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\nopagebreak

In this chapter, we will describe algorithms used for several
operations done by \CS.

\section*{Geometric Quantities\label{sec:geo_quant}}

\subsection*{Normals and Face Centers%
             \label{sec:geo_quant.normal}}

To calculate face normals, we take care to use an algorithm
that is correct for any planar simple polygon, including non-convex cases.
The principle is as follows: take an arbitrary point $P_a$ in the
same plane as the polygon, then compute the sum of the vector normals
of triangles $\{P_a, P_i, P_{i+1}\}$, where $\{P_1, P_2, ..., P_i, ..., P_n\}$
are the polygon vertices and $P_{n+1} \equiv P_0$. As shown on figure
\ref{fig:algo.norm_fac.principle}, some normals have a ``positive''
contribution while others have a ``negative'' contribution (taking into
account the ordering of the polygon's vertices). The length of the final normal
obtained is equal to the polygon's surface.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=4.5cm]{face_surf}}
\caption{Face normals calculation principle}
\label{fig:algo.norm_fac.principle}
\end{figure}

In our implementation, we take the ``arbitrary'' $P_a$ point as
the center of the polygon's vertices, so as to limit
precision problems due to truncation errors and to ensure that
the chosen point is on the polygon's plane.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=3.5cm]{face_quant}}
\caption{Triangles for calculation of face quantities}
\label{fig:algo.grd_fac.triangles}
\end{figure}

A face's center is defined as the weighted center $G$ of triangles
$T_i$ defined as $\{P_a, P_i, P_{i+1}\}$ and whose centers are
noted $G_i$. Let $O$ be the center of the coordinate system and
$\overrightarrow{n_f}$ the face normal, then:

\begin{displaymath}
\overrightarrow{OG}
= \frac{\sum_{i=1}^n \textrm{surf}(T_i).\overrightarrow{OG_i}}
       {\sum_{i=1}^n \textrm{surf}(T_i)}
\qquad \textrm {avec} \quad
\textrm{surf}(T_i) = \frac{\overrightarrow{n_{T_i}}.\overrightarrow{n_f}}
                          {\parallel \overrightarrow{n_f} \parallel}
\end{displaymath}

It is important to use the signed surface of each triangle so
that this formula remains true in the case of non convex faces.

In real cases, some faces are nor perfectly planar. In this case,
a slight error is introduced, but it is difficult to choose an exact
and practical (implementation and computing cost wise) definition of a
polygon's surface when its edges do not all lie in a same plane.

So as to limit errors due to warped faces, we compare the contribution
of a given face to the the neighboring cell volumes (through
Stoke's formula) with the contribution obtained from the separate
triangles $\{P_a, P_i, P_{i+1}\}$, and we translate the initial center
of gravity along the face normal axis so as to obtain the same contribution.

\subsection*{Cell Centers%
               \label{sec:geo_quant.cdgcel}}

If we consider that in theory, the Finite Volume method uses constant
per-cell values, we can make without a precise knowledge of a given cell's
center, as any point inside the cell could be chosen.
In practice, precision (spatial order) depends on a good choice of
the cell's center, as this point is used for the computation of values
and gradients at mesh faces.
We do not compute the center a the circumscribed sphere as
a cell center, as this notion is usually linked to tetrahedral meshes,
and is not easily defined and calculated for general polyhedra.

Let us consider a cell $\mathcal{C}$ with $p$ faces of centers of gravity
$G_k$ and surfaces of norm $S_k$. If $O$ is the origin of the coordinate
system, $\mathcal{C}$'s center $G$ is defined as:

\begin{displaymath}
\overrightarrow{OG}
= \frac{\sum_{k=1}^p S_k.\overrightarrow{OG_k}}
       {\sum_{k=1}^p S_k}
\end{displaymath}

An older algorithm computed a cell $\mathcal{C}$'s center of gravity as the
center of gravity of its vertices $q$ of coordinates $X_l$:

$$\overrightarrow{OG} = \sum_{l=1}^q \frac{\overrightarrow{OX_l}}{q}$$

In most cases, the newer algorithm gives better results, though there
are exceptions (notably near the axis of partial meshes with rotational symmetry).

