From 9c5248f860ada545590d4d8f20be3ea9aa88572b Mon Sep 17 00:00:00 2001 From: Timo Heister Date: Thu, 17 Sep 2020 13:43:56 -0400 Subject: [PATCH] fix typos --- examples/step-74/doc/intro.dox | 2 +- examples/step-74/doc/results.dox | 2 +- examples/step-74/step-74.cc | 18 +++++++++--------- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/examples/step-74/doc/intro.dox b/examples/step-74/doc/intro.dox index 7bd4844c96..77ea4cb053 100644 --- a/examples/step-74/doc/intro.dox +++ b/examples/step-74/doc/intro.dox @@ -64,7 +64,7 @@ The discretization using the SIPG is given by the following weak formula \biggr\} \\ - \sum_{F \in F_h^b} \biggl\{ - \bigl_F + \bigl_F + \bigl< \nabla v_h \cdot \mathbb n , \nu u_h\bigr>_F - \bigl< v_h,\nu \sigma u_h\bigr>_F \biggr\} diff --git a/examples/step-74/doc/results.dox b/examples/step-74/doc/results.dox index 9ed00fd82b..5342980c31 100644 --- a/examples/step-74/doc/results.dox +++ b/examples/step-74/doc/results.dox @@ -184,7 +184,7 @@ freedom, then $h$ is of order $1/\sqrt{n}$. Combining the theoretical results in the previous case, we see that the error in $L_2$ norm is of order $O(n^{-\frac{p+1}{2}})$ and in $H_1$ seminorm is $O(n^{-\frac{p}{2}})$. From the figure, we see -that the SIPG with adaptive mesh refinement produces desirable resutls +that the SIPG with adaptive mesh refinement produces desirable results that match theoretical ones. In addition, we observe that the error estimator decreases diff --git a/examples/step-74/step-74.cc b/examples/step-74/step-74.cc index a458e7159c..cdc977d86b 100644 --- a/examples/step-74/step-74.cc +++ b/examples/step-74/step-74.cc @@ -351,7 +351,7 @@ namespace Step74 { for (unsigned int j = 0; j < fe_v.dofs_per_cell; ++j) copy_data.cell_matrix(i, j) += - // nu \nabla u \nabla v + // \nu \nabla u \nabla v diffusion_coefficient * fe_v.shape_grad(i, point) * fe_v.shape_grad(j, point) * JxW[point]; @@ -389,16 +389,16 @@ namespace Step74 for (unsigned int j = 0; j < dofs_per_cell; ++j) copy_data.cell_matrix(i, j) += ( - // - nu (\nabla u . n) v + // - \nu (\nabla u . n) v -diffusion_coefficient * (fe_fv.shape_grad(j, point) * normals[point]) * fe_fv.shape_value(i, point) - // - nu u (\nabla v . n) + // - \nu u (\nabla v . n) - diffusion_coefficient * fe_fv.shape_value(j, point) * (fe_fv.shape_grad(i, point) * normals[point]) - // + nu * penalty u v + // + \nu * penalty u v + diffusion_coefficient * penalty * fe_fv.shape_value(j, point) * fe_fv.shape_value(i, point)) * @@ -407,11 +407,11 @@ namespace Step74 for (unsigned int i = 0; i < dofs_per_cell; ++i) copy_data.cell_rhs(i) += ( - // -nu g (\nabla v . n) + // -\nu g (\nabla v . n) -diffusion_coefficient * g[point] * (fe_fv.shape_grad(i, point) * normals[point]) - // +nu penalty g v + // +\nu penalty g v + diffusion_coefficient * penalty * g[point] * fe_fv.shape_value(i, point)) * JxW[point]; @@ -457,16 +457,16 @@ namespace Step74 for (unsigned int j = 0; j < n_dofs_face; ++j) copy_data_face.cell_matrix(i, j) += ( - // - nu {\nabla u}.n [v] (consistency) + // - \nu {\nabla u}.n [v] (consistency) -diffusion_coefficient * (fe_iv.average_gradient(j, point) * normals[point]) * fe_iv.jump(i, point) - // - nu [u] {\nabla v}.n (symmetry) // NIPG: use + + // - \nu [u] {\nabla v}.n (symmetry) // NIPG: use + - diffusion_coefficient * fe_iv.jump(j, point) * (fe_iv.average_gradient(i, point) * normals[point]) - // nu sigma [u] [v] (penalty) + // \nu sigma [u] [v] (penalty) + diffusion_coefficient * penalty * fe_iv.jump(j, point) * fe_iv.jump(i, point) -- 2.39.5