From: wolf Date: Mon, 27 Mar 2006 23:34:11 +0000 (+0000) Subject: Generate intro from doxygen. X-Git-Url: https://gitweb.dealii.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=66aa2539cc87ef157087b8924d32d536f5f5f9a5;p=dealii-svn.git Generate intro from doxygen. git-svn-id: https://svn.dealii.org/trunk@12688 0785d39b-7218-0410-832d-ea1e28bc413d --- diff --git a/deal.II/doc/tutorial/chapter-2.step-by-step/step-9.data/intro.tex b/deal.II/doc/tutorial/chapter-2.step-by-step/step-9.data/intro.dox similarity index 73% rename from deal.II/doc/tutorial/chapter-2.step-by-step/step-9.data/intro.tex rename to deal.II/doc/tutorial/chapter-2.step-by-step/step-9.data/intro.dox index b5de177771..93caff4b43 100644 --- a/deal.II/doc/tutorial/chapter-2.step-by-step/step-9.data/intro.tex +++ b/deal.II/doc/tutorial/chapter-2.step-by-step/step-9.data/intro.dox @@ -1,25 +1,14 @@ -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -%% intro.html was generated from this file -%% with latex2html and some hand work -%% (copying out the relevant parts from the -%% generated html file, replacing IMG= -%% by the proper path) -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -\documentclass{article} -\usepackage{amsmath} -\usepackage{amsfonts} + +

Introduction

-\renewcommand{\vec}[1]{{\mathbf #1}} -\renewcommand{\div}{\nabla \cdot} -\begin{document} In this example, our aims are the following: -\begin{itemize} - \item solve the advection equation $\beta \cdot \nabla u = f$; - \item show how we can use multiple threads to get quicker to +
    +
  1. solve the advection equation $\beta \cdot \nabla u = f$; +
  2. show how we can use multiple threads to get quicker to the desired results if we have a multi-processor machine; - \item develop a simple refinement criterion. -\end{itemize} +
  3. develop a simple refinement criterion. +
While the second aim is difficult to describe in general terms without reference to the code, we will discuss the other two aims in the following. The use of multiple threads will then be detailed at the @@ -27,12 +16,14 @@ relevant places within the program. Furthermore, there exists a report on this subject, which is also available online from the ``Documentation'' section of the deal.II homepage. -\paragraph{Discretizing the advection equation.} + +

