diff --git a/docs/source/pythonapi/base.rst b/docs/source/pythonapi/base.rst
index e7d139938..d621637bb 100644
--- a/docs/source/pythonapi/base.rst
+++ b/docs/source/pythonapi/base.rst
@@ -127,6 +127,7 @@ Constructing Tallies
    openmc.ParticleFilter
    openmc.RegularMesh
    openmc.RectilinearMesh
+   openmc.UnstructuredMesh
    openmc.Trigger
    openmc.TallyDerivative
    openmc.Tally
diff --git a/docs/source/usersguide/beginners.rst b/docs/source/usersguide/beginners.rst
index 85d50a2d4..a87078cb7 100644
--- a/docs/source/usersguide/beginners.rst
+++ b/docs/source/usersguide/beginners.rst
@@ -60,7 +60,7 @@ Now let's look at the pros and cons of Monte Carlo methods:
 
 - **Pro**: No mesh generation is required to build geometry. By using
   `constructive solid geometry`_, it's possible to build arbitrarily complex
-  models with curved surfaces.
+  reactor models with curved surfaces.
 
 - **Pro**: Monte Carlo methods can be used with either continuous-energy or
   multi-group cross sections.
diff --git a/docs/source/usersguide/processing.rst b/docs/source/usersguide/processing.rst
index 56e3bbc1f..210caca82 100644
--- a/docs/source/usersguide/processing.rst
+++ b/docs/source/usersguide/processing.rst
@@ -41,9 +41,9 @@ Plotting in 2D
 --------------
 
 The :ref:`IPython notebook example <notebook_post_processing>` also demonstrates
-how to plot a mesh tally in two dimensions using the Python API. One can also
-use the :ref:`scripts_plot` script which provides an interactive GUI to explore
-and plot mesh tallies for any scores and filter bins.
+how to plot a structured mesh tally in two dimensions using the Python API. One
+can also use the :ref:`scripts_plot` script which provides an interactive GUI to
+explore and plot structured mesh tallies for any scores and filter bins.
 
 .. image:: ../_images/plotmeshtally.png
    :width: 400px
diff --git a/docs/source/usersguide/settings.rst b/docs/source/usersguide/settings.rst
index 1254c4ed5..5df42fcbe 100644
--- a/docs/source/usersguide/settings.rst
+++ b/docs/source/usersguide/settings.rst
@@ -171,16 +171,17 @@ Shannon Entropy
 To assess convergence of the source distribution, the scalar Shannon entropy
 metric is often used in Monte Carlo codes. OpenMC also allows you to calculate
 Shannon entropy at each generation over a specified mesh, created using the
-:class:`openmc.Mesh` class. After instantiating a :class:`Mesh`, you need to
-specify the lower-left coordinates of the mesh (:attr:`Mesh.lower_left`), the
-number of mesh cells in each direction (:attr:`Mesh.dimension`) and either the
-upper-right coordinates of the mesh (:attr:`Mesh.upper_right`) or the width of
-each mesh cell (:attr:`Mesh.width`). Once you have a mesh, simply assign it to
-the :attr:`Settings.entropy_mesh` attribute.
+:class:`openmc.RegularMesh` class. After instantiating a :class:`RegularMesh`,
+you need to specify the lower-left coordinates of the mesh
+(:attr:`RegularMesh.lower_left`), the number of mesh cells in each direction
+(:attr:`RegularMesh.dimension`) and either the upper-right coordinates of the
+mesh (:attr:`RegularMesh.upper_right`) or the width of each mesh cell
+(:attr:`RegularMesh.width`). Once you have a mesh, simply assign it to the
+:attr:`Settings.entropy_mesh` attribute.
 
 ::
 
-   entropy_mesh = openmc.Mesh()
+   entropy_mesh = openmc.RegularMesh()
    entropy_mesh.lower_left = (-50, -50, -25)
    entropy_mesh.upper_right = (50, 50, 25)
    entropy_mesh.dimension = (8, 8, 8)
@@ -193,7 +194,7 @@ property::
 
   geom = openmc.Geometry()
   ...
-  m = openmc.Mesh()
+  m = openmc.RegularMesh()
   m.lower_left, m.upper_right = geom.bounding_box
   m.dimension = (8, 8, 8)