 # Contents of /trunk/doc/cookbook/TEXT/onedheatdiff001.tex

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 1 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3 % 4 % Copyright (c) 2003-2009 by University of Queensland 5 % Earth Systems Science Computational Center (ESSCC) 6 7 % 8 % Primary Business: Queensland, Australia 9 % Licensed under the Open Software License version 3.0 10 11 % 12 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 13 14 \section{One Dimensional Heat Diffusion in an Iron Rod} 15 %\label{Sec:1DHDv0} 16 We will start by examining a simple one dimensional heat diffusion example. While this exact problem is not strictly relevant to earth sciences; it will provide a good launch pad to build our knowledge of \ESCRIPT and how to solve simple partial differential equations (PDEs) \footnote{In case you wonder what a 17 \textit{partial differential equation} wikipedia provides a 18 comprehensive introducton at 19 \url{http://en.wikipedia.org/wiki/Partial_differential_equation}, 20 but things should become a bit clearer when you read further in the cookbook.} 21 22 23 24 Start by imagining we have a simple cold iron bar at a constant temperature of zero. 25 \TODO{Add a diagram to explain the set-up.} 26 The bar is perfectly insulated on all sides and at one end we will apply a heating element of some description. Intuition tells us that as heat is applied, that energy will disperse through the bar with time until the bar reaches the same temperature as the heat source. At this point the temperature in the bar will be constant and the same as the heat source. 27 28 We can model this problem using the one dimensional heat diffusion equation. A detailed discussion on how the heat diffusion equation is derived can be found at 29 \url{http://online.redwoods.edu/instruct/darnold/DEProj/sp02/AbeRichards/paper.pdf} 30 Heat diffusion equation is defined as: 31 \begin{equation} 32 \rho c\hackscore p \frac{\partial T}{\partial t} - \kappa \frac{\partial^{2} T}{\partial x^{2}} = q\hackscore H 33 \label{eqn:hd} 34 \end{equation} 35 where $\rho$ is the material density, $c\hackscore p$ is the specific heat and $\kappa$ is the temperature diffusion constant. All of these values are readily available for most materials or can be established through pre-defined experimentation techniques. \TODO{Give some numbers for eg. granite}. 36 The heatsource is on the right hand side of \eqref{eqn:hd} as $q_{H}$, this could be a constant or defined by an expression \TODO{Give an example}. There are also two partial derivatives in \eqref{eqn:hd}, $\frac{\partial T}{\partial t}$ describes the change in temperature with time while $\frac{\partial T}{\partial x}$ describes the spatial change to temperature. There is only a single spatial dimension to our problem, and so our temperature solution $T$ is only dependent on the time $t$ and our position along the iron bar $x$. 37 38 \TODO{Explain the concept of time discritzation} 39 40 To solve this equation we will write a simple python script which uses \ESCRIPT and 41 \FINLEY of the \ESYS module. At this point we assume that you have some basic 42 understanding of the python programming language. There are in fact a large number of 43 python tutorial available on-line, for instance 44 \begin{itemize} 45 \item This is a very crisp introduction \url{http://hetland.org/writing/instant-python.html}. It covers everthing you need to get started with \ESCRIPT. 46 \item A nice and easy to follow introduction: \url{http://www.sthurlow.com/python/} 47 \item An other crip tutorial \url{http://www.zetcode.com/tutorials/pythontutorial/}. 48 \item A very comprehensive tutorial from the python authors: \url{http://www.python.org/doc/2.5.2/tut/tut.html}. It covers much more than what you will ever need for \ESCRIPT. 49 \item an other comprehensive tutorial: \url{http://www.tutorialspoint.com/python/index.htm} 50 \end{itemize} 51 In the following we will develop the script to solve the heat equation step-by-step. 52 The first step is to import the necessary libraries. 53 \begin{verbatim} 54 from esys.escript import * 55 from esys.escript.linearPDEs import SingleLinearPDE 56 from esys.finley import Rectangle 57 import os 58 \end{verbatim} 59 It is generally a good idea to import all of \verb escript , although if you know the packages you need you can specify them individually. The function \verb|LinearPDE| has been imported individually for ease of use later in the script. \verb|Rectangle| is going to be our type of domain and the \verb|os| package is needed to handle file outputs once our PDE has been solved. 60 61 Once our libraries dependancies have been established, defining the problem specific variables is the next step. In general the number of variables needed will vary between problems. These variables belong to two categories. They are either directly related to the PDE and can be used as inputs into the escript solver, or they are script variables used to control internal functions and iterations in our problem. For this PDE there are a number of constants which will need values. Firstly, the domain upon which we wish to solve our problem needs to be defined. There are many different types of domains in escript. We will demonstrate a few in later tutorials but for our iron rod we will simply use a rectangular domain. 