 # Contents of /trunk/doc/cookbook/example01.tex

Revision 2979 - (show annotations)
Tue Mar 9 02:54:32 2010 UTC (9 years, 10 months ago) by ahallam
File MIME type: application/x-tex
File size: 37735 byte(s)
cookbook review final final 3.1 - artak and tony corrections

 1 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3 % 4 % Copyright (c) 2003-2010 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 \begin{figure}[h!] 15 \centerline{\includegraphics[width=4.in]{figures/onedheatdiff001}} 16 \caption{Example 1: Temperature differential along a single interface between 17 two granite blocks.} 18 \label{fig:onedgbmodel} 19 \end{figure} 20 21 \section{Example 1: One Dimensional Heat Diffusion in Granite} 22 \label{Sec:1DHDv00} 23 24 The first model consists of two blocks of isotropic material, for instance 25 granite, sitting next to each other. 26 Initial temperature in \textit{Block 1} is \verb|T1| and in \textit{Block 2} is 27 \verb|T2|. 28 We assume that the system is insulated. 29 What would happen to the temperature distribution in each block over time? 30 Intuition tells us that heat will be transported from the hotter block to the 31 cooler one until both 32 blocks have the same temperature. 33 34 \subsection{1D Heat Diffusion Equation} 35 We can model the heat distribution of this problem over time using one 36 dimensional heat diffusion equation\footnote{A detailed discussion on how the 37 heat diffusion equation is derived can be found at 38 \url{ 39 http://online.redwoods.edu/instruct/darnold/DEProj/sp02/AbeRichards/paper.pdf}}; 40 which is defined as: 41 \begin{equation} 42 \rho c\hackscore p \frac{\partial T}{\partial t} - \kappa \frac{\partial^{2} 43 T}{\partial x^{2}} = q\hackscore H 44 \label{eqn:hd} 45 \end{equation} 46 where $\rho$ is the material density, $c\hackscore p$ is the specific heat and 47 $\kappa$ is the thermal 48 conductivity\footnote{A list of some common thermal conductivities is available 49 from Wikipedia 50 \url{http://en.wikipedia.org/wiki/List_of_thermal_conductivities}}. Here we 51 assume that these material 52 parameters are \textbf{constant}. 53 The heat source is defined by the right hand side of \refEq{eqn:hd} as 54 $q\hackscore{H}$; this can take the form of a constant or a function of time and 55 space. For example $q\hackscore{H} = q\hackscore{0}e^{-\gamma t}$ where we have 56 the output of our heat source decaying with time. There are also two partial 57 derivatives in \refEq{eqn:hd}; $\frac{\partial T}{\partial t}$ describes the 58 change in temperature with time while $\frac{\partial ^2 T}{\partial x^2}$ is 59 the spatial change of temperature. As there is only a single spatial dimension 60 to our problem, our temperature solution $T$ is only dependent on the time $t$ 61 and our signed distance from the the block-block interface $x$. 62 63 \subsection{PDEs and the General Form} 64 It is possible to solve PDE \refEq{eqn:hd} analytically and obtain an exact 65 solution to our problem. However, it is not always practical to solve the 66 problem this way. Alternatively, computers can be used to find the solution. To 67 do this, a numerical approach is required to discretise 68 the PDE \refEq{eqn:hd} across time and space, this reduces the problem to a 69 finite number of equations for a finite number of spatial points and time steps. 70 These parameters together define the model. While discretisation introduces 71 approximations and a degree of error, a sufficiently sampled model is generally 72 accurate enough to satisfy the accuracy requirements for the final solution. 