Discretizing the advection equation

+ In the present example program, we shall numerically approximate the solution of the advection equation -$$ +@f[ \beta \cdot \nabla u = f, -$$ +@f] where $\beta$ is a vector field that describes advection direction and speed (which may be dependent on the space variables), $f$ is a source function, and $u$ is the solution. The physical process that this @@ -44,19 +35,19 @@ terms. It is obvious that at the inflow, the above equation needs to be augmented by boundary conditions: -$$ - u = g \qquad\qquad \text{on $\partial\Omega_-$}, -$$ +@f[ + u = g \qquad\qquad \mathrm{on}\ \partial\Omega_-, +@f] where $\partial\Omega_-$ describes the inflow portion of the boundary and is formally defined by -$$ +@f[ \partial\Omega_- = - \{\vec x\in \partial\Omega: \beta\cdot\vec n(\vec x) < 0\}, -$$ -and $\vec n(\vec x)$ being the outward normal to the domain at point -$\vec x\in\partial\Omega$. This definition is quite intuitive, since -as $\vec n$ points outward, the scalar product with $\beta$ can only + \{{\mathbf x}\in \partial\Omega: \beta\cdot{\mathbf n}({\mathbf x}) < 0\}, +@f] +and ${\mathbf n}({\mathbf x})$ being the outward normal to the domain at point +${\mathbf x}\in\partial\Omega$. This definition is quite intuitive, since +as ${\mathbf n}$ points outward, the scalar product with $\beta$ can only be negative if the transport direction $\beta$ points inward, i.e. at the inflow boundary. The mathematical theory states that we must not pose any boundary condition on the outflow part of the boundary. @@ -83,56 +74,56 @@ topic. Using the test functions as defined above, the weak formulation of our stabilized problem reads: find a discrete function $u_h$ such that for all discrete test functions $v_h$ there holds -$$ +@f[ (\beta \cdot \nabla u_h, v_h + \delta \beta\cdot\nabla v_h)_\Omega - - (\beta\cdot \vec n u_h, v_h)_{\partial\Omega_-} + (\beta\cdot {\mathbf n} u_h, v_h)_{\partial\Omega_-} = (f, v_h + \delta \beta\cdot\nabla v_h)_\Omega - - (\beta\cdot \vec n g, v_h)_{\partial\Omega_-}. -$$ + (\beta\cdot {\mathbf n} g, v_h)_{\partial\Omega_-}. +@f] Note that we have included the inflow boundary values into the weak form, and that the respective terms to the left hand side operator are -positive definite due to the fact that $\beta\cdot\vec n<0$ on the +positive definite due to the fact that $\beta\cdot{\mathbf n}<0$ on the inflow boundary. One would think that this leads to a system matrix to be inverted of the form -$$ +@f[ a_{ij} = (\beta \cdot \nabla \varphi_i, \varphi_j + \delta \beta\cdot\nabla \varphi_j)_\Omega - - (\beta\cdot \vec n \varphi_i, \varphi_j)_{\partial\Omega_-}, -$$ + (\beta\cdot {\mathbf n} \varphi_i, \varphi_j)_{\partial\Omega_-}, +@f] with basis functions $\varphi_i,\varphi_j$. However, this is a pitfall that happens to every numerical analyst at least once (including the author): we have here expanded the solution $u_h = u_i \varphi_i$, but if we do so, we will have to solve the problem -$$ - \vec u^T A = \vec f^T, -$$ -where $\vec u=(u_i)$, i.e. we have to solve the transpose problem of +@f[ + {\mathbf u}^T A = {\mathbf f}^T, +@f] +where ${\mathbf u}=(u_i)$, i.e. we have to solve the transpose problem of what we might have expected naively. In order to obtain the usual form of the linear system, it is therefore best to rewrite the weak formulation to -$$ +@f[ (v_h + \delta \beta\cdot\nabla v_h, \beta \cdot \nabla u_h)_\Omega - - (\beta\cdot \vec n v_h, u_h)_{\partial\Omega_-} + (\beta\cdot {\mathbf n} v_h, u_h)_{\partial\Omega_-} = (v_h + \delta \beta\cdot\nabla v_h, f)_\Omega - - (\beta\cdot \vec n v_h, g)_{\partial\Omega_-} -$$ + (\beta\cdot {\mathbf n} v_h, g)_{\partial\Omega_-} +@f] and then to obtain -$$ +@f[ a_{ij} = (\varphi_i + \delta \beta \cdot \nabla \varphi_i, \beta\cdot\nabla \varphi_j)_\Omega - - (\beta\cdot \vec n \varphi_i, \varphi_j)_{\partial\Omega_-}, -$$ + (\beta\cdot {\mathbf n} \varphi_i, \varphi_j)_{\partial\Omega_-}, +@f] as system matrix. We will assemble this matrix in the program. There remains the solution of this linear system of equations. As the @@ -145,25 +136,25 @@ it. Regarding the exact form of the problem which we will solve, we use the following domain and functions (in $d=2$ space dimensions): -\begin{eqnarray*} +@f{eqnarray*} \Omega &=& [-1,1]^d \\ - \beta(\vec x) + \beta({\mathbf x}) &=& \left( \begin{array}{c}1 \\ 1+\frac 45 \sin(8\pi x)\end{array} \right), \\ - f(\vec x) + f({\mathbf x}) &=& \left\{ \begin{array}{ll} \frac 1{10 s^d} & - \text{for $|\vec x-\vec x_0|2$, we extend $\beta$ and $\vec x_0$ by the same as the last + e^{5(1-|{\mathbf x}|^2)} \sin(16\pi|{\mathbf x}|^2). +@f} +For $d>2$, we extend $\beta$ and ${\mathbf x}_0$ by the same as the last component. Regarding these functions, we have the following annotations: -\begin{itemize} -\item The advection field $\beta$ transports the solution roughly in +
    +
  1. The advection field $\beta$ transports the solution roughly in diagonal direction from lower left to upper right, but with a wiggle structure superimposed. -\item The right hand side adds to the field generated by the inflow +
  2. The right hand side adds to the field generated by the inflow boundary conditions a bulb in the lower left corner, which is then transported along. -\item The inflow boundary conditions impose a weighted sinusoidal -structure that is transported along with the flow field. Since $|\vec -x|\ge 1$ on the boundary, the weighting term never gets very large. -\end{itemize} +
  3. The inflow boundary conditions impose a weighted sinusoidal +structure that is transported along with the flow field. Since +$|{\mathbf x}|\ge 1$ on the boundary, the weighting term never gets very large. +
-\paragraph{A simple refinement criterion.} +

