Micromechanical modelling and simulation of functional paper materials

纸张材料的功能取决于要检查的纸张材料的微观结构细节，除了所用的纤维材料外，这是功能化和特定应用设计的决定性因素。在第一个资助期，成功地完成了细观力学模型和粘结有限元模拟，并利用机器学习从大量模拟结果中对结构-性能关系进行了统计分析。在拟议的资助期内，各个子项目共同关注的化学功能化及其对纤维、纤维-纤维复合材料乃至整个纤维网络的机械活化的影响将被整合到模拟模型中。其目的也是通过与作为延续PAK962-2的以实验为导向的子项目合作的适当的模拟模型来调查微观机械特性，以便获得关于计划中的功能化的开创性知识，在干燥和潮湿条件下关于功能纸设计空间的布局。此外，纸张材料的微观细节主要具有随机性。利用现代图形成像技术，可以检查纤维的表面空间统计排列以及由此而来的孔结构，其一方面取决于所使用的纤维材料，另一方面严重依赖于制造工艺，以获得微观细节。这些方法提供了许多新的可能性来生成实用和最逼真的几何模型，这对数值模拟计算是有帮助的。然后，可以从这些模型中分析统计特征，或者可以直接从图像中获得模型几何形状，以便随后执行机械性能的模拟。后者追求的进一步目标是使用先进的成像方法来获得一对一的微结构，从而重建所谓的“数字双胞胎”。这应该用于：a)基于实验确定材料参数；b)验证模拟模型；c)为模拟创建几何模型的重建；d)推导统计特性，然后构建统计上均匀分布的真实光纤网络。利用第一阶段发展的基于机器学习的灵敏度分析，还研究了经过化学修饰的纤维网络的复杂结构-性能关系。

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.