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### The finite element method theory implementation and applications

1. 1. Chapter 1Piecewise Polynomial Approximation in 1DAbstract In this chapter we introduce a type of functions called piecewise poly-nomials that can be used to approximate other more general functions, and whichare easy to implement in computer software. For computing piecewise polynomialapproximations we present two techniques, interpolation and L2 -projection. We alsoprove estimates for the error in these approximations.1.1 Piecewise Polynomial Spaces1.1.1 The Space of Linear PolynomialsLet I D Œx0 ; x1  be an interval on the real axis and let P1 .I / denote the vector spaceof linear functions on I , deﬁned by P1 .I / D fv W v.x/ D c0 C c1 x; x 2 I; c0 ; c1 2 Rg (1.1)In other words P1 .I / contains all functions of the form v.x/ D c0 C c1 x on I . Perhaps the most natural basis for P1 .I / is the monomial basis f1; xg, sinceany function v in P1 .I / can be written as a linear combination of 1 and x. Thatis, a constant c0 times 1 plus another constant c1 times x. In doing so, v isclearly determined by specifying c0 and c1 , the so-called coefﬁcients of the linearcombination. Indeed, we say that v has two degrees of freedom. However, c0 and c1 are not the only degrees of freedom possible for v. To seethis, recall that a line, or linear function, is uniquely determined by requiring it topass through any two given points. Now, obviously, there are many pairs of pointsthat can specify the same line. For example, .0; 1/ and .2; 3/ can be used to specifyv D x C1, but so can . 1; 0/ and .4; 5/. In fact, any pair of points within the intervalI will do as degrees of freedom for v. In particular, v can be uniquely determinedby its values ˛0 D v.x0 / and ˛1 D v.x1 / at the end-points x0 and x1 of I .M.G. Larson and F. Bengzon, The Finite Element Method: Theory, Implementation, 1and Applications, Texts in Computational Science and Engineering 10,DOI 10.1007/978-3-642-33287-6__1, © Springer-Verlag Berlin Heidelberg 2013
2. 2. 2 1 Piecewise Polynomial Approximation in 1D To prove this, let us assume that the values ˛0 D v.x0 / and ˛1 D v.x1 / are given.Inserting x D x0 and x D x1 into v.x/ D c0 C c1 x we obtain the linear system Ä Ä Ä 1 x0 c0 ˛ D 0 (1.2) 1 x1 c1 ˛1for ci , i D 1; 2. Computing the determinant of the system matrix we ﬁnd that it equals x1 x0 ,which also happens to be the length of the interval I . Hence, the determinant ispositive, and therefore there exist a unique solution to (1.2) for any right hand sidevector. Moreover, as a consequence, there is exactly one function v in P1 .I /, whichhas the values ˛0 and ˛1 at x0 and x1 , respectively. In the following we shall referto the points x0 and x1 as the nodes. We remark that the system matrix above is called a Vandermonde matrix. Knowing that we can completely specify any function in P1 .I / by its node values˛0 and ˛1 we now introduce a new basis f 0 ; 1 g for P1 .I /. This new basis is calleda nodal basis, and is deﬁned by ( 1; if i D j j .xi / D ; i; j D 0; 1 (1.3) 0; if i ¤ jFrom this deﬁnition we see that each basis function j , j D 0; 1, is a linear function,which takes on the value 1 at node xj , and 0 at the other node. The reason for introducing the nodal basis is that it allows us to express anyfunction v in P1 .I / as a linear combination of 0 and 1 with ˛0 and ˛1 ascoefﬁcients. Indeed, we have v.x/ D ˛0 0 .x/ C ˛1 1 .x/ (1.4)This is in contrast to the monomial basis, which given the node values requiresinversion of the Vandermonde matrix to determine the corresponding coefﬁcients c0and c1 . The nodal basis functions take the following explicit form on I x1 x x x0 0 .x/ D ; 1 .x/ D (1.5) x1 x0 x1 x0This follows directly from the deﬁnition (1.3), or by solving the linear system (1.2)with Œ1; 0T and Œ0; 1T as right hand sides.1.1.2 The Space of Continuous Piecewise Linear PolynomialsA natural extension of linear functions is piecewise linear functions. In constructinga piecewise linear function, v, the basic idea is to ﬁrst subdivide the domain of vinto smaller subintervals. On each subinterval v is simply given by a linear function.
