Solid mechanics simulations are mainly based on the finite element method that adopts the Lagrangian description—in which computational mesh is carved in the material body and deforms with the body. In post-processing, analysts naturally form their interpretation from results at Lagrangian mesh, for example, displacements and reaction forces at nodes, stresses and strains at integration points, nodes, or centroids of elements. Those results are attached to specific material points through Lagrangian mesh and are called results in Lagrangian representation. Investigating Lagrangian results satisfies analysts’ needs in solid mechanics problems most of time.
But in some dynamic or quasi-static problems in which solid material behaves like a flow, we may occasionally be more curious about results at some specific spatial locations rather than material points. For example, in a roll-to-roll process where a web travels along a designed path line for some functional processing from a unwinding roll to a rewinding roll, engineers may specifically be interested in the tension, traveling speed, temperature, or other physical quantities of web at some spatial locations such as some rollers since they may serve as parameters of control system or relate to product quality. Results of material expressed at spatial points are called results in Eulerian representation.
Abaqus has the ‘probe’ function in the visualization module which enables users to approximately get results at spatial locations through Lagrangian mesh in individual solution frame. However, it does not provide a convenient way of extracting the time history of results at spatial locations throughout the whole simulation. Yet, the time history of Eulerian results in these simulation is more valuable as it reveals when and how the steady state is achieved, which some times exposes physical information about the characteristics of the problem.
This project develops two general-purpose Abaqus Python scripts based on two methods that can automatically extract the time history of field outputs at some spatial location of interest. The developer wishes the scripts can save some labor and willpower of analysts so their energy can be reserved for pure problem solving rather than tedious external processing.
Implementation details refer to this blog post.