From: Wolfgang Bangerth Date: Sun, 11 Oct 2020 20:37:48 +0000 (-0600) Subject: Explain SUPG in step-9. X-Git-Tag: v9.3.0-rc1~1006^2~1 X-Git-Url: https://gitweb.dealii.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=87958178680336ab93eaecef48c1fc0a60c01063;p=dealii.git Explain SUPG in step-9. --- diff --git a/examples/step-9/doc/intro.dox b/examples/step-9/doc/intro.dox index e8c4c6ca52..4e42b3abad 100644 --- a/examples/step-9/doc/intro.dox +++ b/examples/step-9/doc/intro.dox @@ -11,7 +11,7 @@ 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 text. The use of multiple threads will then be detailed at the +following. The use of multiple threads will then be detailed at the relevant places within the program. We will, however, follow the general discussion of the WorkStream approach detailed in the @ref threads "Parallel computing with multiple processors accessing shared memory" @@ -20,13 +20,14 @@ documentation module.

Discretizing the advection equation

-In the present example program, we shall numerically approximate the +In the present example program, we want to numerically approximate the solution of the advection equation @f[ \beta \cdot \nabla u = f, @f] where $\beta$ is a vector field that describes the advection direction and -speed (which may be dependent on the space variables), $f$ is a source +speed (which may be dependent on the space variables if +$\beta=\beta(\mathbf x)$), $f$ 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 @@ -53,8 +54,8 @@ 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 stated, the transport equation cannot be solved in a stable way using -the standard finite element method, however. The problem is that +Unfortunately, the equation stated above cannot be solved in a stable way using +the standard finite element method. The problem is that solutions to this equation possess insufficient regularity perpendicular to the transport direction: while they are smooth along the streamlines defined by the "wind field" @@ -66,24 +67,26 @@ for the derivatives in the perpendicular direction, and consequently if $u$ is discontinuous at a point on the inflow boundary, then this discontinuity will simply be transported along the streamline of the wind field that starts at this boundary point. - These discontinuities lead to numerical instabilities that make a stable solution by a standard continuous finite element 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 +impossible. + +A standard approach to address this difficulty is the "streamline-upwind +Petrov-Galerkin" (SUPG) method, sometimes also called the +streamline diffusion method. A good explanation of the method can be +found in @cite elman2005 . Formally, this method replaces the step +in which we derive the the weak form of the differential equation from +the strong form: Instead of multiplying the equation by a test +function $v$ and integrating over the domain, we instead multiply +by $v + \delta \beta\cdot\nabla v$, where $\delta$ is a parameter that is chosen in the range of the (local) mesh width $h$; good results are usually obtained by setting $\delta=0.1h$. -The modification in the test function can be thought as -adding a small amount of diffusion along the direction $\beta$ of the -wind field, i.e., along "stream lines". The value added here is small enough -that we do not introduce excessive diffusion and also large enough that the -linear system can be solved in a reasonable amount of time. -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. +(Why this is called "streamline diffusion" will be explained below; +for the moment, let us simply take for granted that this is how we +derive a stable discrete formulation.) +The value for $\delta$ here is small enough +that we do not introduce excessive diffusion, but large enough that the +resulting problem is well-posed. Using the test functions as defined above, an initial weak form of the problem would ask for finding a function $u_h$ so that for all test @@ -176,6 +179,117 @@ and then to obtain as system matrix. We will assemble this matrix in the program. +

Why is this method called "streamline diffusion"?