3. 3. 1.1 Piecewise Polynomial Spaces 3Fig. 1.1 A continuouspiecewise linear function v v(x) x x0 x1 x2 x3 x4 x5Continuity of v between adjacent subintervals is enforced by placing the degrees offreedom at the start- and end-points of the subintervals. We shall now formalize thismore mathematically stringent. Let I D Œ0; L be an interval and let the n C 1 node points fxi gnD0 deﬁne a ipartition I W 0 D x0 < x1 < x2 < : : : < xn 1 < xn D L (1.6)of I into n subintervals Ii D Œxi 1 ; xi , i D 1; 2 : : : ; n, of length hi D xi xi 1 .We refer to the partition I as to a mesh. On the mesh I we deﬁne the space Vh of continuous piecewise linear functionsby Vh D fv W v 2 C 0 .I /; vjIi 2 P1 .Ii /g (1.7)where C 0 .I / denotes the space of continuous functions on I , and P1 .Ii / denotesthe space of linear functions on Ii . Thus, by construction, the functions in Vh arelinear on each subinterval Ii , and continuous on the whole interval I . An exampleof such a function is shown in Fig. 1.1 It should be intuitively clear that any function v in Vh is uniquely determined byits nodal values fv.xi /gnD0 i (1.8)and, conversely, that for any set of given nodal values f˛i gnD0 there exist a function v iin Vh with these nodal values. Motivated by this observation we let the nodal valuesdeﬁne our degrees of freedom and introduce a basis fj gn D0 for Vh associated with jthe nodes and such that ( 1; if i D j j .xi / D ; i; j D 0; 1; : : : ; n (1.9) 0; if i ¤ jThe resulting basis functions are depicted in Fig. 1.2. Because of their shape the basis functions i are often called hat functions. Eachhat function is continuous, piecewise linear, and takes a unit value at its own node xi ,while being zero at all other nodes. Consequently, i is only non-zero on the two
4. 4. 4 1 Piecewise Polynomial Approximation in 1DFig. 1.2 A typical hat 0 ifunction i on a mesh. Also 1shown is the “half hat” 0 x x0 x1 xi−1 xi xi+1 xnintervals Ii and Ii C1 containing node xi . Indeed, we say that the support of i isIi [Ii C1 . The exception is the two “half hats” 0 and n at the leftmost and rightmostnodes a D x0 and xn D b with support only on one interval. By construction, any function v in Vh can be written as a linear combinationof hat functions fi gnD0 and corresponding coefﬁcients f˛i gnD0 with ˛i D v.xi /, i ii D 0; 1; : : : ; n, the nodal values of v. That is, X n v.x/ D ˛i i .x/ (1.10) i D0 The explicit expressions for the hat functions are given by 8 ˆ.x xi 1 /= hi ; ˆ if x 2 Ii < i D .xi C1 x/= hi C1 ; if x 2 Ii C1 (1.11) ˆ ˆ :0; otherwise1.2 InterpolationWe shall now use the function spaces P1 .I / and Vh to construct approximations,one from each space, to a given function f . The method we are going to use isvery simple and only requires the evaluation of f at the node points. It is calledinterpolation.1.2.1 Linear InterpolationAs before, we start on a single interval I D Œx0 ; x1 . Given a continuous function fon I , we deﬁne the linear interpolant f 2 P1 .I / to f by f .x/ D f .x0 /0 C f .x1 /1 (1.12)We observe that interpolant approximates f in the sense that the values of f andf are the same at the nodes x0 and x1 (i.e., f .x0 / D f .x0 / and f .x1 / D f .x1 /).