73 74 Firstly, we discretise the PDE \refEq{eqn:hd} in time. This leaves us with a 75 steady linear PDE which involves spatial derivatives only and needs to be solved 76 in each time step to progress in time. \esc can help us here. 77 78 For time discretization we use the Backwards Euler approximation 79 scheme\footnote{see \url{http://en.wikipedia.org/wiki/Euler_method}}. It is 80 based on the 81 approximation 82 \begin{equation} 83 \frac{\partial T(t)}{\partial t} \approx \frac{T(t)-T(t-h)}{h} 84 \label{eqn:beuler} 85 \end{equation} 86 for $\frac{\partial T}{\partial t}$ at time $t$ 87 where $h$ is the time step size. This can also be written as; 88 \begin{equation} 89 \frac{\partial T}{\partial t}(t^{(n)}) \approx \frac{T^{(n)} - T^{(n-1)}}{h} 90 \label{eqn:Tbeuler} 91 \end{equation} 92 where the upper index $n$ denotes the n\textsuperscript{th} time step. So one 93 has 94 \begin{equation} 95 \begin{array}{rcl} 96 t^{(n)} & = & t^{(n-1)}+h \\ 97 T^{(n)} & = & T(t^{(n-1)}) \\ 98 \end{array} 99 \label{eqn:Neuler} 100 \end{equation} 101 Substituting \refEq{eqn:Tbeuler} into \refEq{eqn:hd} we get; 102 \begin{equation} 103 \frac{\rho c\hackscore p}{h} (T^{(n)} - T^{(n-1)}) - \kappa \frac{\partial^{2} 104 T^{(n)}}{\partial x^{2}} = q\hackscore H 105 \label{eqn:hddisc} 106 \end{equation} 107 Notice that we evaluate the spatial derivative term at the current time 108 $t^{(n)}$ - therefore the name \textbf{backward Euler} scheme. Alternatively, 109 one can evaluate the spatial derivative term at the previous time $t^{(n-1)}$. 110 This 111 approach is called the \textbf{forward Euler} scheme. This scheme can provide 112 some computational advantages, which 113 are not discussed here. However, the \textbf{forward Euler} scheme has a major 114 disadvantage. Namely, depending on the 115 material parameters as well as the domain discretization of the spatial 116 derivative term, the time step size $h$ needs to be chosen sufficiently small to 117 achieve a stable temperature when progressing in time. Stabiliy is achieved if 118 the temperature does not grow beyond its initial bounds and become 119 non-physical. 120 The backward Euler scheme, which we use here, is unconditionally stable meaning 121 that under the assumption of a 122 physically correct problem set-up the temperature approximation remains physical 123 for all time steps. 124 The user needs to keep in mind that the discretization error introduced by 125 \refEq{eqn:beuler} 126 is sufficiently small, thus a good approximation of the true temperature is 127 computed. It is 128 therefore very important that any results are viewed with caution. For example, 129 one may compare the results for different time and spatial step sizes. 130 131 To get the temperature $T^{(n)}$ at time $t^{(n)}$ we need to solve the linear 132 differential equation \refEq{eqn:hddisc} which only includes spatial 133 derivatives. To solve this problem 134 we want to use \esc. 135 136 In \esc any given PDE can be described by the general form. For the purpose of 137 this introduction we illustrate a simpler version of the general form for full 138 linear PDEs which is available in the \esc user's guide. A simplified form that 139 suits our heat diffusion problem\footnote{The form in the \esc users guide which 140 uses the Einstein convention is written as 141 $-(A\hackscore{jl} u\hackscore{,l})\hackscore{,j}+D u =Y$} 142 is described by; 143 \begin{equation}\label{eqn:commonform nabla} 144 -\nabla\cdot(A\cdot\nabla u) + Du = f 145 \end{equation} 146 where $A$, $D$ and $f$ are known values and $u$ is the unknown solution. The 147 symbol $\nabla$ which is called the \textit{Nabla operator} or \textit{del 148 operator} represents 149 the spatial derivative of its subject - in this case $u$. Lets assume for a 150 moment that we deal with a one-dimensional problem then ; 151 \begin{equation} 152 \nabla = \frac{\partial}{\partial x} 153 \end{equation} 154 and we can write \refEq{eqn:commonform nabla} as; 155 \begin{equation}\label{eqn:commonform} 156 -A\frac{\partial^{2}u}{\partial x^{2}} + Du = f 157 \end{equation} 158 if $A$ is constant. To match this simplified general form to our problem 159 \refEq{eqn:hddisc} 160 we rearrange \refEq{eqn:hddisc}; 161 \begin{equation} 162 \frac{\rho c\hackscore p}{h} T^{(n)} - \kappa \frac{\partial^2 T^{(n)}}{\partial 163 x^2} = q\hackscore H + \frac{\rho c\hackscore p}{h} T^{(n-1)} 164 \label{eqn:hdgenf} 165 \end{equation} 166 The PDE is now in a form that satisfies \refEq{eqn:commonform nabla} which is 167 required for \esc to solve our PDE. This can be done by generating a solution 168 for successive increments in the time nodes $t^{(n)}$ where 169 $t^{(0)}=0$ and $t^{(n)}=t^{(n-1)}+h$ where $h>0$ is the step size and assumed 170 to be constant. 