 / numpy.sqrt(2) - .8 * ns.epsilon)))
/ ns.epsilon)
sqr = domain.integral('(c - cbubble1 - cbubble2 - 1)^2 + mu^2' @ ns,
geometry=ns.x,
degree=4)
lhs0 = solver.optimize('lhs', sqr)
else:
raise Exception('unknown init %r' % init)
# construct residual
res = domain.integral(
'epsilon^2 cbasis_n,k mu_,k + mubasis_n (mu + f) - mubasis_n,k c_,k'
@ ns,
geometry=ns.x,
degree=4)
res -= domain.boundary.integral('mubasis_n ewall' @ ns,
geometry=ns.x,
degree=4)
inertia = domain.integral('cbasis_n c' @ ns, geometry=ns.x, degree=4)
# solve time dependent problem
nsteps = numeric.round(maxtime / timestep)
makeplots = MakePlots(domain, nsteps,
ns) if figures else lambda *args: None
for istep, lhs in log.enumerate(
'timestep',
solver.impliciteuler('lhs',
target0='lhs0',
residual=res,
inertia=inertia,
timestep=timestep,
lhs0=lhs0)):
makeplots(lhs)
if istep == nsteps:
break
return lhs0, lhs
def main(nelems: int, etype: str, btype: str, degree: int,
epsilon: typing.Optional[float], contactangle: float, timestep: float,
mtol: float, seed: int, circle: bool, stab: stab):
'''
Cahn-Hilliard equation on a unit square/circle.
.. arguments::
nelems [20]
Number of elements along domain edge.
etype [square]
Type of elements (square/triangle/mixed).
btype [std]
Type of basis function (std/spline), with availability depending on the
configured element type.
degree [2]
Polynomial degree.
epsilon []
Interface thickness; defaults to an automatic value based on the
configured mesh density if left unspecified.
contactangle [90]
Wall contact angle in degrees.
timestep [.01]
Time step.
mtol [.01]
Threshold value for chemical potential peak to peak difference, used as
a stop criterion.
seed [0]
Random seed for the initial condition.
circle [no]
Select circular domain as opposed to a unit square.
stab [linear]
Stabilization method (linear/optimal/none).
'''
mineps = 1. / nelems
if epsilon is None:
treelog.info('setting epsilon={}'.format(mineps))
epsilon = mineps
elif epsilon < mineps:
treelog.warning('epsilon under crititical threshold: {} < {}'.format(
epsilon, mineps))
domain, geom = mesh.unitsquare(nelems, etype)
bezier = domain.sample('bezier', 5) # sample for plotting
ns = function.Namespace()
if not circle:
ns.x = geom
else:
angle = (geom - .5) * (numpy.pi / 2)
ns.x = function.sin(angle) * function.cos(angle)[[1, 0
]] / numpy.sqrt(2)
ns.epsilon = epsilon
ns.ewall = .5 * numpy.cos(contactangle * numpy.pi / 180)
ns.cbasis = ns.mbasis = domain.basis('std', degree=degree)
ns.c = 'cbasis_n ?c_n'
ns.dc = 'cbasis_n (?c_n - ?c0_n)'
ns.m = 'mbasis_n ?m_n'
ns.F = '.5 (c^2 - 1)^2 / epsilon^2'
ns.dF = stab.value
ns.dt = timestep
nrg_mix = domain.integral('F J(x)' @ ns, degree=7)
nrg_iface = domain.integral('.5 sum:k(d(c, x_k)^2) J(x)' @ ns, degree=7)
nrg_wall = domain.boundary.integral('(abs(ewall) + c ewall) J(x)' @ ns,
degree=7)
nrg = nrg_mix + nrg_iface + nrg_wall + domain.integral(
'(dF - m dc - .5 dt epsilon^2 sum:k(d(m, x_k)^2)) J(x)' @ ns, degree=7)
numpy.random.seed(seed)
state = dict(c=numpy.random.normal(0, .5, ns.cbasis.shape),
m=numpy.random.normal(0, .5,
ns.mbasis.shape)) # initial condition
with treelog.iter.plain('timestep', itertools.count()) as steps:
for istep in steps:
E = sample.eval_integrals(nrg_mix, nrg_iface, nrg_wall, **state)
treelog.user(
'energy: {0:.3f} ({1[0]:.0f}% mixture, {1[1]:.0f}% interface, {1[2]:.0f}% wall)'
.format(sum(E), 100 * numpy.array(E) / sum(E)))
x, c, m = bezier.eval(['x', 'c', 'm'] @ ns, **state)
export.triplot('phase.png', x, c, tri=bezier.tri, clim=(-1, 1))
export.triplot('chempot.png', x, m, tri=bezier.tri)
if numpy.ptp(m) < mtol:
break
state['c0'] = state['c']
state = solver.optimize(['c', 'm'],
nrg,
arguments=state,
tol=1e-10)
return state
def main(nelems: int, ndims: int, degree: int, timescale: float,
newtontol: float, endtime: float):
'''
Burgers equation on a 1D or 2D periodic domain.
.. arguments::
nelems [20]
Number of elements along a single dimension.
ndims [1]
Number of spatial dimensions.
degree [1]
Polynomial degree for discontinuous basis functions.
timescale [.5]
Fraction of timestep and element size: timestep=timescale/nelems.
newtontol [1e-5]
Newton tolerance.
endtime [inf]
Stopping time.
'''
domain, geom = mesh.rectilinear([numpy.linspace(0, 1, nelems + 1)] * ndims,
periodic=range(ndims))
ns = function.Namespace()
ns.x = geom
ns.basis = domain.basis('discont', degree=degree)
ns.u = 'basis_n ?lhs_n'
ns.f = '.5 u^2'
ns.C = 1
res = domain.integral('-basis_n,0 f d:x' @ ns, degree=5)
res += domain.interfaces.integral(
'-[basis_n] n_0 ({f} - .5 C [u] n_0) d:x' @ ns, degree=degree * 2)
inertia = domain.integral('basis_n u d:x' @ ns, degree=5)
sqr = domain.integral(
'(u - exp(-?y_i ?y_i)(y_i = 5 (x_i - 0.5_i)))^2 d:x' @ ns, degree=5)
lhs0 = solver.optimize('lhs', sqr)
timestep = timescale / nelems
bezier = domain.sample('bezier', 7)
with treelog.iter.plain(
'timestep',
solver.impliciteuler('lhs',
res,
inertia,
timestep=timestep,
lhs0=lhs0,
newtontol=newtontol)) as steps:
for itime, lhs in enumerate(steps):
x, u = bezier.eval(['x_i', 'u'] @ ns, lhs=lhs)
export.triplot('solution.png',
x,
u,
tri=bezier.tri,
hull=bezier.hull,
clim=(0, 1))
if itime * timestep >= endtime:
break
return lhs
def test_nanres(self):
err = self.domain.integral('(sqrt(1 - u) - .5)^2' @ self.ns, degree=2)
dofs = solver.optimize('dofs', err, tol=1e-10)
numpy.testing.assert_almost_equal(dofs, .75)
def main(nrefine: int, traction: float, radius: float, poisson: float):
'''
Horizontally loaded linear elastic plate with IGA hole.
.. arguments::
nrefine [2]
Number of uniform refinements starting from 1x2 base mesh.
traction [.1]
Far field traction (relative to Young's modulus).
radius [.5]
Cut-out radius.
poisson [.3]
Poisson's ratio, nonnegative and strictly smaller than 1/2.
