Use the GrainFind module to identify individual grains in structures where the grain boundaries are previously unknown. For each identified grain, an individual best-fit shape and its orientation in the structure is computed. In this way, simulations on the structure are possible which were impossible before, such as simulations of mechanical properties which depend on grain orientation. Furthermore, the structural information gained can be used to generate similar structures using the GrainGeo module – so-called digital twins of the structure can be modeled.

Possible applications are in the area of electrochemistry, where grains in battery electrodes can be identified, or in Digital Rock Physics, where information about individual grains fosters a thorough understanding of the rock structure. GrainFind can also be used to characterize particles in particle filtration applications.

For successful GrainFind runs, the parameters for the module must be chosen carefully. The built-in default values cannot be set to deliver the best possible results for all applications. Best practice is to carry out a parameter study for the solver parameters at the beginning of the analysis.

Additional to the grain identification, it is also possible to Identify Binder (AI) using machine learning technologies. Please note that for this feature a special license is required.

Moreover, the grain size distribution can be estimated through the Estimate Grain Diameters command.