62 63 Using a rectangular domain simplifies a \textit{3D} object into a single dimension. The iron rod will have a lengthways cross section that looks like a rectangle. As a result we do not need to model the volume of the rod because a cylinder is symmetrical about its center. There are four arguments we must consider when we decide to create a rectangular domain, the model length, width and step size in each direction. When defining the size of our problem it will help us determine appropriate values for our domain arguments. If we make our dimensions large but our step sizes very small we will to a point, increase the accuracy of our solution. Unfortunately we also increase the number of calculations that must be solved per time step. This means more computational time is required to produce a solution. In our \textit{1D} problem we will define our bar as being 1 metre long. An appropriate \verb|ndx| would be 1 to 10\% of the length. Our \verb|ndy| need only be 1, uhis is because our problem stipulates no partial derivatives in the $y$ direction. This means the temperature does not vary with $y$. Thus the domain perameters can be defined as follows: 64 \TODO{Use the Unit module!} 65 \begin{verbatim} 66 #Domain related. 67 mx = 1 #meters - model lenght 68 my = .1 #meters - model width 69 ndx = 100 # steps in x direction 70 ndy = 1 # steps in y direction 71 \end{verbatim} 72 The material constants and the temperature variables must also be defined. For the iron rod in the model they are defined as: 73 \begin{verbatim} 74 #PDE related 75 q=473. #Kelvin - our heat source temperature 76 Tref = 273. # Kelvin - starting temp of iron bar 77 rho = 7874. #kg/m^{3} density of iron 78 cp = 449. #j/Kg.K 79 rhocp = rho*cp 80 eta = 0 #radiation condition 81 kappa = 68. #temperature diffusion constant 82 \end{verbatim} 83 \TODO{remove radiation condition and -if required- introduce in a second example} 84 Finally, to control our script we will have to specify our timing controls and where we would like to save the output from the solver. This is simple enough: 85 \begin{verbatim} 86 #Script/Iteration Related 87 t=0 #our start time, usually zero 88 tend=5.*60. #seconds - time to end simulation 89 outputs = 200 # number of time steps required. 90 h=(tend-t)/outputs #size of time step 91 i=0 #loop counter 92 #the folder to put our outputs in, leave blank "" for script path 93 #note this folder path must exist to work 94 save_path = "data/onedheatdiff001" 95 \end{verbatim} 96 Now that we know our inputs we will build a domain using the \verb Rectangle() function from \verb Finley . The four arguments allow us to define our domain \verb rod as: 97 \begin{verbatim} 98 rod = Rectangle(l0=mx,l1=my,n0=ndx,n1=ndy) 99 \end{verbatim} 100 In this form \verb rod does not represent any discrete points, but rather an area of \verb ndx*ndy cells that fit into a rectangular space with opposing vertices at the origin and the point \verb [mx,my] . The reason for this is so that the discrete points used in the solution can be specified by the user. That is, are discrete points used at the corners of each cell, the middle point of a cell or halfway along each side of the cell etc. Fortunately \verb escript offers an easy way to extract finite points from the domain \verb|rod| using the domain property function \verb|getX()| . This function uses the vertices of each cell as finite points to solve in the solution. If we let \verb|x| be these finite points, then: 101 \begin{verbatim} 102 x = rod.getX() 103 \end{verbatim} 104 With a domain and all our required variables established, it is now possible to set up our PDE so that it can be solved by escript. The first step is to define the type of PDE that we are trying to solve in each time step. In this example it is a single linear PDE (in comparison to a system of PDEs which will discussed later) 105 we can define it by: 106 \begin{verbatim} 107 mypde=LinearSinglePDE(rod) 108 \end{verbatim} 109 In the next step we need to define the coefficients of the PDE. The linear 110 PDEs in \ESCRIPT provide a general interface to do this. Here we will only discuss a simplified form that suits our heat diffusion problem and refer to the \ESCRIPT user's guide for the general case. This simpler form 111 \footnote{In the form of the \ESCRIPT users guide which uses the Einstein convention 112 this equation is written as 113 $-(A\hackscore{jl} u\hackscore{,l})\hackscore{,j}+D u =Y$} 114 is described by 115 \begin{equation}\label{eqn:commonform nabla} 116 -\nabla.(A.\nabla u) + Du = f 117 \end{equation} 118 where $A$, $D$ and $f$ are known values. 