As this presentation points out, “[t]he construction of a suitable refinement criterion represents the weakest point of most adaptive strategies“. While it’s still seen as a key requirement for CFD, it’s fair to say that grid adaptation has not lived up to its early promise. What if you could, without extensive programming, modify the refinement criteria? Work along with this series to try it yourself – and ask for help at any stage – starting with these videos on setting up our recommended Python IDE. This video is for Ubuntu, while this one is for Windows.

This 2016 case study outlines how the TecIO-MPI library was implemented in a CFD solver, and discusses the performance gains when writing SZL data files in parallel. Of particular interest is how MPI can help offset the cost of creating the SZPLT format, as compared to PLT. Also interesting is the point at which the effect of the number of cores tapers off.

Caelus is a derivative of OpenFOAM that is available under GPL. This case study describes the usage of Python to drive a process of mesh adaption with Caelus and Pointwise, using point-clouds. The Python script executes the entire process of mesh/CFD cycling. This includes the re-meshing, CFD simulations, and point cloud creation for further cycles. View the webinar to learn how the mesh adapts to the solutions for different examples.