def test_is_affine_group_check(mesh_name): order = 4 nelements = 16 if mesh_name.startswith("box"): dim = int(mesh_name[-2]) is_affine = True mesh = mgen.generate_regular_rect_mesh( a=(-0.5, ) * dim, b=(0.5, ) * dim, nelements_per_axis=(nelements, ) * dim, order=order) elif mesh_name.startswith("warped_box"): dim = int(mesh_name[-2]) is_affine = False mesh = mgen.generate_warped_rect_mesh(dim, order, nelements_side=nelements) elif mesh_name == "cross_warped_box": dim = 2 is_affine = False mesh = _generate_cross_warped_rect_mesh(dim, order, nelements) elif mesh_name == "circle": is_affine = False mesh = mgen.make_curve_mesh(lambda t: mgen.ellipse(1.0, t), np.linspace(0.0, 1.0, nelements + 1), order=order) elif mesh_name == "ellipse": is_affine = False mesh = mgen.make_curve_mesh(lambda t: mgen.ellipse(2.0, t), np.linspace(0.0, 1.0, nelements + 1), order=order) elif mesh_name == "sphere": is_affine = False mesh = mgen.generate_icosphere(r=1.0, order=order) elif mesh_name == "torus": is_affine = False mesh = mgen.generate_torus(10.0, 2.0, order=order) else: raise ValueError(f"unknown mesh name: {mesh_name}") assert all(grp.is_affine for grp in mesh.groups) == is_affine
def main(ctx_factory, dim=2, order=4, product_tag=None, visualize=False): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) actx = PyOpenCLArrayContext(queue) # {{{ parameters # sphere radius radius = 1.0 # sphere resolution resolution = 64 if dim == 2 else 1 # cfl dt_factor = 2.0 # final time final_time = np.pi # velocity field sym_x = sym.nodes(dim) c = make_obj_array([-sym_x[1], sym_x[0], 0.0])[:dim] # flux flux_type = "lf" # }}} # {{{ discretization if dim == 2: from meshmode.mesh.generation import make_curve_mesh, ellipse mesh = make_curve_mesh(lambda t: radius * ellipse(1.0, t), np.linspace(0.0, 1.0, resolution + 1), order) elif dim == 3: from meshmode.mesh.generation import generate_icosphere mesh = generate_icosphere(radius, order=4 * order, uniform_refinement_rounds=resolution) else: raise ValueError("unsupported dimension") discr_tag_to_group_factory = {} if product_tag == "none": product_tag = None else: product_tag = dof_desc.DISCR_TAG_QUAD from meshmode.discretization.poly_element import \ PolynomialWarpAndBlendGroupFactory, \ QuadratureSimplexGroupFactory discr_tag_to_group_factory[dof_desc.DISCR_TAG_BASE] = \ PolynomialWarpAndBlendGroupFactory(order) if product_tag: discr_tag_to_group_factory[product_tag] = \ QuadratureSimplexGroupFactory(order=4*order) from grudge import DiscretizationCollection discr = DiscretizationCollection( actx, mesh, discr_tag_to_group_factory=discr_tag_to_group_factory) volume_discr = discr.discr_from_dd(dof_desc.DD_VOLUME) logger.info("ndofs: %d", volume_discr.ndofs) logger.info("nelements: %d", volume_discr.mesh.nelements) # }}} # {{{ symbolic operators def f_initial_condition(x): return x[0] from grudge.models.advection import SurfaceAdvectionOperator op = SurfaceAdvectionOperator(c, flux_type=flux_type, quad_tag=product_tag) bound_op = bind(discr, op.sym_operator()) u0 = bind(discr, f_initial_condition(sym_x))(actx, t=0) def rhs(t, u): return bound_op(actx, t=t, u=u) # check velocity is tangential sym_normal = sym.surface_normal(dim, dim=dim - 1, dd=dof_desc.DD_VOLUME).as_vector() error = bind(discr, sym.norm(2, c.dot(sym_normal)))(actx) logger.info("u_dot_n: %.5e", error) # }}} # {{{ time stepping # compute time step h_min = bind(discr, sym.h_max_from_volume(discr.ambient_dim, dim=discr.dim))(actx) dt = dt_factor * h_min / order**2 nsteps = int(final_time // dt) + 1 dt = final_time / nsteps + 1.0e-15 logger.info("dt: %.5e", dt) logger.info("nsteps: %d", nsteps) from grudge.shortcuts import set_up_rk4 dt_stepper = set_up_rk4("u", dt, u0, rhs) plot = Plotter(actx, discr, order, visualize=visualize) norm = bind(discr, sym.norm(2, sym.var("u"))) norm_u = norm(actx, u=u0) step = 0 event = dt_stepper.StateComputed(0.0, 0, 0, u0) plot(event, "fld-surface-%04d" % 0) if visualize and dim == 3: from grudge.shortcuts import make_visualizer vis = make_visualizer(discr) vis.write_vtk_file("fld-surface-velocity.vtu", [("u", bind(discr, c)(actx)), ("n", bind(discr, sym_normal)(actx))], overwrite=True) df = dof_desc.DOFDesc(FACE_RESTR_INTERIOR) face_discr = discr.connection_from_dds(dof_desc.DD_VOLUME, df).to_discr face_normal = bind( discr, sym.normal(df, face_discr.ambient_dim, dim=face_discr.dim))(actx) from meshmode.discretization.visualization import make_visualizer vis = make_visualizer(actx, face_discr) vis.write_vtk_file("fld-surface-face-normals.vtu", [("n", face_normal)], overwrite=True) for event in dt_stepper.run(t_end=final_time, max_steps=nsteps + 1): if not isinstance(event, dt_stepper.StateComputed): continue step += 1 if step % 10 == 0: norm_u = norm(actx, u=event.state_component) plot(event, "fld-surface-%04d" % step) logger.info("[%04d] t = %.5f |u| = %.5e", step, event.t, norm_u) plot(event, "fld-surface-%04d" % step)
def curve_func(self, x): return ellipse(3, x)
def curve_fn(self): from meshmode.mesh.generation import ellipse return lambda t: self.radius * ellipse(self.aspect_ratio, t)
def run_exterior_stokes_2d(ctx_factory, nelements, mesh_order=4, target_order=4, qbx_order=4, fmm_order=10, mu=1, circle_rad=1.5, do_plot=False): # This program tests an exterior Stokes flow in 2D using the # compound representation given in Hsiao & Kress, # ``On an integral equation for the two-dimensional exterior Stokes problem,'' # Applied Numerical Mathematics 1 (1985). logging.basicConfig(level=logging.INFO) cl_ctx = cl.create_some_context() queue = cl.CommandQueue(cl_ctx) ovsmp_target_order = 4 * target_order from meshmode.mesh.generation import ( # noqa make_curve_mesh, starfish, ellipse, drop) mesh = make_curve_mesh(lambda t: circle_rad * ellipse(1, t), np.linspace(0, 1, nelements + 1), target_order) coarse_density_discr = Discretization( cl_ctx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) from pytential.qbx import QBXLayerPotentialSource target_association_tolerance = 0.05 qbx, _ = QBXLayerPotentialSource( coarse_density_discr, fine_order=ovsmp_target_order, qbx_order=qbx_order, fmm_order=fmm_order, target_association_tolerance=target_association_tolerance, _expansions_in_tree_have_extent=True, ).with_refinement() density_discr = qbx.density_discr normal = bind(density_discr, sym.normal(2).as_vector())(queue) path_length = bind(density_discr, sym.integral(2, 1, 1))(queue) # {{{ describe bvp from pytential.symbolic.stokes import StressletWrapper, StokesletWrapper dim = 2 cse = sym.cse sigma_sym = sym.make_sym_vector("sigma", dim) meanless_sigma_sym = cse(sigma_sym - sym.mean(2, 1, sigma_sym)) int_sigma = sym.Ones() * sym.integral(2, 