'''
# create the coarsest level parameter domain
domain, geom0 = mesh.rectilinear([1, 2])
bsplinebasis = domain.basis('spline', degree=2)
controlweights = numpy.ones(12)
controlweights[1:3] = .5 + .25 * numpy.sqrt(2)
weightfunc = bsplinebasis.dot(controlweights)
nurbsbasis = bsplinebasis * controlweights / weightfunc
# create geometry function
indices = [0, 2], [1, 2], [2, 1], [2, 0]
controlpoints = numpy.concatenate([
numpy.take([0, 2**.5 - 1, 1], indices) * radius,
numpy.take([0, .3, 1], indices) * (radius + 1) / 2,
numpy.take([0, 1, 1], indices)
])
geom = (nurbsbasis[:, numpy.newaxis] * controlpoints).sum(0)
radiuserr = domain.boundary['left'].integral(
(function.norm2(geom) - radius)**2 * function.J(geom0),
degree=9).eval()**.5
treelog.info('hole radius exact up to L2 error {:.2e}'.format(radiuserr))
# refine domain
if nrefine:
domain = domain.refine(nrefine)
bsplinebasis = domain.basis('spline', degree=2)
controlweights = domain.project(weightfunc,
onto=bsplinebasis,
geometry=geom0,
ischeme='gauss9')
nurbsbasis = bsplinebasis * controlweights / weightfunc
ns = function.Namespace()
ns.x = geom
ns.lmbda = 2 * poisson
ns.mu = 1 - poisson
ns.ubasis = nurbsbasis.vector(2)
ns.u_i = 'ubasis_ni ?lhs_n'
ns.X_i = 'x_i + u_i'
ns.strain_ij = '(d(u_i, x_j) + d(u_j, x_i)) / 2'
ns.stress_ij = 'lmbda strain_kk δ_ij + 2 mu strain_ij'
ns.r2 = 'x_k x_k'
ns.R2 = radius**2 / ns.r2
ns.k = (3 - poisson) / (1 + poisson) # plane stress parameter
ns.scale = traction * (1 + poisson) / 2
ns.uexact_i = 'scale (x_i ((k + 1) (0.5 + R2) + (1 - R2) R2 (x_0^2 - 3 x_1^2) / r2) - 2 δ_i1 x_1 (1 + (k - 1 + R2) R2))'
ns.du_i = 'u_i - uexact_i'
sqr = domain.boundary['top,bottom'].integral('(u_i n_i)^2 J(x)' @ ns,
degree=9)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
sqr = domain.boundary['right'].integral('du_k du_k J(x)' @ ns, degree=20)
cons = solver.optimize('lhs', sqr, droptol=1e-15, constrain=cons)
# construct residual
res = domain.integral('d(ubasis_ni, x_j) stress_ij J(x)' @ ns, degree=9)
# solve system
lhs = solver.solve_linear('lhs', res, constrain=cons)
# vizualize result
bezier = domain.sample('bezier', 9)
X, stressxx = bezier.eval(['X', 'stress_00'] @ ns, lhs=lhs)
export.triplot('stressxx.png',
X,
stressxx,
tri=bezier.tri,
hull=bezier.hull,
clim=(numpy.nanmin(stressxx), numpy.nanmax(stressxx)))
# evaluate error
err = domain.integral('<du_k du_k, sum:ij(d(du_i, x_j)^2)>_n J(x)' @ ns,
degree=9).eval(lhs=lhs)**.5
treelog.user('errors: L2={:.2e}, H1={:.2e}'.format(*err))
return err, cons, lhs
def main(nelems: int, degree: int, reynolds: float, rotation: float,
timestep: float, maxradius: float, seed: int, endtime: float):
'''
Flow around a cylinder.
.. arguments::
nelems [24]
Element size expressed in number of elements along the cylinder wall.
All elements have similar shape with approximately unit aspect ratio,
with elements away from the cylinder wall growing exponentially.
degree [3]
Polynomial degree for velocity space; the pressure space is one degree
less.
reynolds [1000]
Reynolds number, taking the cylinder radius as characteristic length.
rotation [0]
Cylinder rotation speed.
timestep [.04]
Time step
maxradius [25]
Target exterior radius; the actual domain size is subject to integer
multiples of the configured element size.
seed [0]
Random seed for small velocity noise in the intial condition.
endtime [inf]
Stopping time.
'''
elemangle = 2 * numpy.pi / nelems
melems = int(numpy.log(2 * maxradius) / elemangle + .5)
treelog.info('creating {}x{} mesh, outer radius {:.2f}'.format(
melems, nelems, .5 * numpy.exp(elemangle * melems)))
domain, geom = mesh.rectilinear([melems, nelems], periodic=(1, ))
domain = domain.withboundary(inner='left', outer='right')
ns = function.Namespace()
ns.uinf = 1, 0
ns.r = .5 * function.exp(elemangle * geom[0])
ns.Re = reynolds
ns.phi = geom[1] * elemangle # add small angle to break element symmetry
ns.x_i = 'r <cos(phi), sin(phi)>_i'
ns.J = ns.x.grad(geom)
ns.unbasis, ns.utbasis, ns.pbasis = function.chain([ # compatible spaces
domain.basis(
'spline', degree=(degree, degree - 1), removedofs=((0, ), None)),
domain.basis('spline', degree=(degree - 1, degree)),
domain.basis('spline', degree=degree - 1),
]) / function.determinant(ns.J)
ns.ubasis_ni = 'unbasis_n J_i0 + utbasis_n J_i1' # piola transformation
ns.u_i = 'ubasis_ni ?lhs_n'
ns.p = 'pbasis_n ?lhs_n'
ns.sigma_ij = '(u_i,j + u_j,i) / Re - p δ_ij'
ns.h = .5 * elemangle
ns.N = 5 * degree / ns.h
ns.rotation = rotation
ns.uwall_i = '0.5 rotation <-sin(phi), cos(phi)>_i'
inflow = domain.boundary['outer'].select(
-ns.uinf.dotnorm(ns.x),
ischeme='gauss1') # upstream half of the exterior boundary
sqr = inflow.integral('(u_i - uinf_i) (u_i - uinf_i)' @ ns,
degree=degree * 2)
cons = solver.optimize(
'lhs', sqr, droptol=1e-15) # constrain inflow semicircle to uinf
sqr = domain.integral('(u_i - uinf_i) (u_i - uinf_i) + p^2' @ ns,
degree=degree * 2)
lhs0 = solver.optimize('lhs', sqr) # set initial condition to u=uinf, p=0
numpy.random.seed(seed)
lhs0 *= numpy.random.normal(1, .1, lhs0.shape) # add small velocity noise
res = domain.integral(
'(ubasis_ni u_i,j u_j + ubasis_ni,j sigma_ij + pbasis_n u_k,k) d:x'
@ ns,
degree=9)
res += domain.boundary['inner'].integral(
'(N ubasis_ni - (ubasis_ni,j + ubasis_nj,i) n_j) (u_i - uwall_i) d:x / Re'
@ ns,
degree=9)
inertia = domain.integral('ubasis_ni u_i d:x' @ ns, degree=9)
bbox = numpy.array(
[[-2, 46 / 9],
[-2, 2]]) # bounding box for figure based on 16x9 aspect ratio
bezier0 = domain.sample('bezier', 5)
bezier = bezier0.subset((bezier0.eval(
(ns.x - bbox[:, 0]) * (bbox[:, 1] - ns.x)) > 0).all(axis=1))
interpolate = util.tri_interpolator(
bezier.tri, bezier.eval(ns.x),
mergetol=1e-5) # interpolator for quivers
spacing = .05 # initial quiver spacing
xgrd = util.regularize(bbox, spacing)
with treelog.iter.plain(
'timestep',
solver.impliciteuler('lhs',
residual=res,
inertia=inertia,
lhs0=lhs0,
timestep=timestep,
constrain=cons,
newtontol=1e-10)) as steps:
for istep, lhs in enumerate(steps):
t = istep * timestep
x, u, normu, p = bezier.eval(
['x_i', 'u_i', 'sqrt(u_k u_k)', 'p'] @ ns, lhs=lhs)
ugrd = interpolate[xgrd](u)
with export.mplfigure('flow.png', figsize=(12.8, 7.2)) as fig:
ax = fig.add_axes([0, 0, 1, 1],
yticks=[],
xticks=[],
frame_on=False,
xlim=bbox[0],
ylim=bbox[1])
im = ax.tripcolor(x[:, 0],
x[:, 1],
bezier.tri,
p,
shading='gouraud',
cmap='jet')
import matplotlib.collections
ax.add_collection(
matplotlib.collections.LineCollection(x[bezier.hull],
colors='k',
linewidths=.1,
alpha=.5))
ax.quiver(xgrd[:, 0],
xgrd[:, 1],
ugrd[:, 0],
ugrd[:, 1],
angles='xy',
width=1e-3,
headwidth=3e3,
headlength=5e3,
headaxislength=2e3,
zorder=9,
alpha=.5)
ax.plot(0,
0,
'k',
marker=(3, 2, t * rotation * 180 / numpy.pi - 90),
markersize=20)
cax = fig.add_axes([0.9, 0.1, 0.01, 0.8])
cax.tick_params(labelsize='large')
fig.colorbar(im, cax=cax)
if t >= endtime:
break
xgrd = util.regularize(bbox, spacing, xgrd + ugrd * timestep)
return lhs0, lhs
def main(
nelems: 'number of elements' = 12,