5. 5. 1.2 Interpolation 5Fig. 1.3 A function f and itslinear interpolant f p f(x) f(x) x x0 x1 In Fig. 1.3 we show a function f and its linear interpolant f . Unless f is linear, f will only approximate f , and it is therefore of interest tomeasure the difference f f , which is called the interpolation error. To this end,we need a norm. Now, there are many norms and it is not easy to know which is thebest. For instance, should we measure the interpolation error in the inﬁnity norm,deﬁned by kvk1 D max jv.x/j (1.13) x2I 2or the L .I /-norm deﬁned, for any square integrable function v on I , by ÂZ Ã1=2 kvkL2 .I / D v2 dx (1.14) I We shall use the latter norm, since it captures the average size of a function,whereas the former only captures the pointwise maximum. In this context we recall that the L2 .I /-norm, or any norm for that matter, obeysthe Triangle inequality kv C wkL2 .I / Ä kvkL2 .I / C kwkL2 .I / (1.15)as well as the Cauchy-Schwarz inequality Z vw dx Ä kvkL2 .I / kwkL2 .I / (1.16) Ifor any two functions v and w in L2 .I /. Then, using the L2 -norm to measure the interpolation error, we have thefollowing results.Proposition 1.1. The interpolant f satisﬁes the estimates kf f kL2 .I / Ä C h2 kf 00 kL2 .I / (1.17) k.f f /0 kL2 .I / Ä C hkf 00 kL2 .I / (1.18)where C is a constant, and h D x1 x0 .
6. 6. 6 1 Piecewise Polynomial Approximation in 1DProof. Let e D f f denote the interpolation error. From the fundamental theorem of calculus we have, for any point y in I , Z y e.y/ D e.x0 / C e 0 dx (1.19) x0where e.x0 / D f .x0 / f .x0 / D 0 due to the deﬁnition of f . Now, using the Cauchy-Schwarz inequality we have Z y e.y/ D e 0 dx (1.20) x0 Z y Ä je 0 j dx (1.21) x0 Z Ä 1 je 0 j dx (1.22) I ÂZ Ã1=2 ÂZ Ã1=2 Ä 12 dx e 02 dx (1.23) I I ÂZ Ã1=2 02 Dh 1=2 e dx (1.24) Ior, upon squaring both sides, Z e.y/2 Ä h e 02 dx D hke 0 k2 2 .I / L (1.25) IIntegrating this inequality over I we further have Z Z kek2 2 .I / D L e.y/2 dy Ä hke 0 k2 2 .I / dy D h2 ke 0 k2 2 .I / L L (1.26) I Isince the integrand to the right of the inequality is independent of y. Thus, we have kekL2 .I / Ä hke 0 kL2 .I / (1.27) With a similar, but slightly different argument, we also have ke 0 kL2 .I / Ä hke 00 kL2 .I / (1.28) Hence, we conclude that kekL2 .I / Ä hke 0 kL2 .I / Ä h2 ke 00 kL2 .I / (1.29)
7. 7. 1.2 Interpolation 7Fig. 1.4 The function f(x)f .x/ D 2x sin.2 x/ C 3 andits continuous piecewiselinear interpolant f .x/ on auniform mesh of I D Œ0; 1 3with six nodes xi ,i D 0; 1; : : : ; 5 p f(x) 2 x 0 = x0 x1 x2 x3 x4 x5 = 1from which the ﬁrst inequality of the proposition follows by noting that since fis linear e 00 D f 00 . The second inequality of the proposition follows similarlyfrom (1.26) The difference in argument between deriving (1.27) and (1.28) has to do with thefact that we can not simply replace e with e 0 in (1.19), since e 0 .x0 / ¤ 0. However,noting that e.x0 / D e.x1 / D 0, there exist by Rolle’s theorem a point x in I such Nthat e 0 .x/ D 0, which means that N Z y Z y 0 0 00 e .y/ D e .x/ C N e dx D e 00 dx (1.30) N x N xStarting instead from this and proceeding as shown above (1.28) follows. t u Examining the proof of Proposition 1.1 we note that the constant C equals unityand could equally well be left out. We have, however, chosen to retain this constant,since the estimates generalize to higher spatial dimensions, where C is not unity.The important thing to understand is how the interpolation error depends on theinterpolated function f , and the size of the interval h.1.2.2 Continuous Piecewise Linear InterpolationIt is straight forward to extend the concept of linear interpolation on a single intervalto continuous piecewise linear interpolation on a mesh. Indeed, given a continuousfunction f on the interval I D Œ0; L, we deﬁne its continuous piecewise linearinterpolant f 2 Vh on a mesh I of I by X n f .x/ D f .xi /i .x/ (1.31) i D1 Figure 1.4 shows the continuous piecewise linear interpolant f .x/ to f .x/ D2x sin.2 x/ C 3 on a uniform mesh of I D Œ0; 1 with 6 nodes. Regarding the interpolation error f f we have the following results.