A simple refinement criterion

+ In all previous examples with adaptive refinement, we have used an error estimator first developed by Kelly et al., which assigns to each cell $K$ the following indicator: -$$ +@f[ \eta_K = \left( \frac {h_K}{12} \int_{\partial K} [\partial_n u_h]^2 \; d\sigma \right)^{1/2}, -$$ +@f] where $[\partial n u_h]$ denotes the jump of the normal derivatives across a face $\gamma\subset\partial K$ of the cell $K$. It can be shown that this error indicator uses a discrete analogue of the second derivatives, weighted by a power of the cell size that is adjusted to the linear elements assumed to be in use here: -$$ +@f[ \eta_K \approx C h \| \nabla^2 u \|_K, -$$ +@f] which itself is related to the error size in the energy norm. The problem with this error indicator in the present case is that it @@ -230,49 +222,49 @@ a discrete approximation of the gradient. Although the gradient often does not exist, this is the only criterion available to us, at least as long as we use continuous elements as in the present example. To start with, we note that given two cells $K$, $K'$ of -which the centers are connected by the vector $\vec y_{KK'}$, we can +which the centers are connected by the vector ${\mathbf y}_{KK'}$, we can approximate the directional derivative of a function $u$ as follows: -$$ - \frac{\vec y_{KK'}^T}{|\vec y_{KK'}|} \nabla u +@f[ + \frac{{\mathbf y}_{KK'}^T}{|{\mathbf y}_{KK'}|} \nabla u \approx - \frac{u(K') - u(K)}{|\vec y_{KK'}|}, -$$ + \frac{u(K') - u(K)}{|{\mathbf y}_{KK'}|}, +@f] where $u(K)$ and $u(K')$ denote $u$ evaluated at the centers of the respective cells. We now multiply the above approximation by -$\vec y_{KK'}/|\vec y_{KK'}|$ and sum over all neighbors $K'$ of $K$: -$$ +${\mathbf y}_{KK'}/|{\mathbf y}_{KK'}|$ and sum over all neighbors $K'$ of $K$: +@f[ \underbrace{ - \left(\sum_{K'} \frac{\vec y_{KK'} \vec y_{KK'}^T} - {|\vec y_{KK'}|^2}\right)}_{=:Y} + \left(\sum_{K'} \frac{{\mathbf y}_{KK'} {\mathbf y}_{KK'}^T} + {|{\mathbf y}_{KK'}|^2}\right)}_{=:Y} \nabla u \approx \sum_{K'} - \frac{\vec y_{KK'}}{|\vec y_{KK'}|} - \frac{u(K') - u(K)}{|\vec y_{KK'}|}. -$$ -If the vectors $\vec y_{KK'}$ connecting $K$ with its neighbors span + \frac{{\mathbf y}_{KK'}}{|{\mathbf y}_{KK'}|} + \frac{u(K') - u(K)}{|{\mathbf y}_{KK'}|}. +@f] +If the vectors ${\mathbf y}_{KK'}$ connecting $K$ with its neighbors span the whole space (i.e. roughly: $K$ has neighbors in all directions), then the term in parentheses in the left hand side expression forms a regular matrix, which we can invert to obtain an approximation of the gradient of $u$ on $K$: -$$ +@f[ \nabla u \approx Y^{-1} \left( \sum_{K'} - \frac{\vec y_{KK'}}{|\vec y_{KK'}|} - \frac{u(K') - u(K)}{|\vec y_{KK'}|} + \frac{{\mathbf y}_{KK'}}{|{\mathbf y}_{KK'}|} + \frac{u(K') - u(K)}{|{\mathbf y}_{KK'}|} \right). -$$ +@f] We will denote the approximation on the right hand side by $\nabla_h u(K)$, and we will use the following quantity as refinement criterion: -$$ +@f[ \eta_K = h^{1+d/2} |\nabla_h u_h(K)|, -$$ +@f] which is inspired by the following (not rigorous) argument: -\begin{eqnarray*} +@f{eqnarray*} \|u-u_h\|^2_{L_2} &\le& C h^2 \|\nabla u\|^2_{L_2} @@ -291,6 +283,4 @@ which is inspired by the following (not rigorous) argument: C \sum_K h_K^{2+d} |\nabla_h u_h(K)|^2 -\end{eqnarray*} - -\end{document} +@f} diff --git a/deal.II/doc/tutorial/chapter-2.step-by-step/step-9.data/intro.html b/deal.II/doc/tutorial/chapter-2.step-by-step/step-9.data/intro.html deleted file mode 100644 index be47bc32da..0000000000 --- a/deal.II/doc/tutorial/chapter-2.step-by-step/step-9.data/intro.html +++ /dev/null @@ -1,624 +0,0 @@ - -

