Generation of Computational Mesh for CFPD Simulations
Generation of a quality mesh is of extreme importance for obtaining reliable computational solutions. A good quality mesh improves numerical stability, and increases the likelihood of attaining a reliable solution. A mesh can be viewed as a number of smal
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Generation of Computational Mesh for CFPD Simulations
4.1
Introduction
Generation of a quality mesh is of extreme importance for obtaining reliable computational solutions. A good quality mesh improves numerical stability, and increases the likelihood of attaining a reliable solution. A mesh can be viewed as a number of smaller meshes or grid cells that overlays the entire domain geometry. In general the set of fundamental mathematical equations that represent the flow physics are applied to each cell. These equations, which calculate the changes in each cell within the domain, are subsequently solved to yield the corresponding discrete values of the flow-field variables such as the velocity, pressure, and temperature. In the computational fluid dynamics community, mesh generation is also referred to as grid generation, which has become a separate discipline in itself and remains a very active area of research and development. This chapter does not intend to elaborate on various methods of grid generation. There are many books dedicated to this subject alone, such as Thompson et al. (1985), Arcilla et al. (1991), and Lisekin et al. (1999). We also note that many existing commercial codes in the market have their own powerful, built-in mesh generators; a number of independent grid generation packages are given in Appendix A. Although, many of the meshgeneration packages are designed with very user-friendly interfaces, and are easy to utilize, proficient management of these software packages still relies on the reader’s aptitude to operate them. Generating a mesh is as much an art form as it is science. One has to decide on the arrangement of discrete points (nodes) throughout the computational domain, and the type of connections of each point, which leads to either the great success or utter failure of the numerical solution. In this chapter, guidelines and best practices are given for developing quality meshes. Furthermore the material presented will give the reader an introduction into mesh construction.
J. Tu et al., Computational Fluid and Particle Dynamics in the Human Respiratory System, Biological and Medical Physics, Biomedical Engineering DOI 10.1007/978-94-007-4488-2_4, © Springer Science+Business Media Dordrecht 2013
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4 Generation of Computational Mesh for CFPD Simulations
Fig. 4.1 Mesh topology hierarchy from lowest (left) to the highest (right). a Topology heirachy. b 2D and 3D mesh element types
4.1.1
Mesh Topology
A computational mesh topology has a hierarchical system whereby higher topology assumes the existence of the topologies beneath it (Fig. 4.1). For example, the creation of a volume cell automatically inherits the lower topologies (i.e. a volume cell contains face, line and vertex structures). For a 2D mesh, all nodes (discrete points) lie in a given plane with its resulting 2D mesh elements being quadrilaterals or triangles. For a 3D mesh, nodes are not constrained to a single plane with its resulting 3D mesh elements being hexahedra, tetrahedra (tets), square pyramids (pyra
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