viscosity: 'fluid viscosity' = 1e-2,
density: 'fluid density' = 1,
tol: 'solver tolerance' = 1e-12,
rotation: 'cylinder rotation speed' = 0,
timestep: 'time step' = 1/24,
maxradius: 'approximate domain size' = 25,
tmax: 'end time' = numpy.inf,
degree: 'polynomial degree' = 2,
withplots: 'create plots' = True,
):
log.user('reynolds number: {:.1f}'.format(density / viscosity)) # based on unit length and velocity
# create namespace
ns = function.Namespace()
ns.uinf = 1, 0
ns.density = density
ns.viscosity = viscosity
# construct mesh
rscale = numpy.pi / nelems
melems = numpy.ceil(numpy.log(2*maxradius) / rscale).astype(int)
log.info('creating {}x{} mesh, outer radius {:.2f}'.format(melems, 2*nelems, .5*numpy.exp(rscale*melems)))
domain, x0 = mesh.rectilinear([range(melems+1),numpy.linspace(0,2*numpy.pi,2*nelems+1)], periodic=(1,))
rho, phi = x0
phi += 1e-3 # tiny nudge (0.057 deg) to break element symmetry
radius = .5 * function.exp(rscale * rho)
ns.x = radius * function.trigtangent(phi)
domain = domain.withboundary(inner='left', inflow=domain.boundary['right'].select(-ns.uinf.dotnorm(ns.x), ischeme='gauss1'))
# prepare bases (using piola transformation to maintain u/p compatibility)
J = ns.x.grad(x0)
detJ = function.determinant(J)
ns.unbasis, ns.utbasis, ns.pbasis = function.chain([ # compatible spaces using piola transformation
domain.basis('spline', degree=(degree+1,degree), removedofs=((0,),None))[:,_] * J[:,0] / detJ,
domain.basis('spline', degree=(degree,degree+1))[:,_] * J[:,1] / detJ,
domain.basis('spline', degree=degree) / detJ,
])
ns.ubasis_ni = 'unbasis_ni + utbasis_ni'
# populate namespace
ns.u_i = 'ubasis_ni ?lhs_n'
ns.p = 'pbasis_n ?lhs_n'
ns.sigma_ij = 'viscosity (u_i,j + u_j,i) - p δ_ij'
ns.hinner = 2 * numpy.pi / nelems
ns.c = 5 * (degree+1) / ns.hinner
ns.nietzsche_ni = 'viscosity (c ubasis_ni - (ubasis_ni,j + ubasis_nj,i) n_j)'
ns.ucyl = -.5 * rotation * function.trignormal(phi)
# create residual vector components
res = domain.integral('ubasis_ni,j sigma_ij + pbasis_n u_k,k' @ ns, geometry=ns.x, degree=2*(degree+1))
res += domain.boundary['inner'].integral('nietzsche_ni (u_i - ucyl_i)' @ ns, geometry=ns.x, degree=2*(degree+1))
oseen = domain.integral('density ubasis_ni u_i,j uinf_j' @ ns, geometry=ns.x, degree=2*(degree+1))
convec = domain.integral('density ubasis_ni u_i,j u_j' @ ns, geometry=ns.x, degree=3*(degree+1))
inertia = domain.integral('density ubasis_ni u_i' @ ns, geometry=ns.x, degree=2*(degree+1))
# constrain full velocity vector at inflow
sqr = domain.boundary['inflow'].integral('(u_i - uinf_i) (u_i - uinf_i)' @ ns, geometry=ns.x, degree=9)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
# solve unsteady navier-stokes equations, starting from stationary oseen flow
lhs0 = solver.solve_linear('lhs', res+oseen, constrain=cons)
makeplots = MakePlots(domain, ns, timestep=timestep, rotation=rotation) if withplots else lambda *args: None
for istep, lhs in log.enumerate('timestep', solver.impliciteuler('lhs', residual=res+convec, inertia=inertia, lhs0=lhs0, timestep=timestep, constrain=cons, newtontol=1e-10)):
makeplots(lhs)
if istep * timestep >= tmax:
break
return lhs0, lhs
def main(inflow: 'inflow velocity' = 10,
viscosity: 'kinematic viscosity' = 1.0,
density: 'density' = 1.0,
theta=0.5,
timestepsize=0.01):
# mesh and geometry definition
grid_x_1 = numpy.linspace(-3, -1, 7)
grid_x_1 = grid_x_1[:-1]
grid_x_2 = numpy.linspace(-1, -0.3, 8)
grid_x_2 = grid_x_2[:-1]
grid_x_3 = numpy.linspace(-0.3, 0.3, 13)
grid_x_3 = grid_x_3[:-1]
grid_x_4 = numpy.linspace(0.3, 1, 8)
grid_x_4 = grid_x_4[:-1]
grid_x_5 = numpy.linspace(1, 3, 7)
grid_x = numpy.concatenate(
(grid_x_1, grid_x_2, grid_x_3, grid_x_4, grid_x_5), axis=None)
grid_y_1 = numpy.linspace(0, 1.5, 16)
grid_y_1 = grid_y_1[:-1]
grid_y_2 = numpy.linspace(1.5, 2, 4)
grid_y_2 = grid_y_2[:-1]
grid_y_3 = numpy.linspace(2, 4, 7)
grid_y = numpy.concatenate((grid_y_1, grid_y_2, grid_y_3), axis=None)
grid = [grid_x, grid_y]
topo, geom = mesh.rectilinear(grid)
domain = topo.withboundary(inflow='left', wall='top,bottom', outflow='right') - \
topo[18:20, :10].withboundary(flap='left,right,top')
# Nutils namespace
ns = function.Namespace()
# time approximations
# TR interpolation
ns._functions['t'] = lambda f: theta * f + (1 - theta) * subs0(f)
ns._functions_nargs['t'] = 1
# 1st order FD
ns._functions['δt'] = lambda f: (f - subs0(f)) / dt
ns._functions_nargs['δt'] = 1
# 2nd order FD
ns._functions['tt'] = lambda f: (1.5 * f - 2 * subs0(f) + 0.5 * subs00(f)
) / dt
ns._functions_nargs['tt'] = 1
# extrapolation for pressure
ns._functions['tp'] = lambda f: (1.5 * f - 0.5 * subs0(f))
ns._functions_nargs['tp'] = 1
ns.nu = viscosity
ns.rho = density
ns.uin = inflow
ns.x0 = geom # reference geometry
ns.dbasis = domain.basis('std', degree=1).vector(2)
ns.d_i = 'dbasis_ni ?meshdofs_n'
ns.umesh_i = 'dbasis_ni (1.5 ?meshdofs_n - 2 ?oldmeshdofs_n + 0.5 ?oldoldmeshdofs_n ) / ?dt'
ns.x_i = 'x0_i + d_i' # moving geometry
ns.ubasis, ns.pbasis = function.chain([
domain.basis('std', degree=2).vector(2),
domain.basis('std', degree=1),
])
ns.F_i = 'ubasis_ni ?F_n' # stress field
ns.urel_i = 'ubasis_ni ?lhs_n' # relative velocity
ns.u_i = 'umesh_i + urel_i' # total velocity
ns.p = 'pbasis_n ?lhs_n' # pressure
# initialization of dofs
meshdofs = numpy.zeros(len(ns.dbasis))
oldmeshdofs = meshdofs
oldoldmeshdofs = meshdofs
oldoldoldmeshdofs = meshdofs
lhs0 = numpy.zeros(len(ns.ubasis))
# for visualization
bezier = domain.sample('bezier', 2)
# preCICE setup
configFileName = "../precice-config.xml"
participantName = "Fluid"
solverProcessIndex = 0
solverProcessSize = 1
interface = precice.Interface(participantName, configFileName,
solverProcessIndex, solverProcessSize)
# define coupling meshes
meshName = "Fluid-Mesh"
meshID = interface.get_mesh_id(meshName)
couplinginterface = domain.boundary['flap']
couplingsample = couplinginterface.sample(
'gauss', degree=2) # mesh located at Gauss points
dataIndices = interface.set_mesh_vertices(meshID,
couplingsample.eval(ns.x0))
# coupling data
writeData = "Force"
readData = "Displacement"
writedataID = interface.get_data_id(writeData, meshID)
readdataID = interface.get_data_id(readData, meshID)
# initialize preCICE
precice_dt = interface.initialize()
dt = min(precice_dt, timestepsize)
# boundary conditions for fluid equations
sqr = domain.boundary['wall,flap'].integral('urel_k urel_k d:x0' @ ns,
degree=4)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
sqr = domain.boundary['inflow'].integral(
'((urel_0 - uin)^2 + urel_1^2) d:x0' @ ns, degree=4)
cons = solver.optimize('lhs', sqr, droptol=1e-15, constrain=cons)
# weak form fluid equations
res = domain.integral('t(ubasis_ni,j (u_i,j + u_j,i) rho nu d:x)' @ ns,
degree=4)
res += domain.integral('(-ubasis_ni,j p δ_ij + pbasis_n u_k,k) d:x' @ ns,
degree=4)
res += domain.integral('rho ubasis_ni δt(u_i d:x)' @ ns, degree=4)
res += domain.integral('rho ubasis_ni t(u_i,j urel_j d:x)' @ ns, degree=4)
# weak form for force computation
resF = domain.integral('(ubasis_ni,j (u_i,j + u_j,i) rho nu d:x)' @ ns,
