Micromechanical modelling and simulation of functional paper materials

• Applicant: Professorin Dr.-Ing. Bai-Xiang Xu
• Subject Area: Synthesis and Properties of Functional Materials
• Term: since 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 405422877

The functionality of paper materials relies on the microstructural details of the paper material to be examined, which, in addition to the used fiber material, is a decisive factor for the functionalization and for the design with regard to specific applications. In the first funding period, a micromechanical model and cohesive finite element simulations have been successfully completed, as well as statistical analysis of the structure-property relation using machine learning from massive simulation results. In the proposed funding period, the chemical functionalizations that are the common focus of various sub-projects and their influence on the mechanical activation of the fibers, the fiber-fiber composite and finally the entire fiber network are to be integrated into the simulation models. The aim is also to investigate the micromechanical properties by means of adapted simulation models in cooperation with the experimentally oriented sub-projects as part of the continuation PAK962-2 in order to gain groundbreaking knowledge with regard to the planned functionalization, under both dry and wet conditions regarding the layout of the design space for functional papers. Furthermore, microscopic details of the paper materials are primarily of a stochastic nature. The apparently spatially statistical arrangement of the fibers and thus the pore structure, which on the one hand depends on the fiber material used and on the other hand is heavily dependent on the manufacturing process, can be examined for microscopic details thanks to modern graphic imaging processes. These methods offer a number of new possibilities to generate practical and most realistic geometry models, which is helpful for numerical simulation calculations. Statistical features can then be analyzed from these models or the model geometry can be obtained directly from the images in order to then carry out the simulation of the mechanical properties. The latter pursues the further aim of using advanced imaging methods to derive a one-to-one microstructure and thus to reconstruct a so-called “digital twin”. This should be used: a) to determine material parameters based on the experiments; b) to validate the simulation models; c) to create reconstruction of geometry models for the simulation; d) to derive statistical characteristics and then construct realistic fiber networks that are statistically evenly distributed. The complex structure-property relationship of the fiber networks through chemical modification is also examined using a sensitivity analysis based on machine learning that has been developed in the first period.

• DFG Programme: Research Grants