On figure \ref{fig:algo.cog_cel.loc}, we show the center of gravity
calculated with both algorithms on a 2D cell. On the left, we have
a simple cell. On the right, we have added additional vertices as they
occur in the case of a joining of non conforming faces. The position
of the center of gravity is stable using the newer algorithm, whilst
this point is shifted towards the joined sub-faces with the older,
vertex-based algorithm (which worsens the mesh quality).

\begin{figure}[!h]
\centerline{
\includegraphics*[width=15.5cm]{cell_cog}}
\caption{Choice of cell center}
\label{fig:algo.cog_cel.loc}
\end{figure}

On figure \ref{fig:algo.cog_cel.nonorth}, we show the possible effect of
the choice of a cell's COG on the position of line segments joining the COG's
of neighboring cells after a joining of non-conforming cells.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=4.5cm]{cell_cog_nonorth}}
\caption{Choice of cell center and face orthogonality}
\label{fig:algo.cog_cel.nonorth}
\end{figure}

We see here that the vertex-based algorithm tends to increase
non-orthogonality of faces, compared to the present
face-based algorithm.

\section*{Conforming Joining\label{sec:join}}

The use of non conforming meshes is one of \CS's key features, and
the associated algorithms constitute the most complex part of the
code's preprocessor. The idea is to build new faces corresponding to
the intersections of the initial faces to be joined.
Those initial faces are then replaced by their covering built
from the new faces, as shown on figure \ref{fig:algo.join.principle}
(from a 2D sideways perspective):

\begin{figure}[!h]
\centerline{
\includegraphics*[height=5cm]{join_principle}}
\caption{Principle of face joinings}
\label{fig:algo.join.principle}
\end{figure}

We speak of \emph{conforming joining}, as the mesh resulting from
this joining is conforming, whereas the initial mesh was not.

The number of faces of a cell of which one side has been joined will
be greater or equal to the initial number of faces, and the new faces
resulting from the joining will not always be triangles or quadrangles,
even if all of the initial faces were of theses types. For this
reason, the data representations of \CS and its preprocessor
are designed with arbitrary simple polygonal faces and polyhedral
cells in mind. We exclude polygons and polyhedra with holes from
this representation, as shown on figure \ref{fig:algo.join.possible}.
Thus the cells shown in figure \ref{fig:algo.join.possible} cannot be
joined, as this would require opening a ``hole'' in one of the larger
cell's faces. In the case of figure \ref{fig:algo.join.possible}b, we
have no such problem, as the addition of another smaller cell
splits the larger face into pieces that do not contain holes.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=5cm]{join_possible}}
\caption{Possible case joinings}
\label{fig:algo.join.possible}
\end{figure}

\subsection*{Robustness Factors%
               \label{sec:join.robust}}

We have sought to build a joining algorithm that could function with
a minimum of user input, on a wide variety of cases.

Several criteria were deemed important:

\begin{enumerate}

\item {\bf determinism}: we want to be able to predict the algorithm's behavior.
We especially want the algorithm to produce the same results whether
some mesh $A$ was joined to mesh $B$ or $B$ to $A$. This might not be perfectly
true in the implementation due to truncation errors, but the main point is
that the user should not have to worry about the order in which he enters
his meshes for the best computational results.
\footnote{The geometry produced by a joining is in theory independent
from the order mesh inputs, but mesh numbering is not. The input order
used for a calculation should thus be kept for any calculation restart.}

\item {\bf non planar surfaces}: We must be able to join both curved surface
meshes and meshes of surfaces assembled from a series of planar sections,
but whose normal is not necessarily a continuous function of space,
as shown on figure \ref{fig:algo.join.non_planar}.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=3cm]{join_non_planar}}
\caption{Initial surfaces}
\label{fig:algo.join.non_planar}
\end{figure}

\item {\bf spacing between meshes}: the surfaces to be joined may
not match perfectly, due to truncation errors or precision differences,
or to the approximation of curved surfaces by a set of planar faces.
The algorithm must not leave gaps where none are desired.