degree=4)
resF += domain.integral('tp(-ubasis_ni,j p δ_ij d:x)' @ ns, degree=4)
resF += domain.integral('pbasis_n u_k,k d:x' @ ns, degree=4)
resF += domain.integral('rho ubasis_ni tt(u_i d:x)' @ ns, degree=4)
resF += domain.integral('rho ubasis_ni (u_i,j urel_j d:x)' @ ns, degree=4)
resF += couplinginterface.sample('gauss',
4).integral('ubasis_ni F_i d:x' @ ns)
consF = numpy.isnan(
solver.optimize('F',
couplinginterface.sample('gauss',
4).integral('F_i F_i' @ ns),
droptol=1e-10))
# boundary conditions mesh displacements
sqr = domain.boundary['inflow,outflow,wall'].integral('d_i d_i' @ ns,
degree=2)
meshcons0 = solver.optimize('meshdofs', sqr, droptol=1e-15)
# weak form mesh displacements
meshsqr = domain.integral('d_i,x0_j d_i,x0_j d:x0' @ ns, degree=2)
# better initial guess: start from Stokes solution, comment out for comparison with other solvers
#res_stokes = domain.integral('(ubasis_ni,j ((u_i,j + u_j,i) rho nu - p δ_ij) + pbasis_n u_k,k) d:x' @ ns, degree=4)
#lhs0 = solver.solve_linear('lhs', res_stokes, constrain=cons, arguments=dict(meshdofs=meshdofs, oldmeshdofs=oldmeshdofs, oldoldmeshdofs=oldoldmeshdofs, oldoldoldmeshdofs=oldoldoldmeshdofs, dt=dt))
lhs00 = lhs0
timestep = 0
t = 0
while interface.is_coupling_ongoing():
# read displacements from interface
if interface.is_read_data_available():
readdata = interface.read_block_vector_data(
readdataID, dataIndices)
coupledata = couplingsample.asfunction(readdata)
sqr = couplingsample.integral(((ns.d - coupledata)**2).sum(0))
meshcons = solver.optimize('meshdofs',
sqr,
droptol=1e-15,
constrain=meshcons0)
meshdofs = solver.optimize('meshdofs', meshsqr, constrain=meshcons)
# save checkpoint
if interface.is_action_required(
precice.action_write_iteration_checkpoint()):
lhs_checkpoint = lhs0
lhs00_checkpoint = lhs00
t_checkpoint = t
timestep_checkpoint = timestep
oldmeshdofs_checkpoint = oldmeshdofs
oldoldmeshdofs_checkpoint = oldoldmeshdofs
oldoldoldmeshdofs_checkpoint = oldoldoldmeshdofs
interface.mark_action_fulfilled(
precice.action_write_iteration_checkpoint())
# solve fluid equations
lhs1 = solver.newton(
'lhs',
res,
lhs0=lhs0,
constrain=cons,
arguments=dict(
lhs0=lhs0,
dt=dt,
meshdofs=meshdofs,
oldmeshdofs=oldmeshdofs,
oldoldmeshdofs=oldoldmeshdofs,
oldoldoldmeshdofs=oldoldoldmeshdofs)).solve(tol=1e-6)
# write forces to interface
if interface.is_write_data_required(dt):
F = solver.solve_linear('F',
resF,
constrain=consF,
arguments=dict(
lhs00=lhs00,
lhs0=lhs0,
lhs=lhs1,
dt=dt,
meshdofs=meshdofs,
oldmeshdofs=oldmeshdofs,
oldoldmeshdofs=oldoldmeshdofs,
oldoldoldmeshdofs=oldoldoldmeshdofs))
# writedata = couplingsample.eval(ns.F, F=F) # for stresses
writedata = couplingsample.eval('F_i d:x' @ ns, F=F, meshdofs=meshdofs) * \
numpy.concatenate([p.weights for p in couplingsample.points])[:, numpy.newaxis]
interface.write_block_vector_data(writedataID, dataIndices,
writedata)
# do the coupling
precice_dt = interface.advance(dt)
dt = min(precice_dt, timestepsize)
# advance variables
timestep += 1
t += dt
lhs00 = lhs0
lhs0 = lhs1
oldoldoldmeshdofs = oldoldmeshdofs
oldoldmeshdofs = oldmeshdofs
oldmeshdofs = meshdofs
# read checkpoint if required
if interface.is_action_required(
precice.action_read_iteration_checkpoint()):
lhs0 = lhs_checkpoint
lhs00 = lhs00_checkpoint
t = t_checkpoint
timestep = timestep_checkpoint
oldmeshdofs = oldmeshdofs_checkpoint
oldoldmeshdofs = oldoldmeshdofs_checkpoint
oldoldoldmeshdofs = oldoldoldmeshdofs_checkpoint
interface.mark_action_fulfilled(
precice.action_read_iteration_checkpoint())
if interface.is_time_window_complete():
x, u, p = bezier.eval(['x_i', 'u_i', 'p'] @ ns,
lhs=lhs1,
meshdofs=meshdofs,
oldmeshdofs=oldmeshdofs,
oldoldmeshdofs=oldoldmeshdofs,
oldoldoldmeshdofs=oldoldoldmeshdofs,
dt=dt)
with treelog.add(treelog.DataLog()):
export.vtk('Fluid_' + str(timestep), bezier.tri, x, u=u, p=p)
interface.finalize()
def main(fname: str, degree: int, Δuload: unit['mm'], nsteps: int, E: unit['GPa'], nu: float, σyield: unit['MPa'], hardening: unit['GPa'], referencedata: typing.Optional[str], testing: bool):
'''
Plastic deformation of a perforated strip.
.. arguments::
fname [strip.msh]
Mesh file with units in [mm]
degree [1]
Finite element interpolation order
Δuload [5μm]
Load boundary displacement steps
nsteps [11]
Number of load steps
E [70GPa]
Young's modulus
nu [0.2]
Poisson ratio
σyield [243MPa]
Yield strength
hardening [2.25GPa]
Hardening parameter
referencedata [zienkiewicz.csv]
Reference data file name
testing [False]
Use a 1 element mesh for testing
.. presets::
testing
nsteps=30
referencedata=
testing=True
'''
# We commence with reading the mesh from the specified GMSH file, or, alternatively,
# we continue with a single-element mesh for testing purposes.
domain, geom = mesh.gmsh(pathlib.Path(__file__).parent/fname)
Wdomain, Hdomain = domain.boundary['load'].integrate([function.J(geom),geom[1]*function.J(geom)], degree=1)
Hdomain /= Wdomain
if testing:
domain, geom = mesh.rectilinear([numpy.linspace(0,Wdomain/2,2),numpy.linspace(0,Hdomain,2)])
domain = domain.withboundary(hsymmetry='left', vsymmetry='bottom', load='top')
# We next initiate the point set in which the constitutive bahvior will be evaluated.
# Note that this is also the point set in which the history variable will be stored.
gauss = domain.sample('gauss', 2)
# Elasto-plastic formulation
# ==========================
# The weak formulation is constructed using a `Namespace`, which is initialized and
# populated with the necessary problem parameters and coordinate system. Note that the
# coefficients `mu` and `λ` have been defined such that 'C_{ijkl}' is the fourth-order
# **plane stress** elasticity tensor.
ns = function.Namespace(fallback_length=domain.ndims)
ns.x = geom
ns.Δuload = Δuload
ns.mu = E/(1-nu**2)*((1-nu)/2)
ns.λ = E/(1-nu**2)*nu
ns.delta = function.eye(domain.ndims)
ns.C_ijkl = 'mu ( delta_ik delta_jl + delta_il delta_jk ) + λ delta_ij delta_kl'
# We make use of a Lagrange finite element basis of arbitrary `degree`. Since we
# approximate the displacement field, the basis functions are vector-valued. Both
# the displacement field at the current load step `u` and displacement field of the
# previous load step `u0` are approximated using this basis:
ns.basis = domain.basis('std',degree=degree).vector(domain.ndims)
ns.u0_i = 'basis_ni ?lhs0_n'
ns.u_i = 'basis_ni ?lhs_n'
# This simulation is based on a standard elasto-plasticity model. In this
# model the *total strain*, ε_kl = ½ (∂u_k/∂x_l + ∂u_l/∂x_k)', is comprised of an
# elastic and a plastic part:
#
# ε_kl = εe_kl + εp_kl
#
# The stress is related to the *elastic strain*, εe_kl, through Hooke's law:
#
# σ_ij = C_ijkl εe_kl = C_ijkl (ε_kl - εp_kl)
ns.ε_kl = '(u_k,l + u_l,k) / 2'
ns.ε0_kl = '(u0_k,l + u0_l,k) / 2'
ns.gbasis = gauss.basis()
ns.εp0_ij = 'gbasis_n ?εp0_nij'
ns.κ0 = 'gbasis_n ?κ0_n'
ns.εp = PlasticStrain(ns.ε, ns.ε0, ns.εp0, ns.κ0, E, nu, σyield, hardening)
ns.εe_ij = 'ε_ij - εp_ij'
ns.σ_ij = 'C_ijkl εe_kl'
