A parametric stochastic microstructure model in 3D is developed for the simulation of aggregate (active material) NMC particles manufactured with different values of production parameters and sintering temperatures which influence first of all the mean size of primary particles and porosity. The considered nanoporous aggregate particles are used in electrodes, since they show higher energy densities and lead to a better functionality in terms of electric conductivity . Such functional properties strongly depend on the 3D morphology of the nanopores, which, in turn are influenced by the underlying production parameters. The morphology of nanopores can be analyzed by means of 3D imaging, which is, however, expensive in costs and time. Note that, even if the production parameters remain unchanged, a rather large variation of porosity of the aggregate particles is observed and thus, a huge amount of 3D image data would be required to analyze the morphology of nanopores systematically. To deal with this problem, the stochastic microstructure model can be used to generate virtual, but realistic microstructures in short time, see Fig. 1, in order to investigate the relationship between production parameters and the 3D morphology of the nanopores. For example, varying the porosity and keeping all other model parameters fixed, a large database of virtual aggregate particles can be created in this way. The stochastic microstructure model is based on tools from stochastic geometry. To be more precise, the solid phase of aggregate particles is modeled by an excursion set of a certain class of so-called χ^2-fields , which allows us to include a structural gradient into the model. After having fitted the model parameters to image data, model validation is performed by comparing both, structural characteristics and effective electric conductivities of simulated and tomographic image data. Here, the effective electric conductivity is numerically simulated by a goal oriented adaptive cut-cell finite element approach. Thus, the presented approach allows also for an investigation of the relationship between production parameters and electric conductivity of individual aggregate particles. The values of effective electric conductivity of aggregate particles can further be used for a numerical up-scaling of a multiscale system, i.e., as an input for the computation of electric conductivity of systems of aggregate particles forming an electrode.
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