Easing The CFD Engineer’s Life With Automated Meshing

Simplifying import and preparation of geometry as well as surface and volume meshing.


Mesh generation is where the user’s expertise and ingenuity can influence the convergence and accuracy of a computational fluid dynamics (CFD) solution by selecting mesh type, topology, and cell quality. But with the rush to automate mesh generation, will the control be ripped out of the user’s hand, or will a valuable engineering skill be lost?

The extent to which meshing can be automated

According to the Oxford Dictionary, the definition of automatic is ‘having controls that work without needing a person to operate them,’ and this is in sync with NASA’s CFD Vision 2030 Study, where the author states that “ultimately the mesh generation process should be invisible to the CFD user.”

However, applying the literal definition of “automatic” to technology is not recommended or feasible. Automatic methods are often plagued by the inevitable dead ends where 90 percent of the mesh is generated automatically. Still, the last 10 percent is virtually impossible to complete or consumes days or weeks.

Creating an automated mesh generator is a much more tractable task, especially when automation is coupled with manual techniques that serve as backups when automation goes astray. Let us explore how automation has been implemented in Fidelity Pointwise, starting with CAD model import.

Automated solid model assembly

Import and preparation of geometry is the bane of mesh generation. The main issue during the import and geometry clean-up is the gaps and overlaps between adjacent surfaces. These gaps and overlaps cause the mesher to see each surface as an isolated, disjoint piece rather than part of a whole geometry (figure 1). When you mesh sloppy CAD, there is no guarantee that component meshes will match across surface boundaries.

Fig. 1: This launch vehicle was imported from an IGES file. The colors represent individual surfaces with no relationship with each other (left); the entire launch vehicle geometry has been assembled automatically during import into a single topological solid model (right).

Fortunately, during CAD file import Fidelity Pointwise automatically assembles the surfaces from your CAD file (e.g., STEP, SolidWorks) into a topological solid (figure 1). The resulting solid model has neither gaps nor overlaps.

The significance of meshing a solid model is that when you mesh the model, all the component meshes – one per CAD surface – will knit together seamlessly while respecting the geometric intent, making the surface meshes immediately suitable for volume meshing.

On the other hand, when solid model assembly does not work fully or at all during import, Pointwise gives the user the ability to manually perform the assembly operation with full control over tolerances and the surfaces to assemble.

Automated surface meshing

Surface meshing can often represent a challenge. Ensuring points are properly projected to complex CAD geometry and dealing with CAD surface artifacts like slivers or overlaps often forces one to resort to manual techniques.

With a single click, Fidelity Pointwise can mesh all the CAD surfaces and fully connect them. It also offers another automated tool for recovering from CAD or engineering geometry. The solid model can be subdivided into topological entities called quilts during automatic assembly using a single angular tolerance. As shown in figure 2, the quilts represent the launch vehicle’s fuselage, tail, upper and lower wings, and wing tip. This engineering geometry better reflects the goals of the CFD simulation.

Fig. 2: Surfaces in the CAD model have been assembled into quilts, regions on which a single mesh will be applied (left); automated surface meshing of the engineering geometry, automatically recovered from the CAD file as quilts (right).

Fidelity Pointwise offers a full suite of model and quilt assembly tools to tune the engineering topology to your requirements and an even more robust suite of meshing attributes. These can all be applied manually at your discretion.

Automated volume meshing

Fidelity Pointwise’s structured and unstructured meshing techniques are applied automatically when meshes are created (with user-specified default parameters) and are adjusted automatically whenever the mesh topology is edited. Further, the Rules command in Pointwise proactively monitors mesh quality. The user has the flexibility to create a rule that sets a limit on any supported mesh metric. For a more detailed view of the mesh quality, a full suite of mesh diagnostic and visualization tools within Pointwise can be applied at any time to any meshes.

Fig. 3: This T-Rex mesh generates near-wall hex layers for boundary layer resolution and transitions to an isotropic tetrahedral mesh in the far field.

The structured and unstructured meshing techniques described above work closely with a volume mesh topology. But hybrid meshes such as those generated by Pointwise’s anisotropic tetrahedral extrusion (T-Rex) have less reliance on volume mesh topology and thus can be generated much more automatically. The T-Rex technique automatically accounts for the following:

  • Quality of the extruded cells according to user-specified metrics.
  • Collision with other extruded layers.
  • Transition to the isotropic far-field mesh.
  • Adjacency with symmetry planes and other meshes.

Automated meshing using scripted templates

The best approach for truly automatic meshing is to target specific applications such as airfoils, wings, exhaust nozzles, blade passages, etc. Of course, no one relishes writing a complete meshing application from scratch for each of these applications, so the key to success is having a common core mesher that is extensible.

Fortunately, Pointwise’s scripting language, Glyph, was explicitly designed for extending Pointwise via macros (to encapsulate frequently performed sets of operations), extensions (to create new commands that are not part of the baseline code), and templates (complete meshing applications).


  1. Slotnick, Jeffrey, et al., “CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences,” NASA CR-2014-218178, http://ntrs.nasa.gov/search.jsp?R=20140003093.
  2. Dannehoffer, John, “Surface Parametrization of 3D Configurations Using Quilts,” AIAA-2005-5238, June 2005.

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