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The convergence of the finite element method

The following three lemmas apply as follows. The Lax-Milgram lemma and the more general infimum-supremum condition are used to check whether a given weak formulation has a unique solution. The Cea lemma allows for the estimation of the solution error and the rate of convergence for the finite element method.


Let \( V=\{ v \in L^2(\Omega):\int_{\Omega} \| v \|^2 +\| \nabla v \|^2 dx_1 dx_2 < \infty, tr(v)=0 \textrm{ on } \Gamma_D \} \) for \( \Omega \subset {\cal R }^d \)
Let \( B:V\times V \rightarrow {\cal R} \)
will be a two-line form \( B(\alpha_1 u_1+\alpha_2 u_2,v_1 )=\alpha_1 B(u_1,v )+\alpha_2 B(u_2,v_1 ) \)
\( B(u_1,\beta_1 v_1+\beta_2 v_2 ) = \beta_1 B(u_1,v_1 )+ \beta_2 B(u_1,v_2 ) \quad \alpha_1,\alpha_2,\beta_1,\beta_2 \in {\cal R}, \forall u_1,u_2,v_1,v_2 \in V \),
ciągłą \( \exists M > 0, |B(u,v)| \leq M \|u\|_V \|v\|_V \forall u,v \in V \)
and coercive, i.e. \( \exists \alpha > 0, |B(u,u)| \geq \alpha \|u\|^2_V \forall u \in V \).
Let \( L:V \rightarrow {\cal R} \) will be a linear form
\( L(\alpha_1 v_1+\alpha_2 v_2 )=\alpha_1 L(v_1 )+\alpha_2 L(v_2 )\quad \alpha_1,\alpha_2\in {\cal R}, \forall v_1,v_2 \in V \), continuous, i.e.
\( \exists C > 0, |L(v)| \leq C \|v\|_V \forall v \in V \).
Then the weak problem \( B(u,v)=L(v)-B(\hat{u},v) \quad \forall v \in V \)
has a unique solution, such that \( \|u+\hat{u} \|_V \leq \|\hat{u}\|_V \left(1+\frac{M}{\alpha} \right)+\frac{C}{\alpha} \).


For more complicated cases of weak formulations \( B(u,v)=L(v)-B(\hat{u},v) \quad \forall v \in V \) instead of the Lax-Milgram Lemma, we use the Babuska Lemma equivalent to the Brezzi Lemma [1].


Consider the solution to the problem of the finite element method on a computational mesh.
Let \( V=H^1_0(\Omega) = \{ v \in L^2(\Omega):\int_{\Omega} \| v \|^2 +\| \nabla v \|^2 dx_1 dx_2 < \infty, tr(v)=0 \textrm{ on } \Gamma_D \} \) for \( \Omega \subset {\cal R }^d \)
Let \( B:V\times V \rightarrow {\cal R} \)
be a two-line form \( B(\alpha_1 u_1+\alpha_2 u_2,v_1 )=\alpha_1 B(u_1,v )+\alpha_2 B(u_2,v_1 ) \)
\( B(u_1,\beta_1 v_1+\beta_2 v_2 ) = \beta_1 B(u_1,v_1 )+ \beta_2 B(u_1,v_2 ) \quad \alpha_1,\alpha_2,\beta_1,\beta_2 \in {\cal R}, \forall u_1,u_2,v_1,v_2 \in V \),
continuous \( \exists M > 0, |B(u,v)| \leq M \|u\|_V \|v\|_V \forall u,v \in V \)
and coercive \( \exists \alpha > 0, |B(u,u)| \geq \alpha \|u\|^2_V \forall u \in V \).
Let \( L:V \rightarrow {\cal R} \) be a line form.
\( L(\alpha_1 v_1+\alpha_2 v_2 )=\alpha_1 L(v_1 )+\alpha_2 L(v_2 ) \quad \alpha_1,\alpha_2\in {\cal R}, \forall v_1,v_2 \in V \),
continuous \( \exists C > 0, |L(v)| \leq C \|v\|_V \forall v \in V \).
Let \( u \in V \) be a solution of a weak problem \( B(u,v)=L(v)-B(\hat{u},v) \quad \forall v \in V \).
Let \( T_{hp}=\{ \left(K, X\left(K\right), \Pi_p \right) \}_K \) be a computational grid.
Let \( V_{hp} \subset V \) be an approximation space on a computational grid.
Let \( u_{hp} \in V_{hp} \) be a solution to the problem of the finite element method on a computational mesh.
\( B(u_{hp},v_{hp})=L(v_{hp})-B(\hat{u},v_{hp}) \quad \forall v_{hp} \in V_{hp} \) on the computational grid.
Then
\( \|u-u_{hp}\|_V \leq \frac{M}{\alpha} min_{w_{hp} \in V_{hp} } \| u-w_{hp} \|_V \).


The meaning of the Cea Lemma is as follows. Ideally, the distance of the solution of the finite element method problem \( u_{hp} \in V_{hp} \) on the computational grid \( T_{hp }=\{ \left(K, X\left(K\right), \Pi_p \right) \}_K \) from the solution of the weak problem \( u \in V \) would be the minimum distance of solution \( u \in V \) of a weak problem from all elements of space \( V_h \) in which we are looking for a solution to the problem of the finite element method. It would be if constant \( \frac{M}{\alpha} =1 \). However it is \( \frac{M}{\alpha} \geq 1 \). This means that the error we make when solving the problem with the finite element method is burdened with an error resulting from the construction of the computational mesh. \( T_{hp}=\{ \left(K, X\left(K\right), \Pi_p \right) \}_K \) (adaptation of the computational mesh increases the size of the space \( V_{hp} \) and makes our approximate solution \( u_{hp} \in V_{hp} \) closer to the perfect solution \( u \in V \). The remaining error is due to the ratio of the constants \( M \) continuity and \( {\alpha} \) coercivity of the bilinear functional \( B:V\times V \rightarrow {\cal R } \). This error can be eliminated by using the stabilization methods described in the relevant modules of the manual.


In his work [2] prof. Ivo Babuśka puts forward the mathematical theorems from which it implies that the hp algorithm of the adaptive finite element method for elliptic problems used to obtain the fastest possible exponential convergence \( err\_rel= error\_0 exp (C nrdof^{\alpha}) \) where \( error\_0, C, \alpha \) are constants depending on the problem, \( err\_rel=\frac{\|u_{hp}-u_{\frac{h}{2}p+1}\|_{H^1(\Omega)}}{\|u_{\frac{h}{2}p+1}\|_{H^1(\Omega)}} \) is the error in the computational mesh relative to the reference mesh, \( nrdof \) is the problem size of the finite element method on the computational mesh (degrees of freedom).

Ostatnio zmieniona Wtorek 14 z Czerwiec, 2022 16:44:27 UTC Autor: Maciej Paszynski
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