119 The symbol $\nabla$ which is called the \textit{Nabla operator} or \textit{del operator} represents 120 the spatial derivative of what ever stands next to the right from it. Lets assume for a moment that we deal with a one-dimensional problem then 121 \begin{equation} 122 \nabla = \frac{\partial}{\partial x} 123 \end{equation} 124 and we can write equation \ref{eqn:commonform nabla} as 125 \begin{equation}\label{eqn:commonform} 126 -A\frac{\partial^{2}u}{\partial x^{2}} + Du = f 127 \end{equation} 128 if $A$ is constant. This is exactly the equation we need to solve 129 in each time step as described in equation~\ref{XXXXX}. When comparing equations \eqref{eqn:hd} and \eqref{eqn:commonform} we see that; 130 \begin{equation} 131 A = \kappa; D = \frac{\rho c \hackscore{p}}{h}; f = q \hackscore{H} 132 \end{equation} 133 134 We need to revisit the general PDE~\ref{eqn:commonform nabla} under the light of a 135 two dimensional domain. As pointed out earlier \ESCRIPT is not designed 136 to solve one-dimensional problems so the general PDE~\ref{eqn:commonform nabla} 137 needs to be read as a higher dimensional problem. In the case of 138 two spatial dimensions the Nable operator has in fact 139 two components $\nabla = (\frac{\partial}{\partial x} 140 \frac{\partial}{\partial y})$. If we spell out the general PDE~\ref{eqn:commonform nabla} and assume a constant coefficient $A$ it then takes the form 141 \begin{equation}\label{eqn:commonform2D} 142 -A\hackscore{00}\frac{\partial^{2}u}{\partial x^{2}} 143 -A\hackscore{01}\frac{\partial^{2}u}{\partial x\partial y} 144 -A\hackscore{10}\frac{\partial^{2}u}{\partial y\partial x} 145 -A\hackscore{11}\frac{\partial^{2}u}{\partial y^{2}} 146 + Du = f 147 \end{equation} 148 We notice that for the higher dimensional case $A$ becomes a matrix. It is also 149 important to notice that the usage of the Nable operator creates 150 a compact formulation which is also independend from the spatial dimension. 151 So to make the general PDE~\ref{eqn:commonform2D} one dimensional as 152 shown in~\ref{eqn:commonform} we need to set 153 \begin{equation}\label{eqn:commonform2D} 154 A\hackscore{00}=A; A\hackscore{01}=A\hackscore{10}=A\hackscore{11} 155 \end{equation} 156 157 158 BLA-BLA 159 \TODO{explain boundray condition; ignore radiation} 160 161 162 163 164 165 Because we have a symmetrical problem we will also need to set the symmetry on by: 166 \begin{verbatim} 167 myPDE.setSymmetryOn() 168 \end{verbatim} 169 To input the PDE into \esc it must be compared with the general form\footnote{Available in section ?? of the users guide to \esc }. For most simple PDEs however, the general form is over complicated and confusing. Thus for this example, we will use a simplified version 170 171 172 173 174 Additionally we must also consider the boundary conditions of our PDE. They take the form: 175 \begin{equation} 176 \eta \hackscore{j} A\hackscore{jl} u\hackscore{,l} + du = y 177 \end{equation} 178 179 180 NEED TO WORK ON THIS SECTION 181 182 We now need to specify Our boundary conditions and initial values. The initial values required to solve this PDE are temperatures for each discrete point in our domain that we wish to solve for. We will set our bar to: 183 \begin{verbatim} 184 T = Tref 185 \end{verbatim} 186 Boundary conditions are a little more difficult. Fortunately the escript solver will handle our insulated boundary conditions. However, we will need to apply our heat source $q_{H}$ to the end of the bar at $x=0$ . escript makes this easy by letting us define areas in our domain. To retrieve all the finite points in our domain we will use 187 188 END WORK ON THIS SECTION 189 190 \section{Plot total heat} 191 \TODO{show the script} 192 193 \TODO{explain how to use matlibplot to visualize the total heat integral(rho*c*T) over time} 194 195 \section{Plot Temperature Distribution} 196 \TODO{explain how to use matlibplot to visualize T} 197 198 199 \TODO{Move this to the 2D section as an advanced topic} 200 The final stage to our problem is exporting the data we have generated and turn our data and visualisation. It is best to export the calculated solutions at each time increment. escript has the inbuilt function \verb|saveVTK()| which makes this step very easy. saveVTK takes two arguments, the path and the filename. We are goind to use the \verb|os.path.join| command to join a subdirectory which must already exist with a file name. The string opperator \verb|%| allows us to increment our file names with the value \verb|i|. In substring \verb %03d does a number of things; 201 \begin{itemize} 202 \item \verb 0 becomes the padding number; 203 \item \verb 3 tells us the number of padding numbers that are required; and 204 \item \verb d indicates the end of the \verb % operator. 205 \end{itemize} 206 To increment a \verb %i is required directly after the operation the string is involed in. The second arugment of the \verb saveVTK function is the \verb sol=T where \verb T is the solution of our PDE for a given time steo. Thus the export command becomes: 207 \begin{verbatim} 208 saveVTK(os.path.join(save_path,"data%03d.xml") %i,sol=T) 209 \end{verbatim} 210 Visualisation is then implemented through \verb mayavi which provides a command line of gui interface to develop plots to suit our output data. For this example a surface plot is appropriate.