Introduction

- -

-In this example, our aims are the following: -

While the second aim is difficult to describe in general terms without -reference to the code, we will discuss the other two aims in the -following. The use of multiple threads will then be detailed at the -relevant places within the program. Furthermore, there exists a report on this -subject, which is also available online from the ``Documentation'' section of -the deal.II homepage. - -

- -

-Discretizing the advection equation. -

-In the present example program, we shall numerically approximate the -solution of the advection equation -

-
- - - -\begin{displaymath}\beta \cdot \nabla u = f,
-\end{displaymath} -
-
-

-where $\beta$ -is a vector field that describes advection direction and -speed (which may be dependent on the space variables), f is a source -function, and u is the solution. The physical process that this -equation describes is that of a given flow field $\beta$, -with which -another substance is transported, the density or concentration of -which is given by u. The equation does not contain diffusion of this -second species within its carrier substance, but there are source -terms. - -

-It is obvious that at the inflow, the above equation needs to be -augmented by boundary conditions: -

-
- - - -\begin{displaymath}u = g \qquad\qquad \text{on $\partial\Omega_-$},
-\end{displaymath} -
-
-

-where - -$\partial\Omega_-$ -describes the inflow portion of the boundary and is -formally defined by -

-
- - - -\begin{displaymath}\partial\Omega_-
-=
-\{\vec x\in \partial\Omega: \beta\cdot\vec n(\vec x) < 0\},
-\end{displaymath} -
-
-

-and - -$\vec n(\vec x)$ -being the outward normal to the domain at point - - -$\vec x\in\partial\Omega$. -This definition is quite intuitive, since -as $\vec n$ -points outward, the scalar product with $\beta$ -can only -be negative if the transport direction $\beta$ -points inward, i.e. at -the inflow boundary. The mathematical theory states that we must not -pose any boundary condition on the outflow part of the boundary. - -

