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% http://www.uq.edu.au
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% Primary Business: Queensland, Australia
% Licensed under the Apache License, version 2.0
% http://www.apache.org/licenses/LICENSE-2.0
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% Development until 2012 by Earth Systems Science Computational Center (ESSCC)
% Development 2012-2013 by School of Earth Sciences
% Development from 2014 by Centre for Geoscience Computing (GeoComp)
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\section{Stokes Flow}
\label{STOKES FLOW CHAP}
In this section we will look at Computational Fluid Dynamics (CFD) to simulate
the flow of fluid under the influence of gravity.
The \class{StokesProblemCartesian} class will be used to calculate the velocity
and pressure of the fluid.
The fluid dynamics is governed by the Stokes equation. In geophysical problems
the velocity of fluids is low; that is, the inertial forces are small compared
with the viscous forces, therefore the inertial terms in the Navier-Stokes
equations can be ignored.
For a body force $f$, the governing equations are given by:
%
\begin{equation}
\nabla \cdot (\eta(\nabla \vec{v} + \nabla^{T} \vec{v})) - \nabla p = -f,
\label{GENERAL NAVIER STOKES}
\end{equation}
%
with the incompressibility condition
%
\begin{equation}
\nabla \cdot \vec{v} = 0.
\label{INCOMPRESSIBILITY}
\end{equation}
%
where $p$, $\eta$ and $f$ are the pressure, viscosity and body forces, respectively.
Alternatively, the Stokes equations can be represented in Einstein summation
tensor notation (compact notation):
%
\begin{equation}
-(\eta(v_{i,j} + v_{j,i})),_{j} - p,_{i} = f_{i},
\label{GENERAL NAVIER STOKES COM}
\end{equation}
%
with the incompressibility condition
%
\begin{equation}
-v_{i,i} = 0.
\label{INCOMPRESSIBILITY COM}
\end{equation}
%
The subscript comma $i$ denotes the derivative of the function with respect to $x_{i}$.
%A linear relationship between the deviatoric stress $\sigma^{'}_{ij}$ and the stretching $D_{ij} = \frac{1}{2}(v_{i,j} + v_{j,i})$ is defined as \cite{GROSS2006}:
%
%\begin{equation}
%\sigma^{'}_{ij} = 2\eta D^{'}_{ij},
%\label{STRESS}
%\end{equation}
%
%where the deviatoric stretching $D^{'}_{ij}$ is defined as
%
%\begin{equation}
%D^{'}_{ij} = D^{'}_{ij} - \frac{1}{3}D_{kk}\delta_{ij}.
%\label{DEVIATORIC STRETCHING}
%\end{equation}
%
%where $\delta_{ij}$ is the Kronecker $\delta$-symbol, which is a matrix wi-th ones for its diagonal entries ($i = j$) and zeros for the remaining entries ($i \neq j$).
The body force $f$ in \eqn{GENERAL NAVIER STOKES COM} is the gravity acting in
the $x_{3}$ direction and is given as $f=-g\rho\delta_{i3}$.
The Stokes equation is a saddle point problem, and can be solved using a Uzawa scheme.
A class called \class{StokesProblemCartesian} in \escript can be used to solve
for velocity and pressure. A more detailed discussion of the class can be
found in Chapter \ref{MODELS CHAPTER}.
In order to keep numerical stability and satisfy the Courant-Friedrichs-Lewy condition (CFL condition)\index{Courant number}\index{CFL condition}, the
time-step size needs to be kept below a certain value.
The Courant number \index{Courant number} is defined as:
%
\begin{equation}
C = \frac{v \delta t}{h}
\label{COURANT}
\end{equation}
%
where $\delta t$, $v$, and $h$ are the time-step, velocity, and the width of
an element in the mesh, respectively. The velocity $v$ may be chosen as the
maximum velocity in the domain. In this problem the time-step size was
calculated for a Courant number of $0.4$.

The following \PYTHON script is the setup for the Stokes flow simulation, and
is available in the \ExampleDirectory as \file{fluid.py}.
It starts off by importing the classes, such as the \class{StokesProblemCartesian}
class, for solving the Stokes equation and the incompressibility condition for
velocity and pressure.
Physical constants are defined for the viscosity and density of the fluid,
along with the acceleration due to gravity.
Solver settings are set for the maximum iterations and tolerance; the default
solver used is PCG (Preconditioned Conjugate Gradients).
The mesh is defined as a rectangle to represent the body of fluid.
We are using $20 \times 20$ elements with piecewise linear elements for the
pressure and for velocity but the elements are subdivided for the velocity.
This approach is called \textit{macro elements}\index{macro elements} and
needs to be applied to make sure that the discretized problem has a unique
solution, see~\cite{LBB} for details\footnote{Alternatively, one can use
second order elements for the velocity and first order elements for pressure
on the same element. You can set \code{order=2} in \class{esys.finley.Rectangle}.}.
The fact that pressure and velocity are represented in different ways is
expressed by
\begin{python}
  velocity=Vector(0., Solution(mesh))
  pressure=Scalar(0., ReducedSolution(mesh))
\end{python}
The gravitational force is calculated based on the fluid density and the
acceleration due to gravity.
The boundary conditions are set for a slip condition at the base and the left
face of the domain. At the base fluid movement in the $x_{0}$-direction
is free, but fixed in the $x_{1}$-direction, and similarly at the left
face fluid movement in the $x_{1}$-direction is free  but fixed in
the $x_{0}$-direction.
An instance of the \class{StokesProblemCartesian} class is defined for the
given computational mesh, and the solver tolerance set.
Inside the \code{while} loop, the boundary conditions, viscosity and body
force are initialized.
The Stokes equation is then solved for velocity and pressure.
The time-step size is calculated based on the Courant-Friedrichs-Lewy condition
(CFL condition)\index{Courant number}\index{CFL condition}, to ensure stable solutions.
The nodes in the mesh are then displaced based on the current velocity and
time-step size, to move the body of fluid.
The output for the simulation of velocity and pressure is then saved to a file
for visualization.
%
\begin{python}
  from esys.escript import *
  import esys.finley
  from esys.escript.linearPDEs import LinearPDE
  from esys.escript.models import StokesProblemCartesian
  from esys.weipa import saveVTK