\end{enumerate}

\subsection*{Basic Principle\label{sec:join.principe}}

Let us consider two surfaces to join, as in figure \ref{fig:algo.join.curv}:
We seek to determine the intersections of the edges of the mesh faces,
and to split these edges at those intersections, as shown on figure
\ref{fig:algo.join.curv2}. We will describe more precisely what we
mean by ``intersection'' of two edges in a later section, as
the notion involves spanning of small gaps in our case.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=4cm]{join_overlap_3d_1}}
\caption{Surfaces to be joined}
\label{fig:algo.join.curv}
\end{figure}

\begin{figure}[!h]
\centerline{
\includegraphics*[height=4cm]{join_overlap_3d_2}}
\caption{After edge intersections}
\label{fig:algo.join.curv2}
\end{figure}

The next step consists of reconstructing sub-faces derived from the
initial faces. Starting from an edge of an initial face, we try to find
closed loops, choosing at each vertex the leftmost edge (as seen standing
on that face, normal pointing upwards, facing in the direction of the
current edge), until we have returned to the starting vertex. This way,
we find the shortest loop turning in the trigonometric
direction. Each face to be joined is replaced by its covering of
sub-faces constructed in this manner.

When traversing the loops, we must be careful to stay close to the plane
of the original face. We thus consider only the edges belonging to a face
whose normal has a similar direction to that of the face being subdivided
(i.e. the absolute value of the dot product of the two unitary normals
should be close to 1).

\begin{figure}[!h]
\centerline{
\includegraphics*[height=4.5cm]{join_overlap_3d_3}}
\caption{Sub-face reconstruction.}
\label{fig:algo.join.curv3}
\end{figure}

Once all the sub-faces are built, we should have obtained for two tangent
initial faces two topologically identical sub-faces, each descending
from one of the initial faces and thus belonging to a different cell.
All that is required at this stage is to merge those two sub-faces,
conserving the properties of both. The merged sub-face thus belongs to
two cells, and becomes an internal face. The joining is thus finalized.

\subsection*{Simplification of Face Joinings\label{sec:join.simplif}}

For a finite-volume code such as \CS, it is best that faces belonging
to one same cell have neighboring sizes. This is hard to ensure
when non-conforming boundary faces are split so as to be joined
in a conforming way. On figure \ref{fig:algo.join.simplif},
we see that this can produce faces of highly varying sizes
when splitting a face for conformal joining.

It is possible to simplify the covering of a face so as to limit
this effect, by moving vertices slightly on each side of the covering.
We seek to move vertices so as to simplify the covering while
deforming the mesh as little as possible.

One covering example is presented figure \ref{fig:algo.join.simplif},
where we show several simplification opportunities. We note that all
these possibilities are associated with a given edge. Similar
possibilities associated with other edges are not shown so that the
figure remains readable. After simplification, we obtain the
situation of figure \ref{fig:algo.join.simpl2}.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=5cm]{join_simplify_1}}
\caption{Simplification possibilities}
\label{fig:algo.join.simplif}
\end{figure}

After simplification, we have the following situation:

\begin{figure}[!h]
\centerline{
\includegraphics*[height=5cm]{join_simplify_2}}
\caption{Faces after simplification}
\label{fig:algo.join.simpl2}
\end{figure}

\subsection*{Processing\label{sec:join.process}}

The algorithm's starting point is the search for intersections
of edges belonging to the faces selected for joining. In 3D, we
do not restrict ourselves to ``true'' intersections, but
we consider that two edges intersect as soon as the minimum
distance between those edges is smaller than a certain tolerance.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=3cm]{join_edge_inter_3d}}
\caption{Intersection of edges in 3d space}
\label{fig:algo.join.edge}
\end{figure}

To each vertex we associate a maximum distance, some factor of
the length of the smallest edge incident to that vertex.
This factor is adjustable (we use $0.1$ by default), but
should always be less than $0.5$.
By default, this factor is usually multiplied by the smallest sine
of the angles between the edges considered, so that the
tolerance assigned to a given vertex is a good estimation
of the smallest height/width/depth or the adjacent cells.

On figure \ref{fig:algo.join.tolerance}, we illustrate
this tolerance in 2d with a factor of $0.25$, with red circles
showing the tolerance region without the sine factor
correction, and blue circle showing the tolerance with
the sine correction.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=3cm]{join_tolerance}}
\caption{Join tolerance for vertices of joinable faces}
\label{fig:algo.join.tolerance}
\end{figure}

We consider a neighborhood of an edge defined by the spheres
associated to the minimal distances around each vertex and
the shell joining this two spheres, as shown on figure
\ref{fig:algo.join.edgeint_eps}.
More precisely, at a point on the edge of linear abscissa $s$,
the neighborhood is defined to be the sphere of radius
$d_{max}(s) = (1-s)d_{max}\mid_{s=0} + s.d_{max}\mid_{s=1}$.

