From 3a9ad1fe9bb6e61da2ab4eb9c0cea1122655af08 Mon Sep 17 00:00:00 2001
From: Martin Schneider <martin.schneider@iws.uni-stuttgart.de>
Date: Fri, 24 Mar 2023 10:43:30 +0100
Subject: [PATCH] [example][diffusion] Fix typos
---
examples/README.md | 4 ++--
examples/diffusion/README.md | 8 ++++----
examples/diffusion/doc/_intro.md | 8 ++++----
examples/diffusion/doc/model.md | 2 +-
examples/diffusion/model.hh | 2 +-
5 files changed, 12 insertions(+), 12 deletions(-)
diff --git a/examples/README.md b/examples/README.md
index 50b47011ee..beb935118d 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -15,8 +15,8 @@ You learn how to
* setup a new simple model equation (diffusion equation)
* read parameters from a configuration file
* create a type tag and specialize properties for it
-* generate a randomly distributed intial field (with MPI parallelism)
-* solve a time-depedent diffusion problem in parallel
+* generate a randomly distributed initial field (with MPI parallelism)
+* solve a time-dependent diffusion problem in parallel
__Model equations:__ A diffusion equation model fully developed and contained within the example<br />
__Discretization method:__ Vertex-centered finite volumes / control-volume finite elements (Lagrange, P1) (`BoxModel`)
diff --git a/examples/diffusion/README.md b/examples/diffusion/README.md
index d54937c4e1..31d2018ff9 100644
--- a/examples/diffusion/README.md
+++ b/examples/diffusion/README.md
@@ -19,7 +19,7 @@ __The main points illustrated in this example are__
## Equation and problem description
The scalar diffusion equation on a domain $\Omega \subset \mathbb{R}^2$
-with boundary $\partial\Omega = \Gamma_D \cup \Gamma_N$ composed of Dirichlet and Neummann boundaries
+with boundary $\partial\Omega = \Gamma_D \cup \Gamma_N$ composed of Dirichlet and Neumann boundaries
reads
```math
@@ -93,18 +93,18 @@ The simulation result will look something like this.
By default Dumux will try to speed up the assembly by using shared memory parallelism if a suitable
backend has been found on your system (one of TBB, OpenMP, Kokkos, C++ parallel algorithms).
You can limit the number of threads by prepending your executable with `DUMUX_NUM_THREADS=<number>`.
-If you also want to use distributed memory parallelsim with MPI (works better for solvers at the moment),
+If you also want to use distributed memory parallelism with MPI (works better for solvers at the moment),
run the executable with your MPI environment. Each MPI process will use multi-threading if
`DUMUX_NUM_THREADS` is larger than $1$.
-Running the example with four MPI processes (distribution memory parallelsim)
+Running the example with four MPI processes (distribution memory parallelism)
each with two threads (shared memory parallelism):
```sh
DUMUX_NUM_THREADS=2 mpirun -np 4 ./example_diffusion
```
-You can set the parameter `Grid.Overlap` to some non-zero integer in `param.input`
+You can set the parameter `Grid.Overlap` to some non-zero integer in `params.input`
to turn the domain decomposition into an overlapping decomposition where
`Grid.Overlap` specifies the number of grid cells in the overlap between processes.
This can help to increase the convergence speed of the linear solver.
diff --git a/examples/diffusion/doc/_intro.md b/examples/diffusion/doc/_intro.md
index 5ca71136bd..2f759145fb 100644
--- a/examples/diffusion/doc/_intro.md
+++ b/examples/diffusion/doc/_intro.md
@@ -17,7 +17,7 @@ __The main points illustrated in this example are__
## Equation and problem description
The scalar diffusion equation on a domain $\Omega \subset \mathbb{R}^2$
-with boundary $\partial\Omega = \Gamma_D \cup \Gamma_N$ composed of Dirichlet and Neummann boundaries
+with boundary $\partial\Omega = \Gamma_D \cup \Gamma_N$ composed of Dirichlet and Neumann boundaries
reads
```math
@@ -91,18 +91,18 @@ The simulation result will look something like this.
By default Dumux will try to speed up the assembly by using shared memory parallelism if a suitable
backend has been found on your system (one of TBB, OpenMP, Kokkos, C++ parallel algorithms).
You can limit the number of threads by prepending your executable with `DUMUX_NUM_THREADS=<number>`.
-If you also want to use distributed memory parallelsim with MPI (works better for solvers at the moment),
+If you also want to use distributed memory parallelism with MPI (works better for solvers at the moment),
run the executable with your MPI environment. Each MPI process will use multi-threading if
`DUMUX_NUM_THREADS` is larger than $1$.
-Running the example with four MPI processes (distribution memory parallelsim)
+Running the example with four MPI processes (distribution memory parallelism)
each with two threads (shared memory parallelism):
```sh
DUMUX_NUM_THREADS=2 mpirun -np 4 ./example_diffusion
```
-You can set the parameter `Grid.Overlap` to some non-zero integer in `param.input`
+You can set the parameter `Grid.Overlap` to some non-zero integer in `params.input`
to turn the domain decomposition into an overlapping decomposition where
`Grid.Overlap` specifies the number of grid cells in the overlap between processes.
This can help to increase the convergence speed of the linear solver.
diff --git a/examples/diffusion/doc/model.md b/examples/diffusion/doc/model.md
index 145552f4ad..292a56b6f4 100644
--- a/examples/diffusion/doc/model.md
+++ b/examples/diffusion/doc/model.md
@@ -56,7 +56,7 @@ Box method which is based on $P_1$ basis functions (piece-wise linears)
and the degrees of freedom are on the nodes. Each node is associate with
exactly one sub control volume (`scv`) per element and several ($2$ in $\mathbb{R}^2$)
sub control volume faces (`scvf`). In the local residual, we can implement the
-constribution for one `scv` (storage and source terms) or one `scvf` (flux terms).
+contribution for one `scv` (storage and source terms) or one `scvf` (flux terms).
Let's have a look at the class implementation.
diff --git a/examples/diffusion/model.hh b/examples/diffusion/model.hh
index c2c9b67dd3..df4f97c723 100644
--- a/examples/diffusion/model.hh
+++ b/examples/diffusion/model.hh
@@ -58,7 +58,7 @@ struct DiffusionModel {};
// and the degrees of freedom are on the nodes. Each node is associate with
// exactly one sub control volume (`scv`) per element and several ($2$ in $\mathbb{R}^2$)
// sub control volume faces (`scvf`). In the local residual, we can implement the
-// constribution for one `scv` (storage and source terms) or one `scvf` (flux terms).
+// contribution for one `scv` (storage and source terms) or one `scvf` (flux terms).
//
// Let's have a look at the class implementation.
//
--
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