171 In the following the upper index ${(n)}$ refers to a value at time $t^{(n)}$. 172 Finally, by comparing \refEq{eqn:hdgenf} with \refEq{eqn:commonform} one can see 173 that; 174 \begin{equation}\label{ESCRIPT SET} 175 u=T^{(n)}; 176 A = \kappa; D = \frac{\rho c \hackscore{p}}{h}; f = q \hackscore{H} + \frac{\rho 177 c\hackscore p}{h} T^{(n-1)} 178 \end{equation} 179 180 \subsection{Boundary Conditions} 181 \label{SEC BOUNDARY COND} 182 With the PDE sufficiently modified, consideration must now be given to the 183 boundary conditions of our model. Typically there are two main types of boundary 184 conditions known as \textbf{Neumann} and \textbf{Dirichlet} boundary 185 conditions\footnote{More information on Boundary Conditions is available at 186 Wikipedia \url{http://en.wikipedia.org/wiki/Boundary_conditions}}, 187 respectively. 188 A \textbf{Dirichlet boundary condition} is conceptually simpler and is used to 189 prescribe a known value to the unknown solution (in our example the temperature) 190 on parts of the boundary or on the entire boundary of the region of interest. 191 We discuss Dirichlet boundary condition in our second example presented in 192 Section~\ref{Sec:1DHDv0}. 193 194 However, for this example we have made the model assumption that the system is 195 insulated, so we need 196 to add an appropriate boundary condition to prevent 197 any loss or inflow of energy at the boundary of our domain. Mathematically this 198 is expressed by prescribing 199 the heat flux $\kappa \frac{\partial T}{\partial x}$ to zero. In our simplified 200 one dimensional model this is expressed 201 in the form; 202 \begin{equation} 203 \kappa \frac{\partial T}{\partial x} = 0 204 \end{equation} 205 or in a more general case as 206 \begin{equation}\label{NEUMAN 1} 207 \kappa \nabla T \cdot n = 0 208 \end{equation} 209 where $n$ is the outer normal field \index{outer normal field} at the surface 210 of the domain. 211 The $\cdot$ (dot) refers to the dot product of the vectors $\nabla T$ and $n$. 212 In fact, the term $\nabla T \cdot n$ is the normal derivative of 213 the temperature $T$. Other notations used here are\footnote{The \esc notation 214 for the normal 215 derivative is $T\hackscore{,i} n\hackscore i$.}; 216 \begin{equation} 217 \nabla T \cdot n = \frac{\partial T}{\partial n} \; . 218 \end{equation} 219 A condition of the type \refEq{NEUMAN 1} defines a \textbf{Neuman boundary 220 condition} for the PDE. 221 222 The PDE \refEq{eqn:hdgenf} 223 and the Neuman boundary condition~\ref{eqn:hdgenf} (potentially together with 224 the Dirichlet boundary conditions) define a \textbf{boundary value problem}. 225 It is the nature of a boundary value problem to allow making statements about 226 the solution in the 227 interior of the domain from information known on the boundary only. In most 228 cases 229 we use the term partial differential equation but in fact it is a boundary value 230 problem. 231 It is important to keep in mind that boundary conditions need to be complete and 232 consistent in the sense that 233 at any point on the boundary either a Dirichlet or a Neuman boundary condition 234 must be set. 235 236 Conveniently, \esc makes default assumption on the boundary conditions which the 237 user may modify where appropriate. 238 For a problem of the form in~\refEq{eqn:commonform nabla} the default 239 condition\footnote{In the form of the \esc users guide which is using the 240 Einstein convention is written as 241 $n\hackscore{j}A\hackscore{jl} u\hackscore{,l}=0$.} is; 242 \begin{equation}\label{NEUMAN 2} 243 -n\cdot A \cdot\nabla u = 0 244 \end{equation} 245 which is used everywhere on the boundary. Again $n$ denotes the outer normal 246 field. 247 Notice that the coefficient $A$ is the same as in the \esc 248 PDE~\ref{eqn:commonform nabla}. 