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                        8 Numerical Grid Generation                                May 10, 2005  16:28










                        8.1 Introduction

                        As mentioned in Chapter 6, curvilinear grid generation for 2D domains involves
                        specification of functions

                                          x 1 = x 1 (ξ 1 ,ξ 2 ),  x 2 = x 2 (ξ 1 ,ξ 2 ),    (8.1)


                        where ξ 1 ,ξ 2 are curvilinear coordinates and x 1 , x 2 are Cartesian coordinates. These
                        two functions can be generated in two ways: (1) by algebraic specification or
                        (2) by differential specification.
                           Algebraic specification is typically employed in 1D problems but can also be
                        employed in 2D problems when the domain is simple (Section 8.2). For complex do-
                        mains, however, differential grid generation is preferred. In this type, functions (8.1)
                        are generated by solving differential equations with dependent variables x 1 and x 2 .
                        The differential equations can beof parabolic, hyperbolic, orelliptictype[81]. How-
                        ever, we shall consider the most commonly used elliptic grid generation technique
                        (Sections 8.3 and 8.4)
                           The unstructured meshes again can be generated in a variety of ways. Two types
                        will be considered: (1) generation by exploiting structuredness and (2) automatic
                        mesh generation (Section 8.5).




                        8.2 Algebraic Grid Generation

                        8.2.1 1D Domains

                        The objective of grid generation is to locate nodes such that they are closely spaced
                        in regions where the dependent variable   in the transport equations is expected to
                        have steep gradients and sparsely spaced in regions where the gradients are small.
                        This ensures that accurate solutions are economically obtained.



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