-As it is stated, the transport equation is not stably solvable using -the standard finite element method, however. The problem is that -solutions to this equation possess only insufficient regularity -orthogonal to the transport direction: while they are smooth parallel -to $\beta$, -they may be discontinuous perpendicular to this -direction. These discontinuities lead to numerical instabilities that -make a stable solution by a straight-forward discretization -impossible. We will thus use the streamline diffusion stabilized -formulation, in which we test the equation with test functions - -$v +
-\delta \beta\cdot\nabla v$ -instead of v, where $\delta$ -is a -parameter that is chosen in the range of the (local) mesh width h; -good results are usually obtained by setting - -$\delta=0.1h$. -Note that -the modification in the test function vanishes as the mesh size tends -to zero. We will not discuss reasons, pros, and cons of the streamline -diffusion method, but rather use it ``as is'', and refer the -interested reader to the sufficiently available literature; every -recent good book on finite elements should have a discussion of that -topic. - -

-Using the test functions as defined above, the weak formulation of -our stabilized problem reads: find a discrete function uh such that -for all discrete test functions vh there holds -

-
- - - -\begin{displaymath}(\beta \cdot \nabla u_h, v_h + \delta \beta\cdot\nabla v_h)_\...
-...v_h)_\Omega
--
-(\beta\cdot \vec n g, v_h)_{\partial\Omega_-}.
-\end{displaymath} -
-
-

-Note that we have included the inflow boundary values into the weak -form, and that the respective terms to the left hand side operator are -positive definite due to the fact that - -$\beta\cdot\vec n<0$ -on the -inflow boundary. One would think that this leads to a system matrix -to be inverted of the form -

-
- - - -\begin{displaymath}a_{ij} =
-(\beta \cdot \nabla \varphi_i,
-\varphi_j + \delta ...
-...
-(\beta\cdot \vec n \varphi_i, \varphi_j)_{\partial\Omega_-},
-\end{displaymath} -
-
-

-with basis functions - -$\varphi_i,\varphi_j$. -However, this is a -pitfall that happens to every numerical analyst at least once -(including the author): we have here expanded the solution - - -$u_h = u_i \varphi_i$, -but if we do so, we will have to solve the -problem -

-
- - - -\begin{displaymath}\vec u^T A = \vec f^T,
-\end{displaymath} -
-
-

-where - -$\vec u=(u_i)$, -i.e. we have to solve the transpose problem of -what we might have expected naively. In order to obtain the usual form -of the linear system, it is therefore best to rewrite the weak -formulation to -

-
- - - -\begin{displaymath}(v_h + \delta \beta\cdot\nabla v_h, \beta \cdot \nabla u_h)_\...
-...h, f)_\Omega
--
-(\beta\cdot \vec n v_h, g)_{\partial\Omega_-}
-\end{displaymath} -
-
-

-and then to obtain -

-
- - - -\begin{displaymath}a_{ij} =
-(\varphi_i + \delta \beta \cdot \nabla \varphi_i,
-...
-...
-(\beta\cdot \vec n \varphi_i, \varphi_j)_{\partial\Omega_-},
-\end{displaymath} -
-
-

-as system matrix. We will assemble this matrix in the program. - -

-There remains the solution of this linear system of equations. As the -resulting matrix is no more symmetric positive definite, we can't -employ the usual CG method any more. Suitable for the solution of -systems as the one at hand is the BiCGStab (bi-conjugate gradients -stabilized) method, which is also available in deal.II, so we will use -it. - -

-Regarding the exact form of the problem which we will solve, we use -the following domain and functions (in d=2 space dimensions): -

-
-\begin{eqnarray*}\Omega &=& [-1,1]^d \\
-\beta(\vec x)
-&=&
-\left(
-\begin{ar...
-... &=&
-e^{5(1-\vert\vec x\vert^2)} \sin(16\pi\vert\vec x\vert^2).
-\end{eqnarray*} -

-
For d>2, we extend $\beta$ -and $\vec x_0$ -by the same as the last -component. Regarding these functions, we have the following -annotations: - -

