# 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 numpy.linalg as la import pyopencl as cl import pyopencl.array import pyopencl.clmath from grudge import bind, sym import logging logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def main(ctx_factory, dim=1, order=4, visualize=True): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) # {{{ parameters # domain side [-d/2, d/2]^dim d = 1.0 # number of points in each dimension npoints = 20 # grid spacing h = d / npoints # cfl? dt_factor = 2.0 # final time final_time = 1.0 # velocity field c = np.array([0.5] * dim) 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 # }}} # {{{ discretization 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 discr = DGDiscretizationWithBoundaries(cl_ctx, mesh, order=order) volume_discr = discr.discr_from_dd(sym.DD_VOLUME) faces_discr = discr.discr_from_dd(sym.FACE_RESTR_INTERIOR) # }}} # {{{ solve advection def f(x): return sym.sin(3 * x) 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, [ ("u", evt.state_component) ], overwrite=True) 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) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser() parser.add_argument("--dim", default=2, type=int) args = parser.parse_args() main(cl.create_some_context, dim=args.dim)