Newer
Older
# Copyright (C) 2007 Andreas Kloeckner
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
import pyopencl as cl
import pyopencl.array
import pyopencl.clmath
from grudge import bind, sym
logging.basicConfig(level=logging.INFO)
def main(ctx_factory, dim=2, order=4, visualize=True):
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
# domain side [-d/2, d/2]^dim
d = 1.0
# number of points in each dimension
npoints = 20
# grid spacing
h = d / npoints
# cfl?
# final time
final_time = 1.0
norm_c = la.norm(c)
# flux
flux_type = "central"
# compute number of steps
dt = dt_factor * h / order**2
nsteps = int(final_time // dt) + 1
dt = final_time/nsteps + 1.0e-15
from meshmode.mesh.generation import generate_box_mesh
mesh = generate_box_mesh(
[np.linspace(-d/2, d/2, npoints) for _ in range(dim)],
order=order)
from grudge import DGDiscretizationWithBoundaries
Andreas Klöckner
committed
discr = DGDiscretizationWithBoundaries(cl_ctx, mesh, order=order)
volume_discr = discr.discr_from_dd(sym.DD_VOLUME)
def u_analytic(x, t=None):
if t is None:
t = sym.var("t", sym.DD_SCALAR)
return f(-np.dot(c, x) / norm_c + t * norm_c)
from grudge.models.advection import WeakAdvectionOperator
op = WeakAdvectionOperator(c,
inflow_u=u_analytic(sym.nodes(dim, sym.BTAG_ALL)),
flux_type=flux_type)
bound_op = bind(discr, op.sym_operator())
u = bind(discr, u_analytic(sym.nodes(dim)))(queue, t=0)
def rhs(t, u):
return bound_op(queue, t=t, u=u)
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("u", dt, u, rhs)
if dim == 1:
import matplotlib.pyplot as pt
pt.figure(figsize=(8, 8), dpi=300)
volume_x = volume_discr.nodes().get(queue)
else:
from grudge.shortcuts import make_visualizer
vis = make_visualizer(discr, vis_order=order)
def plot_solution(evt):
if not visualize:
return
if dim == 1:
u = event.state_component.get(queue)
u_ = bind(discr, u_analytic(sym.nodes(dim)))(queue, t=evt.t).get(queue)
pt.plot(volume_x[0], u, 'o')
pt.plot(volume_x[0], u_, "k.")
pt.xlabel("$x$")
pt.ylabel("$u$")
pt.title("t = {:.2f}".format(evt.t))
pt.savefig("fld-weak-%04d.png" % step)
pt.clf()
else:
vis.write_vtk_file("fld-weak-%04d.vtu" % step, [
step = 0
norm = bind(discr, sym.norm(2, sym.var("u")))
for event in dt_stepper.run(t_end=final_time):
if not isinstance(event, dt_stepper.StateComputed):
continue
step += 1
norm_u = norm(queue, u=event.state_component)
logger.info("[%04d] t = %.5f |u| = %.5e", step, event.t, norm_u)
plot_solution(event)
parser = argparse.ArgumentParser()
parser.add_argument("--dim", default=2, type=int)
args = parser.parse_args()
main(cl.create_some_context,
dim=args.dim)