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4.1.1 Galerkin's Method

Multiplying (4.1) by a function $ v(\vec r)$, which is called test or trial function, and integrating over the simulation domain gives the variational formulation

$\displaystyle \int_{\symDomain} v(\vec r) L[u(\vec r)] d\symDomain = \int_{\symDomain} v(\vec r) f(\vec r) d\symDomain.$ (4.2)

Using the notation

$\displaystyle \left(a,b\right) = \int_{\symDomain} a(\vec r)b(\vec r)d\symDomain,$ (4.3)

(4.2) can be written as

$\displaystyle \left(L[u], v\right) = \left(f, v\right).$ (4.4)

In order to obtain the corresponding discrete problem, the simulation domain, , is divided in a set of $ m$ elements, $ T_1, T_2,..., T_m$, which do not overlap, i.e. $ \forall i\neq j: T_i\cap T_j = 0$. The mesh obtained by such a domain discretization is represented by

$\displaystyle T_h(\symDomain)=\bigcup_{i=1}^{m}T_i.$ (4.5)

Further, one defines a set $ P$ of grid points, also called nodes, with each point $ p_k \in P$ being described by a unique global index $ k=1,2,...,N$, where $ N$ is the total number of grid points in the mesh.

The approximate solution, $ u_h(\vec r)$, for the unknown function, $ u(\vec r)$, is given by [152]

$\displaystyle u_h(\vec r) = \sum_{i=1}^{N} u_iN_i(\vec r),$ (4.6)

where $ N_i(\vec r)$ are the so-called basis (or shape) functions. The approximate solution of (4.4) is determined by the coefficients $ u_i$, which represent the value of the unknown function at the node $ i$. At the node $ i$, where the point is given by the coordinates $ \vec r_i$, the basis functions must satisfy the condition

$\displaystyle N_j(\vec r_i) = \symKronecker,\qquad i,j=1,...,N.$ (4.7)

Typically, the basis functions are chosen to be low order polynomials.

Substituting (4.6) in (4.4), and choosing $ v=N_j(\vec r)$ one obtains

$\displaystyle \left(L[\sum_{i=1}^{N} u_iN_i], N_j\right) = \left(f, N_j\right), \qquad j=1,...,N,$ (4.8)

and since $ L[\cdot]$ is a linear operator and the coefficients $ u_i$ are constants one can write

$\displaystyle \sum_{i=1}^{N}u_i\left(L[N_i], N_j\right) = \left(f, N_j\right), \qquad j=1,...,N.$ (4.9)

Equation (4.9) is, in fact, a linear system of $ N$ equations with $ N$ unknowns, $ u_1, u_2, ..., u_N$. Thus, it can be written in matrix notation as

$\displaystyle \mathbf{A} \mathbf{x} = \mathbf{b},$ (4.10)

where $ \mathbf{A} = (a_{ij})$ is called stiffness matrix, given by the elements

$\displaystyle a_{ij} = \left(L[N_i], N_j\right) = \int_{\symDomain} L[N_i(\vec r)] N_j(\vec r) d\symDomain,\qquad i,j=1,...,N,$ (4.11)

$ \mathbf{x} = (u_1,...,u_N)^T$ is the vector of unknown coefficients, and $ \mathbf{b} = (b_1,...,b_N)^T$ is the load vector, given by

$\displaystyle b_{j} = \left(f, N_j\right) = \int_{\symDomain} f(\vec r) N_j(\vec r) d\symDomain, \qquad j=1,...,N.$ (4.12)

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Next: 4.1.2 Assembly Up: 4.1 The Finite Element Previous: 4.1 The Finite Element
R. L. de Orio: Electromigration Modeling and Simulation