We will thus have intersection between edges $E1$ and $E2$ as soon
as the point of $E1$ closest to $E2$ is within the neighborhood
of $E2$, and that simultaneously, the point of $E2$ closest
to $E1$ is inside the neighborhood of $E1$.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=4cm]{join_edge_inter_3d_eps}}
\caption{Tolerances for intersection of edges}
\label{fig:algo.join.edgeint_eps}
\end{figure}

If edge $E2$ cuts the neighborhood of a vertex of edge $E1$,
and this vertex is also in $E2$'s neighborhood, we choose this
vertex to define the intersection rather than the point of
$E1$ closest to $E2$. This avoids needlessly cutting edges.
We thus search for intersections with the following order
of priority: vertex-vertex, vertex-edge, then edge-edge.
If the neighborhoods associated with two edges intersect,
but the criterion:\\
$\exists P1 \in A1, \exists P2 \in A2, d(P1,P2)
 < min(d_{max}(P1), d_{max}(P2))$\\
is not met, we do not have
intersection. These cases are shown on figure
\ref{fig:algo.join.edgeint_type}.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=5cm]{join_edge_inter_3d_type}}
\caption{Tolerances for intersection of edges}
\label{fig:algo.join.edgeint_type}
\end{figure}

\subsection*{Problems Arising From the Merging of Two Neighboring Vertices
                \label{sec:join.pb_merge}}

If we have determined that a vertex $V_1$ should be merged with a
vertex $V_2$ and independently that this vertex $V_2$ should be
merged with a vertex $V_3$, then $V_1$ and $V_3$ should be
merged as a result, even though these vertices share no intersection.
We refer to this problem as merging transitivity and show
theoretical situations leading to it on figure \ref{fig:algo.merging.pb}.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=5cm]{join_merge_1}}
\caption{Merging transitivity.}
\label{fig:algo.merging.pb}
\end{figure}

On figure \ref{fig:algo.merging.pb_1}, we show cases more
characteristic of what we can obtain with real meshes, given
that the definition of local tolerances reduces the risk and
possible cases of this type of situation.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=5cm]{join_merge_2}}
\caption{Merging transitivity (real cases)}
\label{fig:algo.merging.pb_1}
\end{figure}

Figure \ref{fig:algo.merging.pb_2} illustrates the effect of
a combination of merges on vertices belonging to a same edge.
We see that in this case, edges initially going through
vertices $G$ and $J$ are strongly deformed (i.e. cut into
sub-edges that are not well aligned). Without transitivity,
edges going through vertices descended only from the merging
of $(G, H)$ on one hand and $(L, J)$ on the other hand
would be much closer to the initial edges.

To avoid excessive simplifications of the mesh arising from a
combination of merges, we first build ``chains'' of intersections
which should be merged, compute the coordinates of the merged
intersection, and then check for all intersection couples
of a given chain if excessive merging would occur. If this is the case,
we compute a local multiplicative factor ($< 1$) for all the
intersections from this chain, so as to reduce the merge tolerance
and ``break'' this chain into smaller subchains containing only
intersections withing each other's merging distances.

Tolerance reduction may be done in multiple steps, as we
try to break the weakest equivalences (those closest to
the merge tolerance bounds) first.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=6cm]{join_merge_3}}
\caption{Merging transitivity for an edge}
\label{fig:algo.merging.pb_2}
\end{figure}

On figure \ref{fig:algo.merging.pb_3}, we show the possible
effect of merge transitivity limitation on vertices belonging to several edges.
Here, breaking of excessive intersection mergings should lead to
merging of intersections $(G, H, J)$, while $I$ is not merged
with another intersection.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=5cm]{join_merge_4}}
\caption{Limited merging transitivity}
\label{fig:algo.merging.pb_3}
\end{figure}

\subsection*{Algorithm Optimization\label{sec:join.optim}}

Certain factors influence both memory and CPU requirements. We always
try to optimize both, with a slight priority regarding memory
requirements.

When searching for edge intersections, we try to keep the number of intersection
tests to a minimum. We compute coordinate axis-aligned bounding boxes associated
with each joinable face (augmented by the tolerance radii of associated
vertices), and run a full edge intersection test only for
edges belonging to faces whose bounding boxes intersect.