1, sigma_sym) nvec_sym = sym.make_sym_vector("normal", dim) mu_sym = sym.var("mu") # -1 for interior Dirichlet # +1 for exterior Dirichlet loc_sign = 1 stresslet_obj = StressletWrapper(dim=2) stokeslet_obj = StokesletWrapper(dim=2) bdry_op_sym = (-loc_sign * 0.5 * sigma_sym - stresslet_obj.apply( sigma_sym, nvec_sym, mu_sym, qbx_forced_limit='avg') + stokeslet_obj.apply( meanless_sigma_sym, mu_sym, qbx_forced_limit='avg') - (0.5 / np.pi) * int_sigma) # }}} bound_op = bind(qbx, bdry_op_sym) # {{{ fix rhs and solve def fund_soln(x, y, loc, strength): #with direction (1,0) for point source r = cl.clmath.sqrt((x - loc[0])**2 + (y - loc[1])**2) scaling = strength / (4 * np.pi * mu) xcomp = (-cl.clmath.log(r) + (x - loc[0])**2 / r**2) * scaling ycomp = ((x - loc[0]) * (y - loc[1]) / r**2) * scaling return [xcomp, ycomp] def rotlet_soln(x, y, loc): r = cl.clmath.sqrt((x - loc[0])**2 + (y - loc[1])**2) xcomp = -(y - loc[1]) / r**2 ycomp = (x - loc[0]) / r**2 return [xcomp, ycomp] def fund_and_rot_soln(x, y, loc, strength): #with direction (1,0) for point source r = cl.clmath.sqrt((x - loc[0])**2 + (y - loc[1])**2) scaling = strength / (4 * np.pi * mu) xcomp = ((-cl.clmath.log(r) + (x - loc[0])**2 / r**2) * scaling - (y - loc[1]) * strength * 0.125 / r**2 + 3.3) ycomp = (((x - loc[0]) * (y - loc[1]) / r**2) * scaling + (x - loc[0]) * strength * 0.125 / r**2 + 1.5) return [xcomp, ycomp] nodes = density_discr.nodes().with_queue(queue) fund_soln_loc = np.array([0.5, -0.2]) strength = 100. bc = fund_and_rot_soln(nodes[0], nodes[1], fund_soln_loc, strength) omega_sym = sym.make_sym_vector("omega", dim) u_A_sym_bdry = stokeslet_obj.apply( # noqa: N806 omega_sym, mu_sym, qbx_forced_limit=1) omega = [ cl.array.to_device(queue, (strength / path_length) * np.ones(len(nodes[0]))), cl.array.to_device(queue, np.zeros(len(nodes[0]))) ] bvp_rhs = bind(qbx, sym.make_sym_vector("bc", dim) + u_A_sym_bdry)(queue, bc=bc, mu=mu, omega=omega) gmres_result = gmres(bound_op.scipy_op(queue, "sigma", np.float64, mu=mu, normal=normal), bvp_rhs, x0=bvp_rhs, tol=1e-9, progress=True, stall_iterations=0, hard_failure=True) # }}} # {{{ postprocess/visualize sigma = gmres_result.solution sigma_int_val_sym = sym.make_sym_vector("sigma_int_val", 2) int_val = bind(qbx, sym.integral(2, 1, sigma_sym))(queue, sigma=sigma) int_val = -int_val / (2 * np.pi) print("int_val = ", int_val) u_A_sym_vol = stokeslet_obj.apply( # noqa: N806 omega_sym, mu_sym, qbx_forced_limit=2) representation_sym = ( -stresslet_obj.apply(sigma_sym, nvec_sym, mu_sym, qbx_forced_limit=2) + stokeslet_obj.apply(meanless_sigma_sym, mu_sym, qbx_forced_limit=2) - u_A_sym_vol + sigma_int_val_sym) nsamp = 30 eval_points_1d = np.linspace(-3., 3., nsamp) eval_points = np.zeros((2, len(eval_points_1d)**2)) eval_points[0, :] = np.tile(eval_points_1d, len(eval_points_1d)) eval_points[1, :] = np.repeat(eval_points_1d, len(eval_points_1d)) def circle_mask(test_points, radius): return (test_points[0, :]**2 + test_points[1, :]**2 > radius**2) def outside_circle(test_points, radius): mask = circle_mask(test_points, radius) return np.array([row[mask] for row in test_points]) eval_points = outside_circle(eval_points, radius=circle_rad) from pytential.target import PointsTarget vel = bind((qbx, PointsTarget(eval_points)), representation_sym)(queue, sigma=sigma, mu=mu, normal=normal, sigma_int_val=int_val, omega=omega) print("@@@@@@@@") fplot = FieldPlotter(np.zeros(2), extent=6, npoints=100) plot_pts = outside_circle(fplot.points, radius=circle_rad) plot_vel = bind((qbx, PointsTarget(plot_pts)), representation_sym)(queue, sigma=sigma, mu=mu, normal=normal, sigma_int_val=int_val, omega=omega) def get_obj_array(obj_array): return make_obj_array([ary.get() for ary in obj_array]) exact_soln = fund_and_rot_soln(cl.array.to_device(queue, eval_points[0]), cl.array.to_device(queue, eval_points[1]), fund_soln_loc, strength) vel = get_obj_array(vel) err = vel - get_obj_array(exact_soln) # FIXME: Pointwise relative errors don't make sense! rel_err = err / (get_obj_array(exact_soln)) if 0: print("@@@@@@@@") print("vel[0], err[0], rel_err[0] ***** vel[1], err[1], rel_err[1]: ") for i in range(len(vel[0])): print("%15.8e, %15.8e, %15.8e ***** %15.8e, %15.8e, %15.8e\n" % (vel[0][i], err[0][i], rel_err[0][i], vel[1][i], err[1][i], rel_err[1][i])) print("@@@@@@@@") l2_err = np.sqrt((6. / (nsamp - 1))**2 * np.sum(err[0] * err[0]) + (6. / (nsamp - 1))**2 * np.sum(err[1] * err[1])) l2_rel_err = np.sqrt((6. / (nsamp - 1))**2 * np.sum(rel_err[0] * rel_err[0]) + (6. / (nsamp - 1))**2 * np.sum(rel_err[1] * rel_err[1])) print("L2 error estimate: ", l2_err) print("L2 rel error estimate: ", l2_rel_err) print("max error at sampled points: ", max(abs(err[0])), max(abs(err[1]))) print("max rel error at sampled points: ", max(abs(rel_err[0])), max(abs(rel_err[1]))) if do_plot: import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt full_pot = np.zeros_like(fplot.points) * float("nan") mask = circle_mask(fplot.points, radius=circle_rad) for i, vel in enumerate(plot_vel): full_pot[i, mask] = vel.get() plt.quiver(fplot.points[0], fplot.points[1], full_pot[0], full_pot[1], linewidth=0.1) plt.savefig("exterior-2d-field.pdf") # }}} h_max = bind(qbx, sym.h_max(qbx.ambient_dim))(queue) return h_max, l2_err
def run_exterior_stokes( ctx_factory, *, ambient_dim, target_order, qbx_order, resolution, fmm_order=False, # FIXME: FMM is slower than direct evaluation source_ovsmp=None, radius=1.5, mu=1.0, visualize=False, _target_association_tolerance=0.05, _expansions_in_tree_have_extent=True): cl_ctx = cl.create_some_context() queue = cl.CommandQueue(cl_ctx) actx = PyOpenCLArrayContext(queue) # {{{ geometry if source_ovsmp is None: source_ovsmp = 4 if ambient_dim == 2 else 8 places = {} if ambient_dim == 2: from meshmode.mesh.generation import make_curve_mesh, ellipse mesh = make_curve_mesh(lambda t: radius * ellipse(1.0, t), np.linspace(0.0, 1.0, resolution + 1), target_order) elif ambient_dim == 3: from meshmode.mesh.generation import generate_icosphere mesh = generate_icosphere(radius, target_order + 1, uniform_refinement_rounds=resolution) else: raise ValueError(f"unsupported dimension: {ambient_dim}") pre_density_discr = Discretization( actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) from pytential.qbx import QBXLayerPotentialSource qbx = QBXLayerPotentialSource( pre_density_discr, fine_order=source_ovsmp * target_order, qbx_order=qbx_order, fmm_order=fmm_order, target_association_tolerance=_target_association_tolerance, _expansions_in_tree_have_extent=_expansions_in_tree_have_extent) places["source"] = qbx from extra_int_eq_data import