8. 8. 8 1 Piecewise Polynomial Approximation in 1DProposition 1.2. The interpolant f satisﬁes the estimates X n kf f k2 2 .I / Ä C L h4 kf 00 k2 2 .Ii / i L (1.32) i D1 X n k.f f /0 k2 2 .I / Ä C L h2 kf 00 k2 2 .Ii / i L (1.33) i D1Proof. Using the Triangle inequality and Proposition 1.1, we have X n kf f k2 2 .I / D L kf f k2 2 .Ii / L (1.34) i D1 X n Ä C h4 kf 00 k2 2 .Ii / i L (1.35) i D1which proves the ﬁrst estimate. The second follows similarly. t uProposition 1.2 says that the interpolation error vanish as the mesh size hi tends tozero. This is natural, since we expect the interpolant f to be a better approximationto f where ever the mesh is ﬁne. The proposition also says that if the secondderivative f 00 of f is large then the interpolation error is also large. This is alsonatural, since if the graph of f bends a lot (i.e., if f 00 is large) then f is hard toapproximate using a piecewise linear function.1.3 L2 -ProjectionInterpolation is a simple way of approximating a continuous function, but thereare, of course, other ways. In this section we shall study so-called orthogonal-, orL2 -projection. L2 -projection gives a so to speak good on average approximation,as opposed to interpolation, which is exact at the nodes. Moreover, in contrast tointerpolation, L2 -projection does not require the function we seek to approximateto be continuous, or have well-deﬁned node values.1.3.1 DeﬁnitionGiven a function f 2 L2 .I / the L2 -projection Ph f 2 Vh of f is deﬁned by Z .f Ph f /v dx D 0; 8v 2 Vh (1.36) I
9. 9. 1.3 L2 -Projection 9 f − Ph f f v Ph f VhFig. 1.5 Illustration of the function f and its L2 -projection Ph f on the space VhFig. 1.6 The functionf .x/ D 2x sin.2 x/ C 3 andits L2 -projection Ph f on auniform mesh of I D Œ0; 1 3with six nodes, xi ,i D 1; 2; : : : ; 6 2 f(x) 1 Ph f(x) x 0 = x0 x1 x2 x3 x4 x5 = 1 In analogy with projection onto subspaces of Rn , (1.34) deﬁnes a projection of fonto Vh , since the difference f Ph f is required to be orthogonal to all functionsv in Vh . This is illustrated in Fig. 1.5. As we shall see later on, Ph f is the minimizer of minv2Vh kf vkL2 .I / , andtherefore we say that it approximates f in a least squares sense. In fact, Ph f is thebest approximation to f when measuring the error f Ph f in the L2 -norm. In Fig. 1.6 we show the L2 -projection of f .x/ D 2x sin.2 x/ C 3 computed onthe same mesh as was used for showing the continuous piecewise linear interpolant f in Fig. 1.4. It is instructive to compare these two approximations because ithighlights their different characteristics. The interpolant f approximates f exactlyat the nodes, while the L2 -projection Ph f approximates f on average. In doingso, it is common for Ph f to over and under shoot local maxima and minima off , respectively. Also, both the interpolant and the L2 -projection have difﬁcultywith approximating rapidly oscillating or discontinuous functions unless the nodepositions are adjusted appropriately.