249 With the settings for the coefficients we have already identified in 250 \refEq{ESCRIPT SET} this 251 condition translates into 252 \begin{equation}\label{NEUMAN 2b} 253 \kappa \frac{\partial T}{\partial x} = 0 254 \end{equation} 255 for the boundary of the domain. This is identical to the Neuman boundary 256 condition we want to set. \esc will take care of this condition for us. We 257 discuss the Dirichlet boundary condition later. 258 259 \subsection{Outline of the Implementation} 260 \label{sec:outline} 261 To solve the heat diffusion equation (\refEq{eqn:hd}) we write a simple \pyt 262 script. At this point we assume that you have some basic understanding of the 263 \pyt programming language. If not, there are some pointers and links available 264 in Section \ref{sec:escpybas}. The script (discussed in \refSec{sec:key}) has 265 four major steps. Firstly, we need to define the domain where we want to 266 calculate the temperature. For our problem this is the joint blocks of granite 267 which has a rectangular shape. Secondly, we need to define the PDE to solve in 268 each time step to get the updated temperature. Thirdly, we need to define the 269 coefficients of the PDE and finally we need to solve the PDE. The last two steps 270 need to be repeated until the final time marker has been reached. The work flow 271 described in \reffig{fig:wf}. 272 % \begin{enumerate} 273 % \item create domain 274 % \item create PDE 275 % \item while end time not reached: 276 % \begin{enumerate} 277 % \item set PDE coefficients 278 % \item solve PDE 279 % \item update time marker 280 % \end{enumerate} 281 % \item end of calculation 282 % \end{enumerate} 283 284 \begin{figure}[h!] 285 \centering 286 \includegraphics[width=1in]{figures/workflow.png} 287 \caption{Workflow for developing an \esc model and solution.} 288 \label{fig:wf} 289 \end{figure} 290 291 In the terminology of \pyt, the domain and PDE are represented by 292 \textbf{objects}. The nice feature of an object is that it is defined by its 293 usage and features 294 rather than its actual representation. So we will create a domain object to 295 describe the geometry of the two 296 granite blocks. Then we define PDEs and spatially distributed values such as the 297 temperature 298 on this domain. Similarly, to define a PDE object we use the fact that one needs 299 only to define the coefficients of the PDE and solve the PDE. The PDE object has 300 advanced features, but these are not required in simple cases. 301 302 303 \begin{figure}[t] 304 \centering 305 \includegraphics[width=6in]{figures/functionspace.pdf} 306 \caption{\esc domain construction overview} 307 \label{fig:fs} 308 \end{figure} 309 310 \subsection{The Domain Constructor in \esc} 311 \label{ss:domcon} 312 Whilst it is not strictly relevant or necessary, a better understanding of 313 how values are spatially distributed (\textit{e.g.} Temperature) and how PDE 314 coefficients are interpreted in \esc can be helpful. 315 316 There are various ways to construct domain objects. The simplest form is a 317 rectangular shaped region with a length and height. There is 318 a ready to use function for this named \verb rectangle(). Besides the spatial 319 dimensions this function requires to specify the number of 320 elements or cells to be used along the length and height, see \reffig{fig:fs}. 321 Any spatially distributed value 322 and the PDE is represented in discrete form using this element 323 representation\footnote{We use the finite element method (FEM), see 324 \url{http://en.wikipedia.org/wiki/Finite_element_method} for details.}. 325 Therefore we will have access to an approximation of the true PDE solution 326 only. 327 The quality of the approximation depends - besides other factors- mainly on the 328 number of elements being used. In fact, the 329 approximation becomes better when more elements are used. However, computational 330 cost grows with the number of 331 elements being used. It is therefore important that you find the right balance 332 between the demand in accuracy and acceptable resource usage. 333 334 In general, one can think about a domain object as a composition of nodes and 335 elements. 336 As shown in \reffig{fig:fs}, an element is defined by the nodes that are used to 337 describe its vertices. 