To determine which face bounding boxes intersect, we build a ``bounding
box-tree'' , similar to an octree, but with boxes belonging to all
the octree leaves they overlap. When running in parallel using MPI, a first,
coarser version of the tree with the same maximum depth on all ranks is built
so as to estimate the optimal distribution of data, to balance both the computational
and memory load. Bounding boxes are then migrated to the target ranks,
where the final box-tree (which may have different depth on different ranks)
is built. Assigning the global ids of the matching faces to the bounding boxes
ensures the search results are usable independently of the distribution
across MPI ranks.

\subsection*{Influence on mesh quality\label{sec:join.quality}}

It is preferable for a FV solver such as \CS that the mesh be as
``orthogonal'' as possible (a face is perfectly orthogonal when the
segment joining its center of mass to the center of the other cell to
which it belongs is perfectly aligned with its normal).
It is also important to avoid non planar faces
\footnote {Computation of face COG's includes a correction such that
the contribution of a warped face to a cell's volume is the same
as that of the same face split into triangles joining that face's
COG and outer edges, but this correction may not be enough
for second order values.}.
By the joining algorithm's principle, orthogonal faces are split
into several non-orthogonal sub-faces. In addition, the higher
the tolerance setting, the more merging of neighboring vertices
will tend to warp faces on the sides of cells with joined faces.

It is thus important to know when building a mesh that a mesh
built by joining two perfectly regular hexahedral meshes may
be of poor quality, especially if a high tolerance value
was used and the cell-sizes of each mesh are very different.
It is thus important to use the mesh quality criteria visualizations
available in \CS, and to avoid arbitrary joinings in sensible
areas of the mesh. When possible, joining faces of which one
set is already a subdivision of another (to construct local
refinements for example) is recommended.

\section*{Periodicity\label{sec:algo.perio}}

We use an extension of the non-conforming faces joining algorithm
to build periodic structures. The basic principle is described
figure \ref{fig:algo.rc_perio}:

\begin{itemize}

\item Let us select a set of boundary faces. These faces (and their
      edges and vertices) are duplicated, and the copy is moved
      according to the periodic step (a combination of translation
      and rotation). A link between original and duplicate
      entities is kept.

\item We use a conforming joining on the union of
      selected faces and their duplicates.
      This joining will slightly deform the mesh so that vertices
      very close to each other may be merged, and periodic
      faces may be split into conforming sub-faces (if they are
      not already conforming).

\item If necessary, the splitting of duplicated, moved, and joined
      faces is also applied to the faces from which they were
      descended.

\item Duplicated entities which were not joined (i.e. excess entities)
      are deleted.

\end{itemize}

\begin{figure}[!h]
\centerline{
\includegraphics*[height=8cm]{join_perio}}
\caption{Periodic joining principle (translation example)}
\label{fig:algo.rc_perio}
\end{figure}

It is thus not necessary to use periodic boundary conditions
that the periodic surfaces be meshed in a periodic manner,
though it is always best not to make to much use of the
comfort provided by conforming joinings, as it can lower
mesh quality (and as a consequence, quality of computational
results).

We note that it could seem simpler to separate periodic faces
in two sets of ``base'' and ``periodic'' faces, so as
to duplicate and transform only the first set. This would
allow for some algorithm simplifications and optimizations,
but here we gave a higher priority to the consistency
of user input for specification of face selections, and
the definition of two separate sets of faces would have
made user input more complex. As for the basic
conforming joining, it is usually not necessary to specify
face selections for relatively simple meshes (in which case
all faces on the mesh boundary are selected).

\section*{Triangulation of faces\label{sec:triangle}}

Face triangulation is done in two
stages. The first stage uses an \emph{ear cutting} algorithm, which
can triangulate any planar polygon, whether convex or not.
The triangulation is arbitrary, as it depends on the vertex chosen
to start the loop around the polygon.

The second stage consists of flipping edges so that the final
triangulation is constrained Delaunay, which leads to a
more regular triangulation.

