Identification of the mechanical behavior and coupling with improved internal structural analysis of frozen particle-fluid-systems

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Frozen particle-fluid systems (PFS) can be considered as particle-reinforced composite materials and are frequently encountered in both natural and technical systems. When subjected to mechanical loading at low temperatures for water-based fluids within the PFS, a phase transition occurs, leading to changes in their mechanical behavior and associated properties. For instance, soil stability is critical in the Arctic for construction projects, and artificial ground freezing (AGF) is commonly used to stabilize soil for mining and tunneling projects. Similarly, materials stored in silos at low temperatures or subjected to cryogenic grinding are exposed to changes in their mechanical properties. Therefore, it is important to investigate frozen PFS using in-situ methods to understand their characteristic properties and behavior under realistic conditions. Currently, different approaches are used to simulate frozen PFS, including finite element method (FEM) simulations and discrete element method (DEM) simulations with an alternate contact model. However, these approaches have limitations, such as higher computational costs, problems with discretization, and an inability to precisely describe the behavior of complex materials. To overcome these limitations, this project proposes using the Bonded Particle Model (BPM), which extends the DEM by connecting particles with bridges, forming agglomerates. A newly developed solid bond model considers creep behavior to describe the mechanics of frozen PFS in relation to different temperatures, strain rates, and volumetric ice contents. Micro-computed tomography (Micro-CT) measurements will also be carried out to generate the internal structure of frozen PFS based on different volumetric ice contents and particles. This non-destructive three-dimensional imaging of the internal structure with a high spatial resolution of the slice images using X-rays will be used. Using an in-situ Micro-CT device, a miniaturized uniaxial compression test apparatus will be developed to capture the freezing process with coupled mechanical loading in real time. Artificial neural networks (ANNs) will be used to calibrate the DEM-BPM material parameters and predict the mechanical properties of the agglomerate during freezing and loading. The computational time necessary for predicting the mechanical properties of agglomerates can be significantly reduced through the proposed method. For this purpose, a database consisting of thousands of simulations will be created and expanded to correlate with additional scenarios and realistic experimental data, which will further enhance the predictive capabilities of this tool.