338 To represent spatial distributed values the user can use 339 the values at the nodes, at the elements in the interior of the domain or at the 340 elements located at the surface of the domain. 341 The different approach used to represent values is called \textbf{function 342 space} and is attached to all objects 343 in \esc representing a spatial distributed value such as the solution of a PDE. 344 The three 345 function spaces we use at the moment are; 346 \begin{enumerate} 347 \item the nodes, called by \verb|ContinuousFunction(domain)| ; 348 \item the elements/cells, called by \verb|Function(domain)| ; and 349 \item the boundary, called by \verb|FunctionOnBoundary(domain)| . 350 \end{enumerate} 351 A function space object such as \verb|ContinuousFunction(domain)| has the method 352 \verb|getX| attached to it. This method returns the 353 location of the so-called \textbf{sample points} used to represent values of the 354 particular function space. So the 355 call \verb|ContinuousFunction(domain).getX()| will return the coordinates of the 356 nodes used to describe the domain while 357 the \verb|Function(domain).getX()| returns the coordinates of numerical 358 integration points within elements, see 359 \reffig{fig:fs}. 360 361 This distinction between different representations of spatially distributed 362 values 363 is important in order to be able to vary the degrees of smoothness in a PDE 364 problem. 365 The coefficients of a PDE do not need to be continuous, thus this qualifies as a 366 \verb|Function()| type. 367 On the other hand a temperature distribution must be continuous and needs to be 368 represented with a \verb|ContinuousFunction()| function space. 369 An influx may only be defined at the boundary and is therefore a \verb 370 FunctionOnBoundary() object. 371 \esc allows certain transformations of the function spaces. A \verb 372 ContinuousFunction() can be transformed into a \verb|FunctionOnBoundary()| 373 or \verb|Function()|. On the other hand there is not enough information in a 374 \verb FunctionOnBoundary() to transform it to a \verb ContinuousFunction() . 375 These transformations, which are called \textbf{interpolation} are invoked 376 automatically by \esc if needed. 377 378 Later in this introduction we discuss how 379 to define specific areas of geometry with different materials which are 380 represented by different material coefficients such the 381 thermal conductivities $\kappa$. A very powerful technique to define these types 382 of PDE 383 coefficients is tagging. Blocks of materials and boundaries can be named and 384 values can be defined on subregions based on their names. 385 This is a method for simplifying PDE coefficient and flux definitions. It makes 386 scripting much easier and we will discuss this technique in 387 Section~\ref{STEADY-STATE HEAT REFRACTION}. 388 389 390 \subsection{A Clarification for the 1D Case} 391 \label{SEC: 1D CLARIFICATION} 392 It is necessary for clarification that we revisit our general PDE from 393 \refeq{eqn:commonform nabla} for two dimensional domain. \esc is inherently 394 designed to solve problems that are greater than one dimension and so 395 \refEq{eqn:commonform nabla} needs to be read as a higher dimensional problem. 396 In the case of two spatial dimensions the \textit{Nabla operator} has in fact 397 two components $\nabla = (\frac{\partial}{\partial x}, \frac{\partial}{\partial 398 y})$. Assuming the coefficient $A$ is constant, the \refEq{eqn:commonform nabla} 399 takes the following form; 400 \begin{equation}\label{eqn:commonform2D} 401 -A\hackscore{00}\frac{\partial^{2}u}{\partial x^{2}} 402 -A\hackscore{01}\frac{\partial^{2}u}{\partial x\partial y} 403 -A\hackscore{10}\frac{\partial^{2}u}{\partial y\partial x} 404 -A\hackscore{11}\frac{\partial^{2}u}{\partial y^{2}} 405 + Du = f 406 \end{equation} 407 Notice that for the higher dimensional case $A$ becomes a matrix. It is also 408 important to notice that the usage of the Nabla operator creates 409 a compact formulation which is also independent from the spatial dimension. 