\section*{Initial triangulation\label{sec:triangle_ini}}

\begin{figure}[!h]
\centerline{
\includegraphics*[width=14cm]{face_split_main}}
\caption{Principle of face triangulation}
\label{fig:algo.ear_splitting}
\end{figure}

The algorithm used is based on the one described in \cite{Theussl:1998}.
Its principle is illustrated figure \ref{fig:algo.ear_splitting}.
We start by checking if the triangle defined by the first vertices
of the polygon, $(P_0, P_1, P_2)$ is an ``ear'', that is if it is
interior to the polygon and does not intersect it. As this is the case
on this example, we obtain a first triangle, ad we must then process
the remaining part of the polygon. At the next stage triangle
$(P_0, P_2, P_3)$ is also an ear, and may be removed.

At stage $2$, we see that the next triangle which is a candidate for
removal, $(P_0, P_3, P_4)$ is not an ear, as it is not contained in the
remaining polygon. We thus shift the indexes of vertices to consider,
and see at stage $4$ that triangle $(P_3, P_4, P_5)$
is an ear and may be removed.

The algorithm is built in such a way that a triangle is selected based on
the last vertex of the last triangle considered (starting from triangle
$(P_0, P_1, P_2)$). Thus, we consider at stage $5$ the triangle ending
with $P_5$, that is $(P_0, P_3, P_5)$.
Once this triangle is removed, the remaining polygon is a triangle,
and its handling is trivial.

\section*{Improving the Triangulation\label{sec:triangle_delaunay}}

We show on figures \ref{fig:algo.decoup_ex_1} and \ref{fig:algo.decoup_ex_2}
two examples of a triangulation on similar polygons whose vertices are
numbered in a different manner.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=2.5cm]{face_split_1}}
\caption{Triangulation example (1)}
\label{fig:algo.decoup_ex_1}
\end{figure}

\begin{figure}[!h]
\centerline{
\includegraphics*[height=2.5cm]{face_split_2}}
\caption{Triangulation example (2)}
\label{fig:algo.decoup_ex_2}
\end{figure}

\vfill

Not only is the obtained triangulation different, but it has a tendency
to produce very flat triangles. Once a first triangulation is obtained,
we apply a corrective algorithm, based on edge flips so as to respect
the Delaunay condition \cite{Shewchuck:1999}.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=4cm]{face_split_delaunay_crit}}
\caption{Delaunay condition (2)}
\label{fig:algo.delaunay_cond}
\end{figure}

This condition is illustrated figure \ref{fig:algo.delaunay_cond}.
In the first case, edge $\overline{P_i P_j}$ does not fulfill the
condition, as vertex $P_l$ is contained in the circle going through
$P_i, P_j, P_k$. In the second case, edge $\overline{P_k P_l}$ fulfills
this condition, as $P_i$ is not contained in the circle going through
$P_j, P_k, P_l$.

If triangles $(P_i, P_k, P_j)$ and $(P_i, P_j, P_l)$ originated from
the initial triangulation of a same polygon, they would thus be replaced
by triangles $(P_i, P_k, P_l)$ and $(P_l, P_k, P_j)$, which
fulfill the Delaunay condition. In the case of a quadrangle, we
would be done, but with a more complex initial polygon,
these new triangles could be replaced by others, depending on their
neighbors from the same polygon.

\begin{figure}[!h]
\centerline{
\includegraphics*[height=2.5cm]{face_split_delaunay_1}}
\caption{Edge flip example (1)}
\label{fig:algo.delaunay_ex_1}
\end{figure}

\begin{figure}[!h]
\centerline{
\includegraphics*[height=2cm]{face_split_delaunay_2}}
\caption{Edge flip example (2)}
\label{fig:algo.delaunay_ex_2}
\end{figure}

On figures \ref{fig:algo.delaunay_ex_1} and \ref{fig:algo.delaunay_ex_2},
we illustrate this algorithm on the same examples as before (figures
\ref{fig:algo.decoup_ex_1} and \ref{fig:algo.decoup_ex_2}). We see that
the final result is the same. In theory, the edge flipping algorithm
always converges. To avoid issues due to truncation errors, we allow
a tolerance before deciding to flip two edges. Thus, we allow that the
final triangulation only ``almost'' fulfill the Delaunay condition.

In some cases, especially that of perfect rectangles, two different
triangulations may both fulfill the Delaunay condition, notwithstanding
truncation errors. For periodicity, we must avoid having two periodic
faces triangulated in a non-periodic manner (for example, along
different diagonals of a quadrangle). In this specific case, we
triangulate only one face using the geometric algorithm, and then
apply the same triangulation to it's periodic face, rather then
triangulate both faces independently.

