Page 103 - Basic Structured Grid Generation
P. 103

92  Basic Structured Grid Generation

                          In all the above approaches a matrix equation has to be solved for the values of


                        y , y ,...,y      . The matrix A is tridiagonal and diagonally dominant in each case,
                         1   2      n−1
                        and possesses a large degree of sparseness. There are standard numerical methods of
                        solving such a system, such as the Thomas Algorithm (see Section 5.2). It is then
                        straightforward to calculate y and y , and we are then in a position to calculate the


                                                 0      n
                        interpolating cubic spline itself from eqn (4.50).
                          The same remarks hold for the following.
                        Method 5 Here we may consider a mixture of end-conditions of the type considered
                        in the previous four approaches. For example, one might have a natural spline at the
                        left-hand end (Method 1) with a specified slope at the right-hand end (Method 3). In
                        this case we simply have to take the matrix A as in eqn (4.54) but change the last row
                        in accordance with eqn (4.66). Moreover, the column vector on the right-hand side of
                        the matrix equation must agree with eqn (4.53), except that the last entry will have to
                        agree with the last entry in eqn (4.64). Other combinations of end-conditions can be
                        handled in a similar way.



                           4.3 Multidirectional interpolation and TFI

                        4.3.1 Projectors and bilinear mapping in two dimensions


                        Suppose there exists a transformation r = r(ξ, η) (or x = x(ξ, η), y = y(ξ, η))
                        which maps the unit square 0 <ξ < 1, 0 <η < 1 onto the interior of the region
                        ABDC in the xy (physical) plane (Fig. 4.12), such that the edges ξ = 0, 1 map to
                        the boundaries AB, CD, respectively, which we can formulate as r(0,η) and r(1,η),
                        the boundaries AC, BD being similarly given by r(ξ, 0), r(ξ, 1). We can write down
                        another transformation P ξ , called a projector, which maps points in computational
                        space to points (or position vectors) in physical space, defined by

                                             P ξ (ξ, η) = (1 − ξ)r(0,η) + ξr(1,η).         (4.67)
                          As we have seen in Section 4.2.1 this maps the unit square in the ξη plane onto the
                        region shown in Fig. 4.8, in which the boundaries AC, BD are replaced by straight
                        lines. The sides ξ = 0, 1 are mapped onto AB, CD respectively, and the sides η =
                        0, 1 are mapped onto the straight lines AC, BD. Furthermore, co-ordinate lines of
                        constant η are mapped into straight lines rather than co-ordinate curves in the physical
                        plane.

                                           h                    y
                                           1                       B        D
                                                                            C
                                           0                         A
                                                  1    x        O
                                                                             x
                        Fig. 4.12 Mapping unit square onto curved four-sided figure.
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