+ +Looking at the bilinear form mentioned above, we see that the discrete +solution has to satisfy an equation of which the left hand side in +weak form has a domain term of the kind +@f[ + (v_h + \delta \beta\cdot\nabla v_h, \beta \cdot \nabla u_h)_\Omega, +@f] +or if we split this up, the form +@f[ + (v_h, \beta \cdot \nabla u_h)_\Omega + + + (\delta \beta\cdot\nabla v_h, \beta \cdot \nabla u_h)_\Omega. +@f] +If we wanted to see what strong form of the equation that would +correspond to, we need to integrate the second term. This yields the +following formulation, where for simplicity we'll ignore boundary +terms for now: +@f[ + (v_h, \beta \cdot \nabla u_h)_\Omega + - + \left(v_h, \delta \nabla \cdot \left[\beta \left(\beta \cdot \nabla + u_h\right)\right]\right)_\Omega + + + \text{boundary terms}. +@f] +Let us assume for a moment that the wind field $\beta$ is +divergence-free, i.e., that $\nabla \cdot \beta = 0$. Then applying +the product rule to the derivative of the term in square brackets on +the right and using the divergence-freeness will give us the following: +@f[ + (v_h, \beta \cdot \nabla u_h)_\Omega + - + \left(v_h, \delta \left[\beta \cdot \nabla\right] \left[\beta \cdot \nabla + \right]u_h\right)_\Omega + + + \text{boundary terms}. +@f] +That means that the strong form of the equation would be of the sort +@f[ + \beta \cdot \nabla u_h + - + \delta + \left[\beta \cdot \nabla\right] \left[\beta \cdot \nabla + \right] u_h. +@f] +What is important to recognize now is that $\beta\cdot\nabla$ is the +derivative in direction $\beta$. So, if we denote this by +$\beta\cdot\nabla=\frac{\partial}{\partial \beta}$ (in the same way as +we often write $\mathbf n\cdot\nabla=\frac{\partial}{\partial n}$ for +the derivative in normal direction at the boundary), then the strong +form of the equation is +@f[ + \beta \cdot \nabla u_h + - + \delta + \frac{\partial^2}{\partial\beta^2} u_h. +@f] +In other words, the unusual choice of test function is equivalent to +the addition of term to the strong form that corresponds to a second +order (i.e., diffusion) differential operator in the direction of the wind +field $\beta$, i.e., in "streamline direction". A fuller account would +also have to explore the effect of the test function on boundary +values and why it is necessary to also use the same test function for +the right hand side, but the discussion above might make clear where +the name "streamline diffusion" for the method originates from. + + +

Why is this method also called "Petrov-Galerkin"?

+ +A "Galerkin method" is one where one obtains the weak formulation by +multiplying the equation by a test function $v$ (and then integrating +over $\Omega$) where the functions $v$ are from the same space as the +solution $u$ (though possibly with different boundary values). But +this is not strictly necessary: One could also imagine choosing the +test functions from a different set of functions, as long as that +different set has "as many dimensions" as the original set of +functions so that we end up with as many independent equations as +there are degrees of freedom (where all of this needs to be +appropriately defined in the infinite-dimensional case). Methods that +make use of this possibility (i.e., choose the set of test functions +differently than the set of solutions) are called "Petrov-Galerkin" +methods. In the current case, the test functions all have the form +$v+\beta\cdot\nabla v$ where $v$ is from the set of solutions. + + +

Why is this method also called "streamline-upwind"?

+ +[Upwind methods](https://en.wikipedia.org/wiki/Upwind_scheme) have a +long history in the derivation of stabilized schemes for advection +equations. Generally, the idea is that instead of looking at a +function "here", we look at it a small distance further "upstream" or "upwind", +i.e., where the information "here" originally came from. This might +suggest not considering $u(\mathbf x)$, but +something like $u(\mathbf x - \delta \beta)$. Or, equivalently upon +integration, we could evaluate $u(\mathbf x)$ and instead consider $v$ +a bit downstream: $v(\mathbf x+\delta \beta)$. This would be cumbersome +for a variety of reasons: First, we would have to define what $v$ +should be if $\mathbf x + \delta \beta$ happens to be outside +$\Omega$; second, computing integrals numerically would be much more +awkward since we no longer evaluate $u$ and $v$ at the same quadrature +points. But since we assume that $\delta$ is small, we can do a Taylor +expansion: +@f[ + v(\mathbf x + \delta \beta) + \approx + v(\mathbf x) + \delta \beta \cdot \nabla v(\mathbf x). +@f] +This form for the test function should by now look familiar. + +

Solving the linear system that corresponds to the advection equation

As the resulting matrix is no longer symmetric positive definite, we cannot