  # physical constants
  eta=1.
  rho=100.
  g=10.

  # solver settings
  tolerance=1.0e-4
  max_iter=200
  t_end=50
  t=0.0
  time=0
  verbose=True

  # define mesh
  H=2.
  L=1.
  W=1.
  mesh = esys.finley.Rectangle(l0=L, l1=H, order=-1, n0=20, n1=20)
  coordinates = mesh.getX()

  # gravitational force
  Y=Vector(0., Function(mesh))
  Y[1] = -rho*g

  # element spacing
  h = Lsup(mesh.getSize())

  # boundary conditions for slip at base
  boundary_cond=whereZero(coordinates[1])*[0.0,1.0]
      +whereZero(coordinates[0])*[1.0,0.0]

  # velocity and pressure vectors
  velocity=Vector(0., Solution(mesh))
  pressure=Scalar(0., ReducedSolution(mesh))

  # Stokes Cartesian
  solution=StokesProblemCartesian(mesh)
  solution.setTolerance(tolerance)

  while t <= t_end:
    print(" ----- Time step = %s -----"%t)
    print("Time = %s seconds"%time)

    solution.initialize(fixed_u_mask=boundary_cond, eta=eta, f=Y)
    velocity,pressure=solution.solve(velocity,pressure,max_iter=max_iter, \
                                     verbose=verbose)

    print("Max velocity =", Lsup(velocity), "m/s")

    # CFL condition
    dt=0.4*h/(Lsup(velocity))
    print("dt =", dt)

    # displace the mesh
    displacement = velocity * dt
    coordinates = mesh.getX()
    newx=interpolate(coordinates + displacement, ContinuousFunction(mesh))
    mesh.setX(newx)

    time += dt

    vel_mag = length(velocity)

    #save velocity and pressure output
    saveVTK("vel.%2.2i.vtu"%t, vel=vel_mag, vec=velocity, pressure=pressure)
    t = t+1.
\end{python}
%
The results from the simulation can be viewed with \mayavi, by executing the
following command:
\begin{verbatim}
mayavi2 -d vel.00.vtu -m Surface
\end{verbatim}
%
Colour-coded scalar maps and velocity flow fields can be viewed by selecting
them in the menu.
The time-steps can be swept through to view a movie of the simulation.
\fig{FLUID OUTPUT} shows the simulation output.
Velocity vectors and a colour map for pressure are shown.
As the time progresses the body of fluid falls under the influence of gravity.
%
\begin{figure}[ht]
\center
\subfigure[t=1]{\label{FLOW OUTPUT 01}\includegraphics[height=5cm]{stokes-fluid-t01}}
\hspace{1.6cm}
\subfigure[t=20]{\label{FLOW OUTPUT 10}\includegraphics[height=5cm]{stokes-fluid-t10}}
\hspace{1.6cm}
\subfigure[t=30]{\label{FLOW OUTPUT 20}\includegraphics[height=5cm]{stokes-fluid-t20}}\\
\subfigure[t=40]{\label{FLOW OUTPUT 30}\includegraphics[height=5cm]{stokes-fluid-t30}}
\hspace{1cm}
\subfigure[t=50]{\label{FLOW OUTPUT 40}\includegraphics[height=5cm]{stokes-fluid-t40}}
\hspace{1cm}
\subfigure[t=60]{\label{FLOW OUTPUT 50}\includegraphics[height=5cm]{stokes-fluid-t50}}
%\includegraphics[scale=0.25]{stokes-fluid-colorbar}
\caption{Simulation output for Stokes flow. Fluid body starts off as a
rectangular shape, then progresses downwards under the influence of gravity.
Colour coded distribution represents the scalar values for pressure.
Velocity vectors are displayed at each node in the mesh to show the flow field.
Computational mesh used was 20$\times$20 elements.}
\label{FLUID OUTPUT}
\end{figure}
%
The view used here to track the fluid is the Lagrangian view, since the mesh
moves with the fluid. One of the disadvantages of using the Lagrangian view is
that the elements in the mesh become severely distorted after a period of time
and introduce solver errors. To get around this limitation the Level Set
Method can be used, with the Eulerian point of view for a fixed mesh.
%The Level Set Method is discussed in Section \ref{LEVELSET CHAP}.