10. 10. 10 1 Piecewise Polynomial Approximation in 1D1.3.2 Derivation of a Linear System of EquationsIn order to actually compute the L2 -projection Ph f , we ﬁrst note that the deﬁni-tion (1.36) is equivalent to Z .f Ph f /i dx D 0; i D 0; 1; : : : ; n (1.37) Iwhere i , i D 0; 1; : : : ; n, are the hat functions. This is a consequence of the factthat if (1.36) is satisﬁed for v anyone of the hat functions, then it is also satisﬁedfor v a linear combination of hat functions. Conversely, since any function v in Vh isprecisely such a linear combination of hat functions, (1.37) implies (1.36). Then, since Ph f belongs to Vh it can be written as the linear combination X n Ph f D j j (1.38) j D0where j , j D 0; 1; : : : ; n, are n C 1 unknown coefﬁcients to be determined. Inserting the ansatz (1.38) into (1.37) we get 0 1 Z Z X n f i dx D @ j j A i dx (1.39) I I j D0 X n Z D j j i dx; i D 0; 1; : : : ; n (1.40) j D0 IFurther, introducing the notation Z Mij D j i dx; i; j D 0; 1; : : : ; n (1.41) I Z bi D f i dx; i D 0; 1; : : : ; n (1.42) Iwe have X n bi D Mij j; i D 0; 1; : : : ; n (1.43) j D0which is an .n C 1/ .n C 1/ linear system for the n C 1 unknown coefﬁcients j,j D 0; 1; : : : ; n. In matrix form, we write this M Db (1.44)
11. 11. 1.3 L2 -Projection 11where the entries of the .n C 1/ .n C 1/ matrix M and the .n C 1/ 1 vector bare deﬁned by (1.41) and (1.42), respectively. We, thus, conclude that the coefﬁcients j , j D 0; 1; : : : ; n in the ansatz (1.38)satisfy a square linear system, which must be solved in order to obtain the L2 -projection Ph f . For historical reasons we refer to M as the mass matrix and to b as the loadvector.1.3.3 Basic Algorithm to Compute the L2 -ProjectionThe following algorithm summarizes the basic steps for computing the L2 -projection Ph f :Algorithm 1 Basic algorithm to compute the L2 -projection1: Create a mesh with n elements on the interval I and deﬁne the corresponding space of continuous piecewise linear functions Vh .2: Compute the .n C 1/ .n C 1/ matrix M and the .n C 1/ 1 vector b, with entries Z Z Mij D j i dx; bi D f i dx (1.45) I I3: Solve the linear system M Db (1.46)4: Set X n Ph f D j j (1.47) j D01.3.4 A Priori Error EstimateNaturally, we are interested in knowing how good Ph f is at approximating f . Inparticular, we wish to derive bounds for the error f Ph f . The next theorem givesa key result for deriving such error estimates. It is a so-called a best approximationresult.Theorem 1.1. The L2 -projection Ph f , deﬁned by (1.36), satisﬁes the best approx-imation result kf Ph f kL2 .I / Ä kf vkL2 .I / ; 8v 2 Vh (1.48)Proof. Using the deﬁnition of the L2 -norm and writing f Ph f D f vCv Ph f ,with v an arbitrary function in Vh , we have