# Note that the plasticity model is implemented through the user-defined function `PlasticStrain`,
# which implements the actual yielding model including a standard return mapping algorithm. This
# function is discussed in detail below.
#
# The components of the residual vector are then defined as:
#
# r_n = ∫_Ω (∂N_ni/∂x_j) σ_ij dΩ
res = domain.integral('basis_ni,j σ_ij d:x' @ ns, degree=2)
# The problem formulation is completed by supplementing prescribed displacement boundary conditions
# (for a load step), which are computed in the standard manner:
sqr = domain.boundary['hsymmetry,vsymmetry'].integral('(u_k n_k)^2 d:x' @ ns, degree=2)
sqr += domain.boundary['load'].integral('(u_k n_k - Δuload)^2 d:x' @ ns, degree=2)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
# Incremental-iterative solution procedure
# ========================================
# We initialize the solution vector for the first load step `lhs` and solution vector
# of the previous load step `lhs0`. Note that, in order to construct a predictor step
# for the first load step, we define the previous load step state as the solution
# vector corresponding to a negative elastic loading step.
lhs0 = -solver.solve_linear('lhs', domain.integral('basis_ni,j C_ijkl ε_kl d:x' @ ns, degree=2), constrain=cons)
lhs = numpy.zeros_like(lhs0)
εp0 = numpy.zeros((gauss.npoints,)+ns.εp0.shape)
κ0 = numpy.zeros((gauss.npoints,)+ns.κ0.shape)
# To store the force-dispalcement data we initialize an empty data array with the
# inital state solution substituted in the first row.
fddata = numpy.empty(shape=(nsteps+1,2))
fddata[:] = numpy.nan
fddata[0,:] = 0
# Load step incrementation
# ------------------------
with treelog.iter.fraction('step', range(nsteps)) as counter:
for step in counter:
# The solution of the previous load step is set to `lhs0`, and the Newton solution
# procedure is initialized by extrapolation of the state vector:
lhs_init = lhs + (lhs-lhs0)
lhs0 = lhs
# The non-linear system of equations is solved for `lhs` using Newton iterations,
# where the `step` variable is used to scale the incremental constraints.
lhs = solver.newton(target='lhs', residual=res, constrain=cons*step, lhs0=lhs_init, arguments={'lhs0':lhs0,'εp0':εp0,'κ0':κ0}).solve(tol=1e-6)
# The computed solution is post-processed in the form of a loading curve - which
# plots the normalized mean stress versus the maximum 'ε_{yy}' strain
# component - and a contour plot showing the 'σ_{yy}' stress component on a
# deformed mesh. Note that since the stresses are defined in the integration points
# only, a post-processing step is involved that transfers the stress information to
# the nodal points.
εyymax = gauss.eval(ns.ε[1,1], arguments=dict(lhs=lhs)).max()
basis = domain.basis('std', degree=1)
bw, b = domain.integrate([basis * ns.σ[1,1] * function.J(geom), basis * function.J(geom)], degree=2, arguments=dict(lhs=lhs,lhs0=lhs0,εp0=εp0,κ0=κ0))
σyy = basis.dot(bw / b)
uyload, σyyload = domain.boundary['load'].integrate(['u_1 d:x'@ns,σyy * function.J(geom)], degree=2, arguments=dict(lhs=lhs,εp0=εp0,κ0=κ0))
uyload /= Wdomain
σyyload /= Wdomain
fddata[step,0] = (E*εyymax)/σyield
fddata[step,1] = (σyyload*2)/σyield
with export.mplfigure('forcedisp.png') as fig:
ax = fig.add_subplot(111, xlabel=r'${E \cdot {\rm max}(\varepsilon_{yy})}/{\sigma_{\rm yield}}$', ylabel=r'${\sigma_{\rm mean}}/{\sigma_{\rm yield}}$')
if referencedata:
data = numpy.genfromtxt(pathlib.Path(__file__).parent/referencedata, delimiter=',', skip_header=1)
ax.plot(data[:,0], data[:,1], 'r:', label='Reference')
ax.legend()
ax.plot(fddata[:,0], fddata[:,1], 'o-', label='Nutils')
ax.grid()
bezier = domain.sample('bezier', 3)
points, uvals, σyyvals = bezier.eval(['(x_i + 25 u_i)' @ ns, ns.u, σyy], arguments=dict(lhs=lhs))
with export.mplfigure('stress.png') as fig:
ax = fig.add_subplot(111, aspect='equal', xlabel=r'$x$ [mm]', ylabel=r'$y$ [mm]')
im = ax.tripcolor(points[:,0]/unit('mm'), points[:,1]/unit('mm'), bezier.tri, σyyvals/unit('MPa'), shading='gouraud', cmap='jet')
ax.add_collection(collections.LineCollection(points.take(bezier.hull, axis=0), colors='k', linewidths=.1))
ax.autoscale(enable=True, axis='both', tight=True)
cb = fig.colorbar(im)
im.set_clim(0, 1.2*σyield)
cb.set_label(r'$σ_{yy}$ [MPa]')
# Load step convergence
# ---------------------
# At the end of the loading step, the plastic strain state and history parameter are updated,
# where use if made of the strain hardening relation for the history variable:
#
# Δκ = √(Δεp_ij Δεp_ij)
Δεp = ns.εp-ns.εp0
Δκ = function.sqrt((Δεp*Δεp).sum((0,1)))
κ0 = gauss.eval(ns.κ0+Δκ, arguments=dict(lhs0=lhs0,lhs=lhs,εp0=εp0,κ0=κ0))
εp0 = gauss.eval(ns.εp, arguments=dict(lhs0=lhs0,lhs=lhs,εp0=εp0,κ0=κ0))
def main(
L: 'domain size' = 4.,
R: 'hole radius' = 1.,
E: "young's modulus" = 1e5,
nu: "poisson's ratio" = 0.3,
T: 'far field traction' = 10,
nr: 'number of h-refinements' = 2,
figures: 'create figures' = True,
):
ns = function.Namespace()
ns.lmbda = E*nu/(1-nu**2)
ns.mu = .5*E/(1+nu)
# create the coarsest level parameter domain
domain0, geometry = mesh.rectilinear( [1,2] )
# create the second-order B-spline basis over the coarsest domain
ns.bsplinebasis = domain0.basis('spline', degree=2)
ns.controlweights = 1,.5+.5/2**.5,.5+.5/2**.5,1,1,1,1,1,1,1,1,1
ns.weightfunc = 'bsplinebasis_n controlweights_n'
ns.nurbsbasis = ns.bsplinebasis * ns.controlweights / ns.weightfunc
# create the isogeometric map
ns.controlpoints = [0,R],[R*(1-2**.5),R],[-R,R*(2**.5-1)],[-R,0],[0,.5*(R+L)],[-.15*(R+L),.5*(R+L)],[-.5*(R+L),.15*(R*L)],[-.5*(R+L),0],[0,L],[-L,L],[-L,L],[-L,0]
ns.x_i = 'nurbsbasis_n controlpoints_ni'
# create the computational domain by h-refinement
domain = domain0.refine(nr)
# create the second-order B-spline basis over the refined domain
ns.bsplinebasis = domain.basis('spline', degree=2)
sqr = domain.integral('(bsplinebasis_n ?lhs_n - weightfunc)^2' @ ns, geometry=ns.x, degree=9)
ns.controlweights = solver.optimize('lhs', sqr)
ns.nurbsbasis = ns.bsplinebasis * ns.controlweights / ns.weightfunc
# prepare the displacement field
ns.ubasis = ns.nurbsbasis.vector(2)
ns.u_i = 'ubasis_ni ?lhs_n'
ns.strain_ij = '(u_i,j + u_j,i) / 2'
ns.stress_ij = 'lmbda strain_kk δ_ij + 2 mu strain_ij'
# construct the exact solution
ns.r2 = 'x_k x_k'
ns.R2 = R**2
ns.T = T
ns.k = (3-nu) / (1+nu) # plane stress parameter
ns.uexact_i = '''(T / 4 mu) <x_0 ((k + 1) / 2 + (1 + k) R2 / r2 + (1 - R2 / r2) (x_0^2 - 3 x_1^2) R2 / r2^2),
x_1 ((k - 3) / 2 + (1 - k) R2 / r2 + (1 - R2 / r2) (3 x_0^2 - x_1^2) R2 / r2^2)>_i'''
ns.strainexact_ij = '(uexact_i,j + uexact_j,i) / 2'
ns.stressexact_ij = 'lmbda strainexact_kk δ_ij + 2 mu strainexact_ij'
# define the linear and bilinear forms
res = domain.integral('ubasis_ni,j stress_ij' @ ns, geometry=ns.x, degree=9)
res -= domain.boundary['right'].integral('ubasis_ni stressexact_ij n_j' @ ns, geometry=ns.x, degree=9)
# compute the constraints vector for the symmetry conditions
sqr = domain.boundary['top,bottom'].integral('(u_i n_i)^2' @ ns, degree=9)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
# solve the system of equations
lhs = solver.solve_linear('lhs', res, constrain=cons)
ns = ns(lhs=lhs)
# post-processing
if figures:
geom, stressxx = domain.simplex.elem_eval([ns.x, 'stress_00' @ ns], ischeme='bezier8', separate=True)
with plot.PyPlot( 'solution', index=nr ) as plt:
plt.mesh( geom, stressxx )
plt.colorbar()
# compute the L2-norm of the error in the stress
err = domain.integrate('(?dstress_ij ?dstress_ij)(dstress_ij = stress_ij - stressexact_ij)' @ ns, geometry=ns.x, ischeme='gauss9')**.5
# compute the mesh parameter (maximum physical distance between knots)
hmax = max(numpy.linalg.norm(v[:,numpy.newaxis]-v, axis=2).max() for v in domain.elem_eval(ns.x, ischeme='bezier2', separate=True))
return err, hmax
def main(nelems: int, etype: str, btype: str, degree: int):
'''
Laplace problem on a unit square.