410 To make the general PDE \refEq{eqn:commonform2D} one dimensional as 411 shown in \refEq{eqn:commonform} we need to set 412 \begin{equation} 413 A\hackscore{00}=A; A\hackscore{01}=A\hackscore{10}=A\hackscore{11}=0 414 \end{equation} 415 416 417 \subsection{Developing a PDE Solution Script} 418 \label{sec:key} 419 \sslist{example01a.py} 420 We write a simple \pyt script which uses the \modescript, \modfinley and \modmpl 421 modules. 422 By developing a script for \esc, the heat diffusion equation can be solved at 423 successive time steps for a predefined period using our general form 424 \refEq{eqn:hdgenf}. Firstly it is necessary to import all the 425 libraries\footnote{The libraries contain predefined scripts that are required to 426 solve certain problems, these can be simple like sine and cosine functions or 427 more complicated like those from our \esc library.} 428 that we will require. 429 \begin{python} 430 from esys.escript import * 431 # This defines the LinearPDE module as LinearPDE 432 from esys.escript.linearPDEs import LinearPDE 433 # This imports the rectangle domain function from finley. 434 from esys.finley import Rectangle 435 # A useful unit handling package which will make sure all our units 436 # match up in the equations under SI. 437 from esys.escript.unitsSI import * 438 \end{python} 439 It is generally a good idea to import all of the \modescript library, although 440 if the functions and classes required are known they can be specified 441 individually. The function \verb|LinearPDE| has been imported explicitly for 442 ease of use later in the script. \verb|Rectangle| is going to be our type of 443 domain. The module \verb unitsSI provides support for SI unit definitions with 444 our variables. 445 446 Once our library dependencies have been established, defining the problem 447 specific variables is the next step. In general the number of variables needed 448 will vary between problems. These variables belong to two categories. They are 449 either directly related to the PDE and can be used as inputs into the \esc 450 solver, or they are script variables used to control internal functions and 451 iterations in our problem. For this PDE there are a number of constants which 452 need values. Firstly, the domain upon which we wish to solve our problem needs 453 to be defined. There are different types of domains in \modescript which we 454 demonstrate in later tutorials but for our granite blocks, we simply use a 455 rectangular domain. 456 457 Using a rectangular domain simplifies our granite blocks (which would in reality 458 be a \textit{3D} object) into a single dimension. The granite blocks will have a 459 lengthways cross section that looks like a rectangle. As a result we do not 460 need to model the volume of the block due to symmetry. There are four arguments 461 we must consider when we decide to create a rectangular domain, the domain 462 \textit{length}, \textit{width} and \textit{step size} in each direction. When 463 defining the size of our problem it will help us determine appropriate values 464 for our model arguments. If we make our dimensions large but our step sizes very 465 small we increase the accuracy of our solution. Unfortunately we also increase 466 the number of calculations that must be solved per time step. This means more 467 computational time is required to produce a solution. In this \textit{1D} 468 problem, the bar is defined as being 1 metre long. An appropriate step size 469 \verb|ndx| would be 1 to 10\% of the length. Our \verb|ndy| need only be 1, this 470 is because our problem stipulates no partial derivatives in the $y$ direction. 471 Thus the temperature does not vary with $y$. Hence, the model parameters can be 472 defined as follows; note we have used the \verb unitsSI convention to make sure 473 all our input units are converted to SI. 474 \begin{python} 475 mx = 500.*m #meters - model length 476 my = 100.