12. 12. 12 1 Piecewise Polynomial Approximation in 1D Z kf Ph f k2 2 .I / D L .f Ph f /.f vCv Ph f / dx (1.49) I Z Z D .f Ph f /.f v/ dx C .f Ph f /.v Ph f / dx I I (1.50) Z D .f Ph f /.f v/ dx (1.51) I Ä kf Ph f kL2 .I / kf vkL2 .I / (1.52)where we used the deﬁnition of the L2 -projection to conclude that Z .f Ph f /.v Ph f / dx D 0 (1.53) Isince v Ph f 2 Vh . Dividing by kf Ph f kL2 .I / concludes the proof. t uThis shows that Ph f is the closest of all functions in Vh to f when measuring inthe L2 -norm. Hence, the name best approximation result. We can use best approximation result together with interpolation estimates tostudy how the error f Ph f depends on the mesh size. In doing so, we have thefollowing basic so-called a priori error estimate.Theorem 1.2. The L2 -projection Ph f satisﬁes the estimate X n kf Ph f k2 2 .I / Ä C L h4 kf 00 k2 2 .Ii / i L (1.54) i D1Proof. Starting from the best approximation result, choosing v D f the interpolantof f , and using the interpolation error estimate of Proposition 1.1, we have kf Ph f k2 2 .I / Ä kf L f k2 2 .I / L (1.55) X n Ä kf f k2 2 .Ii / L (1.56) i D1 X n Ä C h4 kf 00 k2 2 .Ii / i L (1.57) i D1which proves the estimate. t uDeﬁning h D max1Äi Än hi we conclude that kf Ph f kL2 .I / Ä C h2 kf 00 kL2 .I / (1.58)Thus, the L2 -error kf Ph f kL2 .I / tends to zero as the maximum mesh size h tendsto zero.
13. 13. 1.4 Quadrature 131.4 QuadratureTo compute the L2 -projection we need to compute the mass matrix M whose entriesare integrals involving products of hat functions. One way of doing this is to usequadrature, or, numerical integration. To this end, f be a continuous function onthe interval I D Œx0 ; x1 , and consider the problem of evaluating, approximately,the integral Z J D f .x/ dx (1.59) I A quadrature rule is a formula that is used to compute integrals approximately.It it usually derived by ﬁrst interpolating the integrand f by a polynomial and thenintegrating the interpolant. Depending on the degree of the interpolating polynomialone obtains quadrature rules of different computational complexity and accuracy.Evaluating a quadrature rule generally involves summing values of the integrand fat a set of carefully selected quadrature points within the interval I times the intervallength h D x1 x0 . We shall next describe three classical quadrature rules called theMid-point rule, the Trapezoidal rule, and Simpson’s formula, which corresponds tousing polynomial interpolation of degree 0, 1, and 2 on f , respectively.1.4.1 The Mid-Point RuleInterpolating f with the constant f .m/, where m D .x0 C x1 /=2 is the mid-pointof I , we get J f .m/h (1.60)which is the Mid-point rule. Geometrically this means that we approximate the areaunder the integrand f with the area of the square f .m/h, see Fig. 1.7. The Mid-point rule integrates linear polynomials exactly.1.4.2 The Trapezoidal RuleContinuing, interpolating f with the line passing through the points .x0 ; f .x0 // and.x1 ; f .x1 // we get f .x0 / C f .x1 / J h (1.61) 2which is the Trapezoidal rule. Geometrically this means that we approximate thearea under f with the area under the trapezoidal with the four corner points .x0 ; 0/,