%-------------------------------------------------------------------------------
\section*{Unwarping algorithm\label{sec:unwarping}}
%-------------------------------------------------------------------------------

The unwarping algorithm is a smoother, its
principle is to mitigate the local defects by averaging
the mesh quality. It moves vertices using an iterative
process which is expected to converge to a mesh with better averaged
warping criteria.
\section*{Warping criterion in \CS\label{sec:warping_criterion}}
The warp face quality criterion in \CS represents the non coplanarity
in space of $N$ points $P_{i=1:N}$ $(N > 3)$.

Let $f$ be the face defined by $P_{i=1:Nbv(f)}$, the center
of gravity $O_{f}$ and $\overrightarrow{n_{f}}$ the face normal, the warping
criterion is calculated for each face by the ``maximum'' angle between
$\overrightarrow{P_{i}P_{i+1}}$ and $\overrightarrow{n_{f}}^{\perp}$
$\forall i\in [1,Nbv(f)]$ where $P_{Nbv(f)+1} = P_{1}$. For consistency
purposes, the warping criterion $warp_{f}$ is defined in degree for each
face of the mesh $\mathcal{M}$ as:

$$\forall f \in \mathcal{M}, \qquad warp_{f} = 90 -
\arccos\left(\max_{\forall i\in [1,Nbv(f)]}\left(\cos(
\overrightarrow{P_{i}P_{i+1}},\overrightarrow{n_{f}})
\right)\right)\frac{180}{\pi}$$

\section*{Unwarping method\label{sec:unwarping_method}}
The principle of unwarping algorithm is to move vertices in the midplane of the
faces using an iterating process. At each iteration the algorithm tries to
approach vertices from the midplane of the face without increasing the warping
of the neighbours faces.

The displacement is calculated by projecting vertices onto
the plane defined by $\overrightarrow{n_{f}}$ and the center of gravity $O$.

For the face $f$, $\forall i \in [1,Nbv(f)]$, the vertices $P_{i}$ are displaced
by the vector $\lambda^{f}_{P_{i}}$
$$\forall i \in [1,Nbv(f)], \qquad \lambda_{P_{i}}^{f}
= (\overrightarrow{P_{i}O_{f}}.\overrightarrow{n_{f}})
\overrightarrow{n_{f}}$$

In most cases, a vertex is shared by many faces $f_{j=1:Nbf(P_{i})}$, and its final
displacement is:

$$\forall f \in \mathcal{M}, \; \forall i \in [1,Nbv(f)], \qquad
 \lambda_{P_{i}}^{f}=\sum_{j=1:Nbf(P_{i})}\lambda^{f_{j}}_{P_{i}}$$

This displacement technique may cause problems because the contributions
$\lambda^{f_{l}}_{P}$ and $\lambda^{f_{k}}_{P}$ can be ``contradictory''.
Moreover, if a small and a large face are neighbours, the large face contribution
imposes a too big displacement to the shared vertices and the warping criterion
can be deteriorated.

\subsection*{Displacements control\label{sec:unwarping_mvt}}
The weighting coefficients shown below allow us to reduce the conflicting
contributions and equilibrate the contributions between small and large faces.
\paragraph*{Face weighting}
Every iteration, for each face the warping criterion is computed.
It is used to give more weight to the warp faces. After the renormalisation of the
warping criterion, a new displacement formula is obtained:

$$\forall f \in \mathcal{M}, \; \forall i \in [1,Nbv(f)], \qquad
\lambda_{P_{i}}^{f}=\sum_{j=1:Nbf(P_{i})}
\frac{warp_{f_{j}}}{\max_{f \in \mathcal{M}} warp_{f}}\lambda^{f_{j}}_{P_{i}}$$

\paragraph*{Vertex weighting}
Every iteration, for each vertex $P \in \mathcal{M} $,
the vertex tolerance is computed:
$$\forall P \in \mathcal{M}, \qquad
vtxtol_{P} = \frac{\min_{P \in nb(P)} PP\prime}
{\max_{Q\in \mathcal{M}}{\min_{Q\prime\in nb(Q)} QQ\prime}}$$
where $nb(Q)$ are the first neighbors of the point $Q$.

