![]() Therefore, the use of an unstructured grid does not always guarantee more efficient computation. These grids can provide comparable accuracy as a structured grid, but they require similarly high node density in regions with high flow gradients. This type of grid generation provides much higher accuracy than Cartesian grid generation in CFD problems, as it will closely follow the surface of the curve along the boundary.Įxample showing a structured grid conforming to the surface of a jet turbine housing Unstructured Grid GenerationĪn unstructured grid is a tessellation across the surface and interior volume of the system, seen as triangles (in 2D problems) or tetrahedra (in 3D problems). The node arrangement in the grid follows the same shape as the boundary surface, effectively scaling its interpolated nodes into the interior of the system. Unlike a Cartesian grid in which the cells are always regularly shaped, a structured grid is warped to follow the boundaries of the system. Structured Grid GenerationĪ structured grid creates an arrangement of quad (2d) or brick (3d) grid cells that are arranged in a simple matrix-like structure. Because of these complications, an alternative grid approach is normally used in most systems. Another technique for handling curved surfaces is for the grid cells to be cut by the boundaries into non-cubical shapes. Systems with curved surfaces or slanted boundaries may require higher meshing density to ensure accuracy, which increases the computation time in the simulation. ![]() Cartesian grids are simple systems, with each cell being shaped like a cube or brick, that can provide very high accuracy as long as the geometry only consists of orthogonal surfaces. In this method, a rectangular grid is used to represent the boundary and interior of a system for use in a CFD simulation, with grid points constrained to planes aligned with the Cartesian axis system. This is the simplest type of grid generation technique. The five common geometries used in grid generation for CFD problems are detailed below. After the simulation converges to a solution, the results can be interpolated for the entire domain. When the generated mesh matches the system geometry, a coarser mesh can be used so that computational cost is reduced. The discretization resolution can also be tuned to the problem at hand to ensure the system can be solved with reasonable computational effort. An example would be compressible flow, where the density of the fluid varies in space and with flow rate. Why is meshing needed in CFD problems? In addition to converting complex differential equations into simpler arithmetic problems, discretization allows a simulation to account for changes in continuous physical properties across the solution domain. ![]() The technique that is chosen can then be used to define grid points along the surface (for 2D or boundary element method problems) or inside the volume (for 3D problems) of the system. Grid generation in CFD simulations involves choosing a mathematical technique to represent the arrangement and spacing between each node in the numerical grid for your system. Using the right grid generation tools, automation and templatization are put in place to ensure that the required expertise and time commitment can be minimal while still ensuring the utmost accuracy in your mesh. The grid that is selected for CFD simulations will define the accuracy and resolution of the simulation results, both of which will affect the computation time and level of detail in the results. Grid generation in CFD refers to a set of techniques for defining a numerical mesh throughout the system to be simulated. When generating meshes for these simulations, it’s your job to understand how discretization in a numerical model influences accuracy and what you’ll be able to observe in your results. Therefore, it’s important to have some knowledge of numerical methods as well as how they are executed in computational simulation. However, as a working engineer, you can’t work out every problem by hand. Numerical methods for solving partial differential equations are advanced topics not normally taught in undergraduate engineering classes. More advanced meshing techniques do not use Cartesian or structured grids, instead, they apply polynomial curves and interpolation schemes to generate the mesh and results. The grid generation method that is used in a problem will try to match the mesh to the geometry of the system being simulated. Mesh generation in CFD simulations plays the same role as meshing in finite element simulations, where discretization will determine the accuracy and computation time in the simulation.
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