14. 14. 14 1 Piecewise Polynomial Approximation in 1DFig. 1.7 The area of theshaded square approximates R f(x)J D I f .x/ dx x x0 m x1Fig. 1.8 The area of theshaded trapezoidal f(x)approximates RJ D I f .x/ dx x x0 x1.x0 ; f .x0 //, .x1 ; 0/, and .x1 ; f .x1 //, see Fig. 1.8. The Trapezoidal rule is also exactfor linear polynomials.1.4.3 Simpson’s FormulaThis rule corresponds to a quadratic interpolant using the end-points and the mid-point of the interval I as nodes. To simplify things a bit let I D Œ0; l be the intervalof integration and let g.x/ D c0 Cc1 x Cc2 x 2 be the interpolant. Since g interpolates l lf at the points .0; f .0//, . 2 ; f . 2 //, and .l; f .l// (i.e., its graph passes trough thesepoints) their coordinates must satisfy the equation for g. This yields the followinglinear system for c0 , c1 , and c2 . 2 32 3 2 3 0 0 1 c0 f .0/ 4 1 l 2 1 l 15 4c1 5 D 4f . l /5 (1.62) 4 2 2 l2 l 1 c2 f .l/Solving this one readily ﬁndsc0 D 2.f .0/ 2f . 2 / Cf .l//= l 2 ; c1 D l .3f .0/ 4f . 2 / Cf .l//= l; c2 D f .0/ l (1.63) Now, integrating g from 0 to l one eventually ends up with
15. 15. 1.5 Computer Implementation 15 Z l f .0/ C 4f . 1 l/ C f .l/ g.x/ dx D 2 l (1.64) 0 6which is Simpson’s formula. On the interval I D Œx0 ; x1  Simpson’s formula takes the form f .x0 / C 4f .m/ C f .x1 / J h (1.65) 6with m D 1 .x0 C x1 / and h D x1 x0 . 2 Simpson’s formula is exact for third order polynomials.1.5 Computer Implementation1.5.1 Assembly of the Mass MatrixHaving studied various quadrature rules, let us now go through the nitty gritty detailsof how to assembleRthe mass matrix M and load vector b. We begin by calculatingthe entries Mij D I i j dx of the mass matrix. Because each hat i is a linearpolynomial the product of two hats is a quadratic polynomial. Thus, Simpson’sformula can be used to integrate Mij exactly. In doing so, since the hats i andj lack common support for ji j j > 1 only Mi i , Mi i C1 , and Mi C1i need to becalculated. All other matrix entries are zero by default. This is clearly seen fromFig. 1.9 showing two neighbouring hat functions and their support. This leads to theobservation that the mass matrix M is tridiagonal. Starting with the diagonal entries Mi i and using Simpson’s formula we have Z Mi i D i2 dx (1.66) I Z xi Z xi C1 D i2 dx C i2 dx (1.67) xi 1 xi 0 C 4 . 1 /2 C 1 1 C 4 . 1 /2 C 0 D 2 hi C 2 hi C1 (1.68) 6 6 hi hi C1 D C ; i D 1; 2; : : : ; n 1 (1.69) 3 3where xi xi 1 D hi and xi C1 xi D hi C1 . The ﬁrst and last diagonal entry areM00 D h1 =3 and Mnn D hn =3, respectively, since the hat functions 0 and n areonly half. Continuing with the subdiagonal entries Mi C1 i , still using Simpson’s formula,we have
16. 16. 16 1 Piecewise Polynomial Approximation in 1DFig. 1.9 Illustration of the i−1 ihat functions i 1 and i and 1their support x xi−2 xi−1 xi xi+1 Z Mi C1 i D i i C1 dx (1.70) I Z xi C1 D i i C1 dx (1.71) xi 1 0 C 4 . 