]> https://gitweb.dealii.org/ - dealii.git/commitdiff
fix typos
authorTimo Heister <timo.heister@gmail.com>
Thu, 17 Sep 2020 17:43:56 +0000 (13:43 -0400)
committerJiaqi Zhang <zjiaqi@vt.edu>
Mon, 30 Nov 2020 17:06:40 +0000 (12:06 -0500)
examples/step-74/doc/intro.dox
examples/step-74/doc/results.dox
examples/step-74/step-74.cc

index 7bd4844c962ed10825e65eb6f6601fec4d30c269..77ea4cb053a046bd4297d78fdbb6eb18599b9186 100644 (file)
@@ -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<v_h, \nv \nabla u_h\cdot \mathbf n \bigr>_F
+    \bigl<v_h, \nv \nabla u_h\cdot \mathbf n \bigr>_F
   + \bigl< \nabla v_h \cdot \mathbb n , \nu u_h\bigr>_F
   - \bigl< v_h,\nu \sigma u_h\bigr>_F
   \biggr\}
index 9ed00fd82bffe36fd463e02d64d964f33e00c66a..5342980c316695648d3cc328aae8350c8dfc6444 100644 (file)
@@ -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
index a458e7159c7ee9733652fdc87925cc390db673e8..cdc977d86b8813cbd547a4518429adf5473fe143 100644 (file)
@@ -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)
 

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