From: Wolfgang Bangerth Date: Sun, 4 Mar 2018 08:22:42 +0000 (-0700) Subject: Expand on the discussion in the introduction to step-14. X-Git-Tag: v9.0.0-rc1~359^2 X-Git-Url: https://gitweb.dealii.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=0c85602e1c0451af56206dbfc3929e5644be172b;p=dealii.git Expand on the discussion in the introduction to step-14. --- diff --git a/examples/step-14/doc/intro.dox b/examples/step-14/doc/intro.dox index 15a115b9af..1691765294 100644 --- a/examples/step-14/doc/intro.dox +++ b/examples/step-14/doc/intro.dox @@ -71,14 +71,18 @@ and we can, by Galerkin orthogonality, rewrite this as @f[ J(e) = a(e,z-\varphi_h) @f] -for all possible functions $\varphi_h$ from the discrete test space. +where $\varphi_h$ can be chosen from the discrete test space in +whatever way we find convenient. Concretely, for Laplace's equation, the error identity reads @f[ J(e) = (\nabla e, \nabla(z-\varphi_h)). @f] -For reasons that we will not explain, we do not want to use this formula as -is, but rather split the scalar products into terms on all cells, and +Because we want to use this formula not only to compute error, but +also to refine the mesh, we need to rewrite the expression above as a +sum over cells where each cell's contribution can then be used as an +error indicator for this cell. +Thus, we split the scalar products into terms for each cell, and integrate by parts on each of them: @f{eqnarray*} J(e) @@ -89,13 +93,14 @@ integrate by parts on each of them: \sum_K (-\Delta (u-u_h), z-\varphi_h)_K + (\partial_n (u-u_h), z-z_h)_{\partial K}. @f} -Next we use that $-\Delta u=f$, and that $\partial_n u$ is a quantity that is -continuous almost everywhere, so the terms involving $\partial_n u$ on one +Next we use that $-\Delta u=f$, and that for solutions of the Laplace +equation, the solution is smooth enough that $\partial_n u$ is +continuous almost everywhere -- so the terms involving $\partial_n u$ on one cell cancels with that on its neighbor, where the normal vector has the -opposite sign. At the boundary of the domain, where there is no neighbor cell +opposite sign. (The same is not true for $\partial_n u_h$, though.) +At the boundary of the domain, where there is no neighbor cell with which this term could cancel, the weight $z-\varphi_h$ can be chosen as -zero, since $z$ has zero boundary values, and $\varphi_h$ can be chosen to -have the same. +zero, and the whole term disappears. Thus, we have @f{eqnarray*} @@ -116,7 +121,7 @@ with the neighbor cell $K'$, to obtain - \frac 12 (\partial_n u_h|_K + \partial_{n'} u_h|_{K'}, z-\varphi_h)_{\partial K\backslash \partial\Omega}. @f} -Using that for the normal vectors $n'=-n$ holds, we define the jump of the +Using that for the normal vectors on adjacent cells we have $n'=-n$, we define the jump of the normal derivative by @f[ [\partial_n u_h] := \partial_n u_h|_K + \partial_{n'} u_h|_{K'} @@ -174,6 +179,35 @@ With this, we end the discussion of the mathematical side of this program and turn to the actual implementation. +@note There are two steps above that do not seem necessary if all you +care about is computing the error: namely, (i) the subtraction of +$\phi_h$ from $z$, and (ii) splitting the integral into a sum of cells +and integrating by parts on each. Indeed, neither of these two steps +change $J(e)$ at all, as we only ever consider identities above until +the substitution of $z$ by $\tilde z$. In other words, if you care +only about estimating the global error $J(e)$, then these steps +are not necessary. On the other hand, if you want to use the error +estimate also as a refinement criterion for each cell of the mesh, +then it is necessary to (i) break the estimate into a sum of cells, +and (ii) massage the formulas in such a way that each cell's +contributions have something to do with the local error. (While the +contortions above do not change the value of the sum $J(e)$, +they change the values we compute for each cell $K$.) To this end, we +want to write everything in the form "residual times dual weight" +where a "residual" is something that goes to zero as the approximation +becomes $u_h$ better and better. For example, the quantity $\partial_n +u_h$ is not a residual, since it simply converges to the (normal +component of) the gradient of the exact solution. On the other hand, +$[\partial_n u_h]$ is a residual because it converges to $[\partial_n +u]=0$. All of the steps we have taken above in developing the final +form of $J(e)$ have indeed had the goal of bringing the final formula +into a form where each term converges to zero as the discrete solution +$u_h$ converges to $u$. This then allows considering each cell's +contribution as an "error indicator" that also converges to zero -- as +it should as the mesh is refined. + + +

The software

The step-14 example program builds heavily on the techniques already used in