make_source_and_target_points point_source, point_target = make_source_and_target_points( side=+1, inner_radius=0.5 * radius, outer_radius=2.0 * radius, ambient_dim=ambient_dim, ) places["point_source"] = point_source places["point_target"] = point_target if visualize: from sumpy.visualization import make_field_plotter_from_bbox from meshmode.mesh.processing import find_bounding_box fplot = make_field_plotter_from_bbox(find_bounding_box(mesh), h=0.1, extend_factor=1.0) mask = np.linalg.norm(fplot.points, ord=2, axis=0) > (radius + 0.25) from pytential.target import PointsTarget plot_target = PointsTarget(fplot.points[:, mask].copy()) places["plot_target"] = plot_target del mask places = GeometryCollection(places, auto_where="source") density_discr = places.get_discretization("source") logger.info("ndofs: %d", density_discr.ndofs) logger.info("nelements: %d", density_discr.mesh.nelements) # }}} # {{{ symbolic sym_normal = sym.make_sym_vector("normal", ambient_dim) sym_mu = sym.var("mu") if ambient_dim == 2: from pytential.symbolic.stokes import HsiaoKressExteriorStokesOperator sym_omega = sym.make_sym_vector("omega", ambient_dim) op = HsiaoKressExteriorStokesOperator(omega=sym_omega) elif ambient_dim == 3: from pytential.symbolic.stokes import HebekerExteriorStokesOperator op = HebekerExteriorStokesOperator() else: assert False sym_sigma = op.get_density_var("sigma") sym_bc = op.get_density_var("bc") sym_op = op.operator(sym_sigma, normal=sym_normal, mu=sym_mu) sym_rhs = op.prepare_rhs(sym_bc, mu=mu) sym_velocity = op.velocity(sym_sigma, normal=sym_normal, mu=sym_mu) sym_source_pot = op.stokeslet.apply(sym_sigma, sym_mu, qbx_forced_limit=None) # }}} # {{{ boundary conditions normal = bind(places, sym.normal(ambient_dim).as_vector())(actx) np.random.seed(42) charges = make_obj_array([ actx.from_numpy(np.random.randn(point_source.ndofs)) for _ in range(ambient_dim) ]) if ambient_dim == 2: total_charge = make_obj_array([actx.np.sum(c) for c in charges]) omega = bind(places, total_charge * sym.Ones())(actx) if ambient_dim == 2: bc_context = {"mu": mu, "omega": omega} op_context = {"mu": mu, "omega": omega, "normal": normal} else: bc_context = {} op_context = {"mu": mu, "normal": normal} bc = bind(places, sym_source_pot, auto_where=("point_source", "source"))(actx, sigma=charges, mu=mu) rhs = bind(places, sym_rhs)(actx, bc=bc, **bc_context) bound_op = bind(places, sym_op) # }}} # {{{ solve from pytential.solve import gmres gmres_tol = 1.0e-9 result = gmres(bound_op.scipy_op(actx, "sigma", np.float64, **op_context), rhs, x0=rhs, tol=gmres_tol, progress=visualize, stall_iterations=0, hard_failure=True) sigma = result.solution # }}} # {{{ check velocity at "point_target" def rnorm2(x, y): y_norm = actx.np.linalg.norm(y.dot(y), ord=2) if y_norm < 1.0e-14: y_norm = 1.0 d = x - y return actx.np.linalg.norm(d.dot(d), ord=2) / y_norm ps_velocity = bind(places, sym_velocity, auto_where=("source", "point_target"))(actx, sigma=sigma, **op_context) ex_velocity = bind(places, sym_source_pot, auto_where=("point_source", "point_target"))(actx, sigma=charges, mu=mu) v_error = rnorm2(ps_velocity, ex_velocity) h_max = bind(places, sym.h_max(ambient_dim))(actx) logger.info("resolution %4d h_max %.5e error %.5e", resolution, h_max, v_error) # }}}} # {{{ visualize if not visualize: return h_max, v_error from meshmode.discretization.visualization import make_visualizer vis = make_visualizer(actx, density_discr, target_order) filename = "stokes_solution_{}d_{}_ovsmp_{}.vtu".format( ambient_dim, resolution, source_ovsmp) vis.write_vtk_file(filename, [ ("density", sigma), ("bc", bc), ("rhs", rhs), ], overwrite=True) # }}} return h_max, v_error