*m #meters - model width 477 ndx = 50 # mesh steps in x direction 478 ndy = 1 # mesh steps in y direction 479 boundloc = mx/2 # location of boundary between the two blocks 480 \end{python} 481 The material constants and the temperature variables must also be defined. For 482 the granite in the model they are defined as: 483 \begin{python} 484 #PDE related 485 rho = 2750. *kg/m**3 #kg/m^{3} density of iron 486 cp = 790.*J/(kg*K) # J/Kg.K thermal capacity 487 rhocp = rho*cp 488 kappa = 2.2*W/m/K # watts/m.Kthermal conductivity 489 qH=0 * J/(sec*m**3) # J/(sec.m^{3}) no heat source 490 T1=20 * Celsius # initial temperature at Block 1 491 T2=2273. * Celsius # base temperature at Block 2 492 \end{python} 493 Finally, to control our script we will have to specify our timing controls and 494 where we would like to save the output from the solver. This is simple enough: 495 \begin{python} 496 t=0 * day #our start time, usually zero 497 tend=1. * day # - time to end simulation 498 outputs = 200 # number of time steps required. 499 h=(tend-t)/outputs #size of time step 500 #user warning statement 501 print "Expected Number of time outputs is: ", (tend-t)/h 502 i=0 #loop counter 503 \end{python} 504 Now that we know our inputs we will build a domain using the 505 \verb Rectangle() function from \verb finley . The four arguments allow us to define our domain 506 \verb model as: 507 \begin{python} 508 #generate domain using rectangle 509 blocks = Rectangle(l0=mx,l1=my,n0=ndx, n1=ndy) 510 \end{python} 511 \verb blocks now describes a domain in the manner of Section \ref{ss:domcon}. 512 513 With a domain and all our required variables established, it is now possible to 514 set up our PDE so that it can be solved by \esc. The first step is to define the 515 type of PDE that we are trying to solve in each time step. In this example it is 516 a single linear PDE\footnote{in comparison to a system of PDEs which we discuss 517 later.}. We also need to state the values of our general form variables. 518 \begin{python} 519 mypde=LinearPDE(blocks) 520 A=zeros((2,2))) 521 A[0,0]=kappa 522 mypde.setValue(A=A, D=rhocp/h) 523 \end{python} 524 In a many cases it may be possible to decrease the computational time of the 525 solver if the PDE is symmetric. 526 Symmetry of a PDE is defined by; 527 \begin{equation}\label{eqn:symm} 528 A\hackscore{jl}=A\hackscore{lj} 529 \end{equation} 530 Symmetry is only dependent on the $A$ coefficient in the general form and the 531 other coefficients $D$ as well as the right hand side $Y$. From the above 532 definition we can see that our PDE is symmetric. The \verb LinearPDE class 533 provides the method \method{checkSymmetry} to check if the given PDE is 534 symmetric. As our PDE is symmetrical we enable symmetry via; 535 \begin{python} 536 myPDE.setSymmetryOn() 537 \end{python} 538 Next we need to establish the initial temperature distribution \verb|T|. We need 539 to 540 assign the value \verb|T1| to all sample points left to the contact interface at 541 $x\hackscore{0}=\frac{mx}{2}$ 542 and the value \verb|T2| right to the contact interface. \esc 543 provides the \verb|whereNegative| function to construct this. In fact, 544 \verb|whereNegative| returns the value $1$ at those sample points where the 545 argument 546 has a negative value. Otherwise zero is returned. If \verb|x| are the 547 $x\hackscore{0}$ 548 coordinates of the sample points used to represent the temperature distribution 549 then \verb|x-boundloc| gives us a negative value for 550 all sample points left to the interface and non-negative value to 551 the right of the interface. So with; 552 \begin{python} 553 # ... set initial temperature .... 554 T= T1*whereNegative(x-boundloc)+T2*(1-whereNegative(x-boundloc)) 555 \end{python} 556 we get the desired temperature distribution. To get the actual sample points 557 \verb|x| we use 558 the \verb|getX()| method of the function space \verb|Solution(blocks)| 559 which is used to represent the solution of a PDE; 560 \begin{python} 561 x=Solution(blocks).getX() 562 \end{python} 563 As \verb|x| are the sample points for the function space 564 \verb|Solution(blocks)| 565 the initial temperature \verb|T| is using these sample points for 566 representation. 