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

冻结颗粒-流体系统（PFS）可以被认为是颗粒增强复合材料，在自然和技术系统中经常遇到。当PFS内的水基流体在低温下受到机械载荷时，会发生相变，导致其机械行为和相关性能发生变化。例如，在北极地区，土壤稳定性对建筑项目至关重要，人工地面冻结（AGF）通常用于稳定采矿和隧道工程的土壤。同样，在低温下储存在筒仓中或经受低温研磨的材料，其机械性能也会发生变化。因此，利用原位方法研究冻结PFS，了解其在现实条件下的特性和行为具有重要意义。目前，冻结PFS的模拟方法有有限元法（FEM）和离散元法（DEM）两种。然而，这些方法有局限性，例如较高的计算成本，离散化问题，以及无法精确描述复杂材料的行为。为了克服这些限制，本项目提出使用结合粒子模型（BPM），该模型通过将粒子与桥连接起来，形成团聚体来扩展DEM。一种新开发的固体粘结模型考虑蠕变行为来描述冻结PFS的力学与不同温度、应变速率和体积冰含量的关系。微计算机断层扫描（Micro-CT）测量也将进行，以生成基于不同体积冰含量和颗粒的冻结PFS的内部结构。这种内部结构的非破坏性三维成像具有高空间分辨率的切片图像使用x射线将被使用。利用原位Micro-CT装置，研制了一种小型单轴压缩试验装置，以实时捕捉耦合机械载荷下的冻结过程。人工神经网络（ann）将用于校准DEM-BPM材料参数，并预测团聚体在冻结和加载过程中的力学性能。通过该方法可以显著减少预测团聚体力学性能所需的计算时间。为此目的，将创建一个由数千个模拟组成的数据库，并将其扩展为与其他情景和实际实验数据相关联，这将进一步增强该工具的预测能力。

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.