def _curve_fn(self, t): from meshmode.mesh.generation import ellipse return self.radius * ellipse(self.aspect_ratio, t)
def test_build_matrix_conditioning(actx_factory, side, op_type, visualize=False): """Checks that :math:`I + K`, where :math:`K` is compact gives a well-conditioned operator when it should. For example, the exterior Laplace problem has a nullspace, so we check that and remove it. """ actx = actx_factory() # prevent cache explosion from sympy.core.cache import clear_cache clear_cache() case = extra.CurveTestCase( name="ellipse", curve_fn=lambda t: ellipse(3.0, t), target_order=16, source_ovsmp=1, qbx_order=4, resolutions=[64], op_type=op_type, side=side, ) logger.info("\n%s", case) # {{{ geometry qbx = case.get_layer_potential(actx, case.resolutions[-1], case.target_order) from pytential.qbx.refinement import refine_geometry_collection places = GeometryCollection(qbx, auto_where=case.name) places = refine_geometry_collection( places, refine_discr_stage=sym.QBX_SOURCE_QUAD_STAGE2) dd = places.auto_source.to_stage1() density_discr = places.get_discretization(dd.geometry) logger.info("nelements: %d", density_discr.mesh.nelements) logger.info("ndofs: %d", density_discr.ndofs) # }}} # {{{ check matrix from pytential.symbolic.execution import build_matrix sym_u, sym_op = case.get_operator(places.ambient_dim, qbx_forced_limit="avg") mat = actx.to_numpy( build_matrix(actx, places, sym_op, sym_u, context=case.knl_concrete_kwargs)) kappa = la.cond(mat) _, sigma, _ = la.svd(mat) logger.info("cond: %.5e sigma_max %.5e", kappa, sigma[0]) # NOTE: exterior Laplace has a nullspace if side == +1 and op_type == "double": assert kappa > 1.0e+9 assert sigma[-1] < 1.0e-9 else: assert kappa < 1.0e+1 assert sigma[-1] > 1.0e-2 # remove the nullspace and check that it worked if side == +1 and op_type == "double": # NOTE: this adds the "mean" to remove the nullspace for the operator # See `pytential.symbolic.pde.scalar` for the equivalent formulation w = actx.to_numpy( flatten( bind(places, sym.sqrt_jac_q_weight(places.ambient_dim)**2)(actx), actx)) w = np.tile(w.reshape(-1, 1), w.size).T kappa = la.cond(mat + w) assert kappa < 1.0e+2 # }}} # {{{ plot if not visualize: return side = "int" if side == -1 else "ext" import matplotlib.pyplot as plt plt.imshow(mat) plt.colorbar() plt.title(fr"$\kappa(A) = {kappa:.5e}$") plt.savefig(f"test_cond_{op_type}_{side}_mat") plt.clf() plt.plot(sigma) plt.ylabel(r"$\sigma$") plt.grid() plt.savefig(f"test_cond_{op_type}_{side}_svd") plt.clf()