.. arguments::
nelems [10]
Number of elements along edge.
etype [square]
Type of elements (square/triangle/mixed).
btype [std]
Type of basis function (std/spline), availability depending on the
selected element type.
degree [1]
Polynomial degree.
'''
# A unit square domain is created by calling the
# :func:`nutils.mesh.unitsquare` mesh generator, with the number of elements
# along an edge as the first argument, and the type of elements ("square",
# "triangle", or "mixed") as the second. The result is a topology object
# ``domain`` and a vectored valued geometry function ``geom``.
domain, geom = mesh.unitsquare(nelems, etype)
# To be able to write index based tensor contractions, we need to bundle all
# relevant functions together in a namespace. Here we add the geometry ``x``,
# a scalar ``basis``, and the solution ``u``. The latter is formed by
# contracting the basis with a to-be-determined solution vector ``?lhs``.
ns = function.Namespace()
ns.x = geom
ns.basis = domain.basis(btype, degree=degree)
ns.u = 'basis_n ?lhs_n'
# We are now ready to implement the Laplace equation. In weak form, the
# solution is a scalar field :math:`u` for which:
#
# .. math:: ∀ v: ∫_Ω \frac{dv}{dx_i} \frac{du}{dx_i} - ∫_{Γ_n} v f = 0.
#
# By linearity the test function :math:`v` can be replaced by the basis that
# spans its space. The result is an integral ``res`` that evaluates to a
# vector matching the size of the function space.
res = domain.integral('d(basis_n, x_i) d(u, x_i) J(x)' @ ns,
degree=degree * 2)
res -= domain.boundary['right'].integral(
'basis_n cos(1) cosh(x_1) J(x)' @ ns, degree=degree * 2)
# The Dirichlet constraints are set by finding the coefficients that minimize
# the error:
#
# .. math:: \min_u ∫_{\Gamma_d} (u - u_d)^2
#
# The resulting ``cons`` array holds numerical values for all the entries of
# ``?lhs`` that contribute (up to ``droptol``) to the minimization problem.
# All remaining entries are set to ``NaN``, signifying that these degrees of
# freedom are unconstrained.
sqr = domain.boundary['left'].integral('u^2 J(x)' @ ns, degree=degree * 2)
sqr += domain.boundary['top'].integral(
'(u - cosh(1) sin(x_0))^2 J(x)' @ ns, degree=degree * 2)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
# The unconstrained entries of ``?lhs`` are to be determined such that the
# residual vector evaluates to zero in the corresponding entries. This step
# involves a linearization of ``res``, resulting in a jacobian matrix and
# right hand side vector that are subsequently assembled and solved. The
# resulting ``lhs`` array matches ``cons`` in the constrained entries.
lhs = solver.solve_linear('lhs', res, constrain=cons)
# Once all entries of ``?lhs`` are establised, the corresponding solution can
# be vizualised by sampling values of ``ns.u`` along with physical
# coordinates ``ns.x``, with the solution vector provided via the
# ``arguments`` dictionary. The sample members ``tri`` and ``hull`` provide
# additional inter-point information required for drawing the mesh and
# element outlines.
bezier = domain.sample('bezier', 9)
x, u = bezier.eval(['x', 'u'] @ ns, lhs=lhs)
export.triplot('solution.png', x, u, tri=bezier.tri, hull=bezier.hull)
# To confirm that our computation is correct, we use our knowledge of the
# analytical solution to evaluate the L2-error of the discrete result.
err = domain.integral('(u - sin(x_0) cosh(x_1))^2 J(x)' @ ns,
degree=degree * 2).eval(lhs=lhs)**.5
treelog.user('L2 error: {:.2e}'.format(err))
return cons, lhs, err
def main(
L: 'domain size' = 4.,
R: 'hole radius' = 1.,
E: "young's modulus" = 1e5,
nu: "poisson's ratio" = 0.3,
T: 'far field traction' = 10,
nr: 'number of h-refinements' = 2,
withplots: 'create plots' = True,
):
ns = function.Namespace()
ns.lmbda = E * nu / (1 - nu**2)
ns.mu = .5 * E / (1 + nu)
# create the coarsest level parameter domain
domain0, geometry = mesh.rectilinear([1, 2])
# create the second-order B-spline basis over the coarsest domain
ns.bsplinebasis = domain0.basis('spline', degree=2)
ns.controlweights = 1, .5 + .5 / 2**.5, .5 + .5 / 2**.5, 1, 1, 1, 1, 1, 1, 1, 1, 1
ns.weightfunc = 'bsplinebasis_n controlweights_n'
ns.nurbsbasis = ns.bsplinebasis * ns.controlweights / ns.weightfunc
# create the isogeometric map
ns.controlpoints = [0, R], [R * (1 - 2**.5), R], [-R, R * (2**.5 - 1)], [
-R, 0
], [0, .5 * (R + L)], [-.15 * (R + L),
.5 * (R + L)], [-.5 * (R + L), .15 * (R * L)
], [-.5 * (R + L),
0], [0, L], [-L,
L], [-L,
L], [-L, 0]
ns.x_i = 'nurbsbasis_n controlpoints_ni'
# create the computational domain by h-refinement
domain = domain0.refine(nr)
# create the second-order B-spline basis over the refined domain
ns.bsplinebasis = domain.basis('spline', degree=2)
sqr = domain.integral('(bsplinebasis_n ?lhs_n - weightfunc)^2' @ ns,
geometry=ns.x,
degree=9)
ns.controlweights = solver.optimize('lhs', sqr)
ns.nurbsbasis = ns.bsplinebasis * ns.controlweights / ns.weightfunc
# prepare the displacement field
ns.ubasis = ns.nurbsbasis.vector(2)
ns.u_i = 'ubasis_ni ?lhs_n'
ns.strain_ij = '(u_i,j + u_j,i) / 2'
ns.stress_ij = 'lmbda strain_kk δ_ij + 2 mu strain_ij'
# construct the exact solution
ns.r2 = 'x_k x_k'
ns.R2 = R**2
ns.T = T
ns.k = (3 - nu) / (1 + nu) # plane stress parameter
ns.uexact_i = '''(T / 4 mu) <x_0 ((k + 1) / 2 + (1 + k) R2 / r2 + (1 - R2 / r2) (x_0^2 - 3 x_1^2) R2 / r2^2),
x_1 ((k - 3) / 2 + (1 - k) R2 / r2 + (1 - R2 / r2) (3 x_0^2 - x_1^2) R2 / r2^2)>_i'''
ns.strainexact_ij = '(uexact_i,j + uexact_j,i) / 2'
ns.stressexact_ij = 'lmbda strainexact_kk δ_ij + 2 mu strainexact_ij'
# define the linear and bilinear forms
res = domain.integral('ubasis_ni,j stress_ij' @ ns,
geometry=ns.x,
degree=9)
res -= domain.boundary['right'].integral(
'ubasis_ni stressexact_ij n_j' @ ns, geometry=ns.x, degree=9)
# compute the constraints vector for the symmetry conditions
sqr = domain.boundary['top,bottom'].integral('(u_i n_i)^2' @ ns, degree=9)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
# solve the system of equations
lhs = solver.solve_linear('lhs', res, constrain=cons)
ns |= dict(lhs=lhs)
# post-processing
if withplots:
geom, stressxx = domain.simplex.elem_eval([ns.x, 'stress_00' @ ns],
ischeme='bezier8',
separate=True)
with plot.PyPlot('solution', index=nr) as plt:
plt.mesh(geom, stressxx)
plt.colorbar()
# compute the L2-norm of the error in the stress
err = domain.integrate(
'?dstress_ij ?dstress_ij | ?dstress_ij = stress_ij - stressexact_ij'
@ ns,
geometry=ns.x,
ischeme='gauss9')**.5
# compute the mesh parameter (maximum physical distance between knots)
hmax = max(
numpy.linalg.norm(v[:, numpy.newaxis] - v, axis=2).max()
