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    Understanding of mechanical deformations in soft, porous particles under consideration of solvent-matrix interactions: experiments and coarse-grained DEM simulations
    This project aims to investigate the impact of mechanical stresses on soft, porous particles during their production in packed beds. Aerogels, known for their high porosity, low density, and large surface area, are used as representative porous systems. Their production involves several extraction steps, during which the porous matrix is exposed to different solvent mixtures with varying concentration gradients. Consequently, the mechanical and structural properties of the particles undergo significant changes, which will be experimentally investigated. In addition to experimental studies, coarse-grained Discrete Element Method (DEM) simulations will be used to explore mechanical deformations during aerogel formation, particularly under conditions that cannot be easily accessed through experiments, such as those involving elevated pressure. DEM simulations will also be further developed to include the influence of solvent-matrix interactions on the mechanical behavior of gels and their response to mechanical stress. The research will begin by analyzing individual particles and gradually scale up to packed beds of varying sizes. The focus will then shift to dynamic processes during solvent exchange and supercritical drying, where DEM simulations will be integrated with Computational Fluid Dynamics (CFD) simulations. The ultimate goal is to predict the mechanical behavior of the particles and define process conditions that maintain the structural integrity and desired properties of the particles during large-scale production
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    Emmy Noether: Computational Soft Material Mechanics Intelligence
    Polymer materials are essential constituents not only of advanced engineered systems but also of everyday products like food, clothes, drugs and consumables, and also all biological organisms consist to a large extent of soft materials. Modeling the mechanical behavior of such materials has proven particularly challenging because they often exhibit complex types of nonlinearity, anisotropy and inelasticity. Descriptive modeling of soft materials therefore still often requires a large amount of expert knowledge. An even greater challenge are predictions how the behavior of soft materials can be tuned by subtle changes of their microstructure and processing. So far such predictions are possible only to a very limited extend and again with considerable expert knowledge. This project aims at leveraging most recent advances in physics-informed machine learning to automate both the descriptive and predictive modeling of soft materials to an unprecedented degree. Thereby, it will create a powerful basis for the future virtual design of soft materials. Moreover, by automating the pipeline from probing to describing to predicting the mechanical behavior of soft materials, the methods developed in in this project will enable robots to examine and handle also unknown and complex soft materials with an unprecedented degree of autonomy. This can be transformative for biomedical and biomechanical robotics, where, for example, surgical robots are facing in each patient or individual a different behavior of soft tissue and have to learn to probe and handle it. To showcase such benefits for medical robotics, this project will not only develop a toolbox of machine-learning based methods for describing and predicting the mechanical behavior of soft materials in a largely automated way but it will also illustrate their application in a simple robot demonstrator in the laboratory.
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