def main(ctx_factory, dim=2, order=4, use_quad=False, visualize=False): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) actx = PyOpenCLArrayContext( queue, allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)), force_device_scalars=True, ) # {{{ parameters # sphere radius radius = 1.0 # sphere resolution resolution = 64 if dim == 2 else 1 # final time final_time = np.pi # flux flux_type = "lf" # }}} # {{{ discretization if dim == 2: from meshmode.mesh.generation import make_curve_mesh, ellipse mesh = make_curve_mesh( lambda t: radius * ellipse(1.0, t), np.linspace(0.0, 1.0, resolution + 1), order) elif dim == 3: from meshmode.mesh.generation import generate_icosphere mesh = generate_icosphere(radius, order=4 * order, uniform_refinement_rounds=resolution) else: raise ValueError("unsupported dimension") discr_tag_to_group_factory = {} if use_quad: qtag = dof_desc.DISCR_TAG_QUAD else: qtag = None from meshmode.discretization.poly_element import \ default_simplex_group_factory, \ QuadratureSimplexGroupFactory discr_tag_to_group_factory[dof_desc.DISCR_TAG_BASE] = \ default_simplex_group_factory(base_dim=dim-1, order=order) if use_quad: discr_tag_to_group_factory[qtag] = \ QuadratureSimplexGroupFactory(order=4*order) from grudge import DiscretizationCollection dcoll = DiscretizationCollection( actx, mesh, discr_tag_to_group_factory=discr_tag_to_group_factory ) volume_discr = dcoll.discr_from_dd(dof_desc.DD_VOLUME) logger.info("ndofs: %d", volume_discr.ndofs) logger.info("nelements: %d", volume_discr.mesh.nelements) # }}} # {{{ Surface advection operator # velocity field x = thaw(dcoll.nodes(), actx) c = make_obj_array([-x[1], x[0], 0.0])[:dim] def f_initial_condition(x): return x[0] from grudge.models.advection import SurfaceAdvectionOperator adv_operator = SurfaceAdvectionOperator( dcoll, c, flux_type=flux_type, quad_tag=qtag ) u0 = f_initial_condition(x) def rhs(t, u): return adv_operator.operator(t, u) # check velocity is tangential from grudge.geometry import normal surf_normal = normal(actx, dcoll, dd=dof_desc.DD_VOLUME) error = op.norm(dcoll, c.dot(surf_normal), 2) logger.info("u_dot_n: %.5e", error) # }}} # {{{ time stepping # FIXME: dt estimate is not necessarily valid for surfaces dt = actx.to_numpy( 0.45 * adv_operator.estimate_rk4_timestep(actx, dcoll, fields=u0)) nsteps = int(final_time // dt) + 1 logger.info("dt: %.5e", dt) logger.info("nsteps: %d", nsteps) from grudge.shortcuts import set_up_rk4 dt_stepper = set_up_rk4("u", dt, u0, rhs) plot = Plotter(actx, dcoll, order, visualize=visualize) norm_u = actx.to_numpy(op.norm(dcoll, u0, 2)) step = 0 event = dt_stepper.StateComputed(0.0, 0, 0, u0) plot(event, "fld-surface-%04d" % 0) if visualize and dim == 3: from grudge.shortcuts import make_visualizer vis = make_visualizer(dcoll) vis.write_vtk_file( "fld-surface-velocity.vtu", [ ("u", c), ("n", surf_normal) ], overwrite=True ) df = dof_desc.DOFDesc(FACE_RESTR_INTERIOR) face_discr = dcoll.discr_from_dd(df) face_normal = thaw(dcoll.normal(dd=df), actx) from meshmode.discretization.visualization import make_visualizer vis = make_visualizer(actx, face_discr) vis.write_vtk_file("fld-surface-face-normals.vtu", [ ("n", face_normal) ], overwrite=True) for event in dt_stepper.run(t_end=final_time, max_steps=nsteps + 1): if not isinstance(event, dt_stepper.StateComputed): continue step += 1 if step % 10 == 0: norm_u = actx.to_numpy(op.norm(dcoll, event.state_component, 2)) plot(event, "fld-surface-%04d" % step) logger.info("[%04d] t = %.5f |u| = %.5e", step, event.t, norm_u) # NOTE: These are here to ensure the solution is bounded for the # time interval specified assert norm_u < 3