1 /2 C 0 1 D 2 hi C1 (1.72) 6 hi C1 D ; i D 0; 1; : : : ; n (1.73) 6A similar calculation shows that the superdiagonal entries are Mi i C1 D hi C1 =6. Hence, the mass matrix takes the form 2h h 3 1 1 3 6 6 h1 h1 C h2 h2 7 66 7 6 3 3 6 7 6 h2 h2 C h3 h3 7 M D6 7 6 3 3 6 6 :: :: :: 7 (1.74) 6 : : : 7 6 hn 7 4 hn 6 1 hn 3 1 C hn 3 6 5 hn hn 6 3 From (1.74) it is evident that the global mass matrix M can be written as a sumof n simple matrices, viz., 2h 3 2 3 2 3 1 h1 3 6 6 h1 h1 7 6 h2 h2 7 6 7 66 3 7 6 7 6 7 6 7 6 3 6 h2 h2 7 6 7 6 7 6 7 6 7 M D6 7C6 6 3 7 C ::: C 6 7 (1.75) 6 7 6 7 6 7 6 7 6 7 6 hn 7 4 5 4 5 4 hn 3 6 5 hn hn 6 3 D M I1 C M I2 C : : : C M In (1.76)Each matrix M Ii , i D 1; 2 : : : ; n, is obtained by restricting integration to onesubinterval, or element, Ii and is therefore called a global element mass matrix.In practice, however, these matrices are never formed, since it sufﬁce to computetheir 2 2 blocks of non-zero entries. From the sum (1.75) we see that on each
17. 17. 1.5 Computer Implementation 17element I this small block takes the form Ä 1 21 M D I h (1.77) 6 12where h is the length of I . We refer to M I as the local element mass matrix. The summation of the element mass matrices into the global mass matrix iscalled assembling. The assembly process lies at the very heart of ﬁnite elementprogramming because it allows the forming of the mass matrix through the use of asingle loop over the elements. It also generalizes to higher dimensions. The following algorithm summarizes how to assemble the mass matrix M :Algorithm 2 Assembly of the mass matrix1: Allocate memory for the .n C 1/ .n C 1/ matrix M and initialize all matrix entries to zero.2: for i D 1; 2; : : : ; n do3: Compute the 2 2 local element mass matrix M I given by Ä 1 21 M D I h (1.78) 6 12 where h is the length of element Ii . I4: Add M11 to Mi i I5: Add M12 to Mi iC1 I6: Add M21 to MiC1i I7: Add M22 to MiC1iC18: end for The following MATLAB routine assembles the mass matrix.function M = MassAssembler1D(x)n = length(x)-1; % number of subintervalsM = zeros(n+1,n+1); % allocate mass matrixfor i = 1:n % loop over subintervals h = x(i+1) - x(i); % interval length M(i,i) = M(i,i) + h/3; % add h/3 to M(i,i) M(i,i+1) = M(i,i+1) + h/6; M(i+1,i) = M(i+1,i) + h/6; M(i+1,i+1) = M(i+1,i+1) + h/3;endInput to this routine is a vector x holding the node coordinates. Output is the globalmass matrix.
18. 18. 18 1 Piecewise Polynomial Approximation in 1D1.5.2 Assembly of the Load Vector RWe next calculate the load vector b. Because the entries bi D i f i dx depend onthe function f , we can not generally expect to calculate them exactly. However, wecan approximate entry bi using a quadrature rule. Using the Trapezoidal rule, forinstance, we have Z bi D f i dx (1.79) I Z xi C1 D f i dx (1.80) xi 1 Z xi Z xi C1 D f i dx C f i dx (1.81) xi 1 xi .f .xi 1 /i .xi 1 / C f .xi /i .xi //hi =2 (1.82) C .f .xi /i .xi / C f .xi C1 /i .xi C1 //hi C1 =2 (1.83) D .0 C f .xi //hi =2 C .f .xi / C 0/hi C1 =2 (1.84) D f .xi /.hi C hi C1 /=2 (1.85)The approximate load vector then takes the form 2 3 f .x0 /h1 =2 6 7 f .x1 /.h1 C h2 /=2 6 7 6 7 6 f .x2 /.h2 C h3 /=2 7 bD6 6 :7 7 (1.86) 6 : :7 6 7 4f .xn 1 /.hn 1 C hn /=25 f .xn /hn =2 Splitting b into a sum over the elements yields the n global element load vectorsb Ii 2 3 2 3 2 3 f .x0 / 6f .x /7 6f .x /7 6 7 6 1 7 6 1 7 6 7 6 7 6 7 6 7 bD6 7 h1 =2 C 6f .x2 /7 h2 =2 C : : : C 6 7 hn =2 (1.87) 6 7 6 7 6 7 4 5 4 5 4f .xn 1 /5 f .xn / D b I1 C b I2 C : : : C b In : (1.88)