for v in domain.elem_eval(ns.x, ischeme='bezier2', separate=True))
return err, hmax
def main(
nelems: 'number of elements' = 12,
viscosity: 'fluid viscosity' = 1e-2,
density: 'fluid density' = 1,
tol: 'solver tolerance' = 1e-12,
rotation: 'cylinder rotation speed' = 0,
timestep: 'time step' = 1/24,
maxradius: 'approximate domain size' = 25,
tmax: 'end time' = numpy.inf,
degree: 'polynomial degree' = 2,
figures: 'create figures' = True,
):
log.user('reynolds number: {:.1f}'.format(density / viscosity)) # based on unit length and velocity
# create namespace
ns = function.Namespace()
ns.uinf = 1, 0
ns.density = density
ns.viscosity = viscosity
# construct mesh
rscale = numpy.pi / nelems
melems = numpy.ceil(numpy.log(2*maxradius) / rscale).astype(int)
log.info('creating {}x{} mesh, outer radius {:.2f}'.format(melems, 2*nelems, .5*numpy.exp(rscale*melems)))
domain, x0 = mesh.rectilinear([range(melems+1),numpy.linspace(0,2*numpy.pi,2*nelems+1)], periodic=(1,))
rho, phi = x0
phi += 1e-3 # tiny nudge (0.057 deg) to break element symmetry
radius = .5 * function.exp(rscale * rho)
ns.x = radius * function.trigtangent(phi)
domain = domain.withboundary(inner='left', inflow=domain.boundary['right'].select(-ns.uinf.dotnorm(ns.x), ischeme='gauss1'))
# prepare bases (using piola transformation to maintain u/p compatibility)
J = ns.x.grad(x0)
detJ = function.determinant(J)
ns.unbasis, ns.utbasis, ns.pbasis = function.chain([ # compatible spaces using piola transformation
domain.basis('spline', degree=(degree+1,degree), removedofs=((0,),None))[:,_] * J[:,0] / detJ,
domain.basis('spline', degree=(degree,degree+1))[:,_] * J[:,1] / detJ,
domain.basis('spline', degree=degree) / detJ,
])
ns.ubasis_ni = 'unbasis_ni + utbasis_ni'
# populate namespace
ns.u_i = 'ubasis_ni ?lhs_n'
ns.p = 'pbasis_n ?lhs_n'
ns.sigma_ij = 'viscosity (u_i,j + u_j,i) - p δ_ij'
ns.hinner = 2 * numpy.pi / nelems
ns.c = 5 * (degree+1) / ns.hinner
ns.nietzsche_ni = 'viscosity (c ubasis_ni - (ubasis_ni,j + ubasis_nj,i) n_j)'
ns.ucyl = -.5 * rotation * function.trignormal(phi)
# create residual vector components
res = domain.integral('ubasis_ni,j sigma_ij + pbasis_n u_k,k' @ ns, geometry=ns.x, degree=2*(degree+1))
res += domain.boundary['inner'].integral('nietzsche_ni (u_i - ucyl_i)' @ ns, geometry=ns.x, degree=2*(degree+1))
oseen = domain.integral('density ubasis_ni u_i,j uinf_j' @ ns, geometry=ns.x, degree=2*(degree+1))
convec = domain.integral('density ubasis_ni u_i,j u_j' @ ns, geometry=ns.x, degree=3*(degree+1))
inertia = domain.integral('density ubasis_ni u_i' @ ns, geometry=ns.x, degree=2*(degree+1))
# constrain full velocity vector at inflow
sqr = domain.boundary['inflow'].integral('(u_i - uinf_i) (u_i - uinf_i)' @ ns, geometry=ns.x, degree=9)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
# solve unsteady navier-stokes equations, starting from stationary oseen flow
lhs0 = solver.solve_linear('lhs', res+oseen, constrain=cons)
makeplots = MakePlots(domain, ns, timestep=timestep, rotation=rotation) if figures else lambda *args: None
for istep, lhs in log.enumerate('timestep', solver.impliciteuler('lhs', residual=res+convec, inertia=inertia, lhs0=lhs0, timestep=timestep, constrain=cons, newtontol=1e-10)):
makeplots(lhs)
if istep * timestep >= tmax:
break
return lhs0, lhs
def main(etype: str, btype: str, degree: int, nrefine: int):
'''
Adaptively refined Laplace problem on an L-shaped domain.
.. arguments::
etype [square]
Type of elements (square/triangle/mixed).
btype [h-std]
Type of basis function (h/th-std/spline), with availability depending on
the configured element type.
degree [2]
Polynomial degree
nrefine [5]
Number of refinement steps to perform.
'''
domain, geom = mesh.unitsquare(2, etype)
x, y = geom - .5
exact = (x**2 + y**2)**(1 / 3) * function.cos(
function.arctan2(y + x, y - x) * (2 / 3))
domain = domain.trim(exact - 1e-15, maxrefine=0)
linreg = util.linear_regressor()
with treelog.iter.fraction('level', range(nrefine + 1)) as lrange:
for irefine in lrange:
if irefine:
refdom = domain.refined
ns.refbasis = refdom.basis(btype, degree=degree)
indicator = refdom.integral(
'd(refbasis_n, x_k) d(u, x_k) J(x)' @ ns,
degree=degree * 2).eval(lhs=lhs)
indicator -= refdom.boundary.integral(
'refbasis_n d(u, x_k) n(x_k) J(x)' @ ns,
degree=degree * 2).eval(lhs=lhs)
supp = ns.refbasis.get_support(
indicator**2 > numpy.mean(indicator**2))
domain = domain.refined_by(refdom.transforms[supp])
ns = function.Namespace()
ns.x = geom
ns.basis = domain.basis(btype, degree=degree)
ns.u = 'basis_n ?lhs_n'
ns.du = ns.u - exact
sqr = domain.boundary['trimmed'].integral('u^2 J(x)' @ ns,
degree=degree * 2)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
sqr = domain.boundary.integral('du^2 J(x)' @ ns, degree=7)
cons = solver.optimize('lhs', sqr, droptol=1e-15, constrain=cons)
res = domain.integral('d(basis_n, x_k) d(u, x_k) J(x)' @ ns,
degree=degree * 2)
lhs = solver.solve_linear('lhs', res, constrain=cons)
ndofs = len(ns.basis)
error = domain.integral('<du^2, sum:k(d(du, x_k)^2)>_i J(x)' @ ns,
degree=7).eval(lhs=lhs)**.5
rate, offset = linreg.add(numpy.log(len(ns.basis)),
numpy.log(error))
treelog.user(
'ndofs: {ndofs}, L2 error: {error[0]:.2e} ({rate[0]:.2f}), H1 error: {error[1]:.2e} ({rate[1]:.2f})'
.format(ndofs=len(ns.basis), error=error, rate=rate))
bezier = domain.sample('bezier', 9)
x, u, du = bezier.eval(['x', 'u', 'du'] @ ns, lhs=lhs)
export.triplot('sol.png', x, u, tri=bezier.tri, hull=bezier.hull)
export.triplot('err.png', x, du, tri=bezier.tri, hull=bezier.hull)
return ndofs, error, lhs
def neutrondiffusion(mat_flag='scatterer',
BC_flag='vacuum',
bigsquare=1,
smallsquare=.1,
degree=1,
basis='lagrange'):
W = bigsquare #width of outer box
Wi = smallsquare #width of inner box
nelems = int((2 * 1 * W / Wi))
if mat_flag == 'scatterer':
sigt = 2
sigs = 1.99
elif mat_flag == 'reflector':
sigt = 2
sigs = 1.8
elif mat_flag == 'absorber':
sigt = 10
sigs = 2
elif mat_flag == 'air':
sigt = .01
sigs = .006
topo, geom = mesh.unitsquare(nelems,
'square') #unit square centred at (0.5, 0.5)
ns = function.Namespace()
ns.basis = topo.basis(basis, degree=degree)
ns.x = W * geom #scales the unit square to our physical square
ns.f = function.max(
function.abs(ns.x[0] - W / 2),
function.abs(ns.x[1] - W / 2)) #level set function for inner square
inner, outer = function.partition(
ns.f, Wi / 2) #indicator function for inner square and outer square
ns.phi = 'basis_A ?dofs_A'
ns.SIGs = sigs * outer #scattering cross-section
ns.SIGt = 0.1 * inner + sigt * outer #total cross-section
ns.SIGa = 'SIGt - SIGs' #absorption cross-section