def run(actx, *, ambient_dim: int = 3, resolution: int = None, target_order: int = 4, tmax: float = 1.0, timestep: float = 1.0e-2, group_factory_name: str = "warp_and_blend", visualize: bool = True): if ambient_dim not in (2, 3): raise ValueError(f"unsupported dimension: {ambient_dim}") mesh_order = target_order radius = 1.0 # {{{ geometry # {{{ element groups import modepy as mp import meshmode.discretization.poly_element as poly # NOTE: picking the same unit nodes for the mesh and the discr saves # a bit of work when reconstructing after a time step if group_factory_name == "warp_and_blend": group_factory_cls = poly.PolynomialWarpAndBlendGroupFactory unit_nodes = mp.warp_and_blend_nodes(ambient_dim - 1, mesh_order) elif group_factory_name == "quadrature": group_factory_cls = poly.InterpolatoryQuadratureSimplexGroupFactory if ambient_dim == 2: unit_nodes = mp.LegendreGaussQuadrature( mesh_order, force_dim_axis=True).nodes else: unit_nodes = mp.VioreanuRokhlinSimplexQuadrature(mesh_order, 2).nodes else: raise ValueError(f"unknown group factory: '{group_factory_name}'") # }}} # {{{ discretization import meshmode.mesh.generation as gen if ambient_dim == 2: nelements = 8192 if resolution is None else resolution mesh = gen.make_curve_mesh( lambda t: radius * gen.ellipse(1.0, t), np.linspace(0.0, 1.0, nelements + 1), order=mesh_order, unit_nodes=unit_nodes) else: nrounds = 4 if resolution is None else resolution mesh = gen.generate_icosphere(radius, uniform_refinement_rounds=nrounds, order=mesh_order, unit_nodes=unit_nodes) from meshmode.discretization import Discretization discr0 = Discretization(actx, mesh, group_factory_cls(target_order)) logger.info("ndofs: %d", discr0.ndofs) logger.info("nelements: %d", discr0.mesh.nelements) # }}} if visualize: from meshmode.discretization.visualization import make_visualizer vis = make_visualizer(actx, discr0, vis_order=target_order, # NOTE: setting this to True will add some unnecessary # resampling in Discretization.nodes for the vis_discr underneath force_equidistant=False) # }}} # {{{ ode def velocity_field(nodes, alpha=1.0): return make_obj_array([ alpha * nodes[0], -alpha * nodes[1], 0.0 * nodes[0] ][:ambient_dim]) def source(t, x): discr = reconstruct_discr_from_nodes(actx, discr0, x) u = velocity_field(thaw(discr.nodes(), actx)) # {{{ # NOTE: these are just here because this was at some point used to # profile some more operators (turned out well!) from meshmode.discretization import num_reference_derivative x = thaw(discr.nodes()[0], actx) gradx = sum( num_reference_derivative(discr, (i,), x) for i in range(discr.dim)) intx = sum(actx.np.sum(xi * wi) for xi, wi in zip(x, discr.quad_weights())) assert gradx is not None assert intx is not None # }}} return u # }}} # {{{ evolve maxiter = int(tmax // timestep) + 1 dt = tmax / maxiter + 1.0e-15 x = thaw(discr0.nodes(), actx) t = 0.0 if visualize: filename = f"moving-geometry-{0:09d}.vtu" plot_solution(actx, vis, filename, discr0, t, x) for n in range(1, maxiter + 1): x = advance(actx, dt, t, x, source) t += dt if visualize: discr = reconstruct_discr_from_nodes(actx, discr0, x) vis = make_visualizer(actx, discr, vis_order=target_order) # vis = vis.copy_with_same_connectivity(actx, discr) filename = f"moving-geometry-{n:09d}.vtu" plot_solution(actx, vis, filename, discr, t, x) logger.info("[%05d/%05d] t = %.5e/%.5e dt = %.5e", n, maxiter, t, tmax, dt)
def curve_fn(self): return lambda t: self.radius * mgen.ellipse(self.aspect_ratio, t)