567 Although \esc is trying to be forgiving with the choice of sample points and to 568 convert 569 where necessary the adjustment of the function space is not always possible. So 570 it is 571 advisable to make a careful choice on the function space used. 572 573 Finally we initialise an iteration loop to solve our PDE for all the time steps 574 we specified in the variable section. As the right hand side of the general form 575 is dependent on the previous values for temperature \verb T across the bar this 576 must be updated in the loop. Our output at each time step is \verb T the heat 577 distribution and \verb totT the total heat in the system. 578 \begin{python} 579 while t < tend: 580 i+=1 #increment the counter 581 t+=h #increment the current time 582 mypde.setValue(Y=qH+rhocp/h*T) # set variable PDE coefficients 583 T=mypde.getSolution() #get the PDE solution 584 totE = integrate(rhocp*T) #get the total heat (energy) in the system 585 \end{python} 586 The last statement in this script calculates the total energy in the system as 587 volume integral 588 of $\rho c\hackscore{p} T$ over the block. As the blocks are insulated no energy 589 should be get lost or added. 590 The total energy should stay constant for the example discussed here. 591 592 \subsection{Running the Script} 593 The script presented so far is available under 594 \verb|example01a.py|. You can edit this file with your favourite text editor. 595 On most operating systems\footnote{The you can use \texttt{run-escript} launcher 596 is not supported under {\it MS Windows} yet.} you can use the 597 \program{run-escript} command 598 to launch {\it escript} scripts. For the example script use; 599 \begin{verbatim} 600 run-escript example01a.py 601 \end{verbatim} 602 The program will print a progress report. Alternatively, you can use 603 the python interpreter directly; 604 \begin{verbatim} 605 python example01a.py 606 \end{verbatim} 607 if the system is configured correctly (Please talk to your system 608 administrator). 609 610 \begin{figure} 611 \begin{center} 612 \includegraphics[width=4in]{figures/ttblockspyplot150} 613 \caption{Example 1b: Total Energy in the Blocks over Time (in seconds).} 614 \label{fig:onedheatout1} 615 \end{center} 616 \end{figure} 617 618 \subsection{Plotting the Total Energy} 619 \sslist{example01b.py} 620 621 \esc does not include its own plotting capabilities. However, it is possible to 622 use a variety of free \pyt packages for visualisation. 623 Two types will be demonstrated in this cookbook; 624 \mpl\footnote{\url{http://matplotlib.sourceforge.net/}} and 625 \verb VTK \footnote{\url{http://www.vtk.org/}} visualisation. 626 The \mpl package is a component of SciPy\footnote{\url{http://www.scipy.org}} 627 and is good for basic graphs and plots. 628 For more complex visualisation tasks in particular, two and three dimensional 629 problems we recommend the use of more advanced tools. For instance, \mayavi 630 \footnote{\url{http://code.enthought.com/projects/mayavi/}} 631 which is based upon the \verb|VTK| toolkit. The usage of \verb|VTK| based 632 visualization is discussed in Chapter~\ref{Sec:2DHD} which focusses on a two 633 dimensional PDE. 634 635 For our simple granite block problem, we have two plotting tasks. Firstly, we 636 are interested in showing the 637 behaviour of the total energy over time and secondly, how the temperature 638 distribution within the block is 639 developing over time. Lets start with the first task. 640 641 The trick is to create a record of the time marks and the corresponding total 642 energies observed. 643 \pyt provides the concept of lists for this. Before 644 the time loop is opened we create empty lists for the time marks \verb|t_list| 645 and the total energies \verb|E_list|. 646 After the new temperature has been calculated by solving the PDE we append the 647 new time marker and the total energy value for that time 648 to the corresponding list using the \verb|append| method. With these 649 modifications our script looks as follows: 650 \begin{python} 651 t_list=[] 652 E_list=[] 653 # ... start iteration: 654 while t