- -

-A simple refinement criterion. -

-In all previous examples with adaptive refinement, we have used an -error estimator first developed by Kelly et al., which assigns to each -cell K the following indicator: -

-
- - - -\begin{displaymath}\eta_K =
-\left(
-\frac {h_K}{12}
-\int_{\partial K}
-[\partial_n u_h]^2 \; d\sigma
-\right)^{1/2},
-\end{displaymath} -
-
-

-where - -$[\partial n u_h]$ -denotes the jump of the normal derivatives -across a face - -$\gamma\subset\partial K$ -of the cell K. It can be -shown that this error indicator uses a discrete analogue of the second -derivatives, weighted by a power of the cell size that is adjusted to -the linear elements assumed to be in use here: -

-
- - - -\begin{displaymath}\eta_K \approx
-C h \Vert \nabla^2 u \Vert _K,
-\end{displaymath} -
-
-

-which itself is related to the error size in the energy norm. - -

-The problem with this error indicator in the present case is that it -assumes that the exact solution possesses second derivatives. This is -already questionable for solutions to Laplace's problem in some cases, -although there most problems allow solutions in H2. If solutions -are only in H1, then the second derivatives would be singular in -some parts (of lower dimension) of the domain and the error indicators -would not reduce there under mesh refinement. Thus, the algorithm -would continuously refine the cells around these parts, i.e. would -refine into points or lines (in 2d). - -

-However, for the present case, solutions are usually not even in H1(and this missing regularity is not the exceptional case as for -Laplace's equation), so the error indicator described above is not -really applicable. We will thus develop an indicator that is based on -a discrete approximation of the gradient. Although the gradient often -does not exist, this is the only criterion available to us, at least -as long as we use continuous elements as in the present -example. To start with, we note that given two cells K, K' of -which the centers are connected by the vector - -$\vec y_{KK'}$, -we can -approximate the directional derivative of a function u as follows: -

-
- - - -\begin{displaymath}\frac{\vec y_{KK'}^T}{\vert\vec y_{KK'}\vert} \nabla u
-\approx
-\frac{u(K') - u(K)}{\vert\vec y_{KK'}\vert},
-\end{displaymath} -
-
-

-where u(K) and u(K') denote u evaluated at the centers of the -respective cells. We now multiply the above approximation by - - -$\vec y_{KK'}/\vert\vec y_{KK'}\vert$ -and sum over all neighbors K' of K: -

-
- - - -\begin{displaymath}\underbrace{
-\left(\sum_{K'} \frac{\vec y_{KK'} \vec y_{KK'}...
-...ec y_{KK'}\vert}
-\frac{u(K') - u(K)}{\vert\vec y_{KK'}\vert}.
-\end{displaymath} -
-
-

-If the vectors - -$\vec y_{KK'}$ -connecting K with its neighbors span -the whole space (i.e. roughly: K has neighbors in all directions), -then the term in parentheses in the left hand side expression forms a -regular matrix, which we can invert to obtain an approximation of the -gradient of u on K: -

-
- - - -\begin{displaymath}\nabla u
-\approx
-Y^{-1}
-\left(
-\sum_{K'}
-\frac{\vec y_{K...
-...}\vert}
-\frac{u(K') - u(K)}{\vert\vec y_{KK'}\vert}
-\right).
-\end{displaymath} -
-
-

-We will denote the approximation on the right hand side by - - -$\nabla_h u(K)$, -and we will use the following quantity as refinement -criterion: -

-
- - - -\begin{displaymath}\eta_K = h^{1+d/2} \vert\nabla_h u_h(K)\vert,
-\end{displaymath} -
-
-

-which is inspired by the following (not rigorous) argument: -

-
-\begin{eqnarray*}\Vert u-u_h\Vert^2_{L_2}
-&\le&
-C h^2 \Vert\nabla u\Vert^2_{L...
-...\\
-&\approx&
-C
-\sum_K
-h_K^{2+d} \vert\nabla_h u_h(K)\vert^2
-\end{eqnarray*} -

-
-

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