ns.D = '1 / (3 SIGt)' #diffusion co-efficient
ns.Q = inner #source term
if BC_flag == 'vacuum':
sqr = topo.boundary.integral('(phi - 0)^2 J(x)' @ ns,
degree=degree * 2)
cons = solver.optimize(
'dofs', sqr,
droptol=1e-14) #this applies the boundary condition to u
#residual
res = topo.integral(
'(D basis_i,j phi_,j + SIGa basis_i phi - basis_i Q) J(x)' @ ns,
degree=degree * 2)
#solve for degrees of freedom
dofs = solver.solve_linear('dofs', res, constrain=cons)
elif BC_flag == 'reflecting':
#residual
res = topo.integral(
'(D basis_i,j phi_,j + SIGa basis_i phi - basis_i Q) J(x)' @ ns,
degree=degree * 2)
#solve for degrees of freedom
dofs = solver.solve_linear('dofs', res)
#select lower triangular half of square domain. Diagonal is one of its boundaries
triang = topo.trim('x_0 - x_1' @ ns, maxrefine=5)
triangbnd = triang.boundary #select boundary of lower triangle
# eval the vertices of the boundary elements of lower triangle:
verts = triangbnd.sample(*element.parse_legacy_ischeme("vertex")).eval(
'x_i' @ ns)
# now select the verts of the boundary
diag = topology.SubsetTopology(triangbnd, [
triangbnd.references[i] if
(verts[2 * i][0] == verts[2 * i][1] and verts[2 * i + 1][0]
== verts[2 * i + 1][1]) else triangbnd.references[i].empty
for i in range(len(triangbnd.references))
])
bezier_diag = diag.sample('bezier', degree + 1)
x_diag = bezier_diag.eval('(x_0^2 + x_1^2)^(1 / 2)' @ ns)
phi_diag = bezier_diag.eval('phi' @ ns, dofs=dofs)
bezier_bottom = topo.boundary['bottom'].sample('bezier', degree + 1)
x_bottom = bezier_bottom.eval('x_0' @ ns)
phi_bottom = bezier_bottom.eval('phi' @ ns, dofs=dofs)
fig_bottom = plt.figure(0)
ax_bottom = fig_bottom.add_subplot(111)
plt.plot(x_bottom, phi_bottom)
ax_bottom.set_title('Scalar flux along bottom for %s with %s BCs' %
(mat_flag, BC_flag))
ax_bottom.set_xlabel('x')
ax_bottom.set_ylabel('$\\phi(x)$')
fig_diag = plt.figure(1)
ax_diag = fig_diag.add_subplot(111)
plt.plot(x_diag, phi_diag)
ax_diag.set_title('Scalar flux along diagonal for %s with %s BCs' %
(mat_flag, BC_flag))
ax_diag.set_xlabel('$\\sqrt{x^2 + y^2}$')
ax_diag.set_ylabel('$\\phi(x = y)$')
return fig_bottom, fig_diag
def main(nelems: int, etype: str, btype: str, degree: int, traction: float,
maxrefine: int, radius: float, poisson: float):
'''
Horizontally loaded linear elastic plate with FCM hole.
.. arguments::
nelems [9]
Number of elements along edge.
etype [square]
Type of elements (square/triangle/mixed).
btype [std]
Type of basis function (std/spline), with availability depending on the
selected element type.
degree [2]
Polynomial degree.
traction [.1]
Far field traction (relative to Young's modulus).
maxrefine [2]
Number or refinement levels used for the finite cell method.
radius [.5]
Cut-out radius.
poisson [.3]
Poisson's ratio, nonnegative and strictly smaller than 1/2.
'''
domain0, geom = mesh.unitsquare(nelems, etype)
domain = domain0.trim(function.norm2(geom) - radius, maxrefine=maxrefine)
ns = function.Namespace()
ns.x = geom
ns.lmbda = 2 * poisson
ns.mu = 1 - poisson
ns.ubasis = domain.basis(btype, degree=degree).vector(2)
ns.u_i = 'ubasis_ni ?lhs_n'
ns.X_i = 'x_i + u_i'
ns.strain_ij = '(d(u_i, x_j) + d(u_j, x_i)) / 2'
ns.stress_ij = 'lmbda strain_kk δ_ij + 2 mu strain_ij'
ns.r2 = 'x_k x_k'
ns.R2 = radius**2 / ns.r2
ns.k = (3 - poisson) / (1 + poisson) # plane stress parameter
ns.scale = traction * (1 + poisson) / 2
ns.uexact_i = 'scale (x_i ((k + 1) (0.5 + R2) + (1 - R2) R2 (x_0^2 - 3 x_1^2) / r2) - 2 δ_i1 x_1 (1 + (k - 1 + R2) R2))'
ns.du_i = 'u_i - uexact_i'
sqr = domain.boundary['left,bottom'].integral('(u_i n_i)^2 J(x)' @ ns,
degree=degree * 2)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
sqr = domain.boundary['top,right'].integral('du_k du_k J(x)' @ ns,
degree=20)
cons = solver.optimize('lhs', sqr, droptol=1e-15, constrain=cons)
res = domain.integral('d(ubasis_ni, x_j) stress_ij J(x)' @ ns,
degree=degree * 2)
lhs = solver.solve_linear('lhs', res, constrain=cons)
bezier = domain.sample('bezier', 5)
X, stressxx = bezier.eval(['X', 'stress_00'] @ ns, lhs=lhs)
export.triplot('stressxx.png',
X,
stressxx,
tri=bezier.tri,
hull=bezier.hull)
err = domain.integral('<du_k du_k, sum:ij(d(du_i, x_j)^2)>_n J(x)' @ ns,
degree=max(degree, 3) * 2).eval(lhs=lhs)**.5
treelog.user('errors: L2={:.2e}, H1={:.2e}'.format(*err))
return err, cons, lhs
def main(
nelems: 'number of elements' = 20,
epsilon: 'epsilon, 0 for automatic (based on nelems)' = 0,
timestep: 'time step' = .01,
maxtime: 'end time' = 1.,
theta: 'contact angle (degrees)' = 90,
init: 'initial condition (random/bubbles)' = 'random',
withplots: 'create plots' = True,
):
mineps = 1./nelems
if not epsilon:
log.info('setting epsilon={}'.format(mineps))
epsilon = mineps
elif epsilon < mineps:
log.warning('epsilon under crititical threshold: {} < {}'.format(epsilon, mineps))
# create namespace
ns = function.Namespace()
ns.epsilon = epsilon
ns.ewall = .5 * numpy.cos( theta * numpy.pi / 180 )
# construct mesh
xnodes = ynodes = numpy.linspace(0,1,nelems+1)
domain, ns.x = mesh.rectilinear( [ xnodes, ynodes ] )
# prepare bases
ns.cbasis, ns.mubasis = function.chain([
domain.basis('spline', degree=2),
domain.basis('spline', degree=2)
])
# polulate namespace
ns.c = 'cbasis_n ?lhs_n'
ns.c0 = 'cbasis_n ?lhs0_n'
ns.mu = 'mubasis_n ?lhs_n'
ns.f = '(6 c0 - 2 c0^3 - 4 c) / epsilon^2'
# construct initial condition
if init == 'random':
numpy.random.seed(0)
lhs0 = numpy.random.normal(0, .5, ns.cbasis.shape)
elif init == 'bubbles':
R1 = .25
R2 = numpy.sqrt(.5) * R1 # area2 = .5 * area1
ns.cbubble1 = function.tanh((R1-function.norm2(ns.x-(.5+R2/numpy.sqrt(2)+.8*ns.epsilon)))/ns.epsilon)
ns.cbubble2 = function.tanh((R2-function.norm2(ns.x-(.5-R1/numpy.sqrt(2)-.8*ns.epsilon)))/ns.epsilon)
sqr = domain.integral('(c - cbubble1 - cbubble2 - 1)^2 + mu^2' @ ns, geometry=ns.x, degree=4)
lhs0 = solver.optimize('lhs', sqr)
else:
raise Exception( 'unknown init %r' % init )
# construct residual
res = domain.integral('epsilon^2 cbasis_n,k mu_,k + mubasis_n (mu + f) - mubasis_n,k c_,k' @ ns, geometry=ns.x, degree=4)
res -= domain.boundary.integral('mubasis_n ewall' @ ns, geometry=ns.x, degree=4)
inertia = domain.integral('cbasis_n c' @ ns, geometry=ns.x, degree=4)
# solve time dependent problem
nsteps = numeric.round(maxtime/timestep)
makeplots = MakePlots(domain, nsteps, ns) if withplots else lambda *args: None
for istep, lhs in log.enumerate('timestep', solver.impliciteuler('lhs', target0='lhs0', residual=res, inertia=inertia, timestep=timestep, lhs0=lhs0)):
makeplots(lhs)
if istep == nsteps:
break
return lhs0, lhs