This criterion is used to reduce the vertex displacement when a vertex lies
on an edge much smaller than the average length. Another vertex tolerance
may have been chosen. For example a more ``local coefficient'' can be
defined as:
$$\forall P \in \mathcal{M}, \qquad
vtxtol_{P}^{local} = \frac{\min_{P\prime \in nb(P)} PP\prime}
{\max_{P\prime \in nb(P)} PP\prime}$$
This ``local coefficient'' has been noticed to be less efficient than the first one.
That is why the first definition is used in \CS.

The unwarping displacement is updated as below:

$$\forall f \in \mathcal{M}, \; \forall i \in [1,Nbv(f)], \qquad
\lambda_{P_{i}}^{f}=vtxtol_{P_{i}}\sum_{j=1:Nbf(P_{i})}
\frac{warp_{f_{j}}}{\max_{f \in \mathcal{M}} warp_{f}}\lambda^{f_{j}}_{P_{i}}$$

\paragraph*{Movement coefficient}
To ensure a better convergence, all vertices' movements are multiplied by a scale
factor $Cm$ (where $Cm \leqslant 1$). This coefficient helps to converge to the
best solution by preventing a too big movement in the wrong direction.
In \CS the default value is set to $0.10$.

The unwarping displacement is then:

$$\forall f \in \mathcal{M}, \; \forall i \in [1,Nbv(f)], \qquad
\lambda_{P_{i}}^{f}=Cm*vtxtol_{P_{i}}\sum_{j=1:Nbf(P_{i})}
\frac{warp_{f_{j}}}{\max_{f \in \mathcal{M}} warp_{f}}\lambda^{f_{j}}_{P_{i}}$$

\paragraph*{Maximum displacement}
To reduce the ``cell reversal'' risk, the maximum displacement is limited for
each vertex $P \in \mathcal{M}$ to $Md = 0.10\,\min_{P \in nb(P)} PP\prime$.

Finally, the complete unwarping displacement formula is defined as:

\begin{equation*}
\begin{split}
\forall f \in \mathcal{M},& \; \forall i \in [1,Nbv(f)], \qquad \\
&\lambda_{P_{i}}^{f}=\min(Cm*vtxtol_{P_{i}}\sum_{j=1:Nbf(P_{i})}
\frac{warp_{f_{j}}}{\max_{f \in \mathcal{M}}
warp_{f}}\lambda^{f_{j}}_{P_{i}},\; Md)
\end{split}
\end{equation*}


\subsection*{Stop criterion\label{sec:unwarping_stop}}
The algorithm automatically stops according to the warp face criterion.

\paragraph*{$1^{st}$ case: the algorithm converges}
The algorithm stops when, at the $i^{th}$ iteration, the relation below is verified.

$$ 1 - \frac{\max_{f\in \mathcal{M}} warp_{f}^{i}}{\max_{f\in \mathcal{M}}
warp_{f}^{i-1}} < 1.E-4$$

\paragraph*{$2^{nd}$ case: the algorithm diverges}

The algorithm stops when at the $i^{th}$ iteration the relation below is verified.

$$\frac{\max_{f\in \mathcal{M}} warp_{f}^{i}}{\max_{f\in M}
warp_{f}^{i-1}} > 1.05$$

It means that the current iteration degrades the previous one.
The obtained mesh is the result of the $(i-1)^{th}$ iteration.

\paragraph*{$3^{rd}$ case: the maximum number of iterations is reached}
The algorithm stops after $N_{max}$ iterations ($51$ by default in \CS).

\subsection*{Specific treatment for boundary faces
\label{sec:unwarping_boundary}}
The unwarping algorithm may modify the mesh geometry. The function
\texttt{fit\_by\_feature} allows to fix boundary faces according to a feature angle.
The feature angle between a vertex and one of its adjacent faces is defined
by the angle between the vertex normal and the face normal.

A vertex normal is defined by the average of the normals of the
faces sharing this vertex.

This function fixes a vertex if one of its feature angles is less than
$cos(\theta)$ where $\theta$ is the maximum feature angle (in degrees)
defined by the user.
In fact, if $\theta = 0^{\circ}$ all boundary vertices will be fixed, and
if $\theta = 90^{\circ}$ all boundary vertices will be free.

Fixing all boundary vertices ensures the geometry is preserved, but reduces
the smoothing algorithm's effectiveness.
