通过微断层扫描重建的多孔单体实现鲁棒的孔隙解析CFD：从数字化到流体预测
Robust pore-resolved CFD through porous monoliths reconstructed by micro-computed tomography: From digitization to flow prediction
AI总结：
本研究通过微断层扫描重建多孔单体，利用改进的CFD方法实现鲁棒的孔隙解析模拟，揭示孔隙结构对压力分布的影响，为复杂多孔介质的数字化设计提供新方法。
摘要：
多孔介质在能量存储与转换、催化、生物力学、水文地质以及许多其他领域中普遍存在。这些材料具有高比表面积，其复杂的通道可以限制和引导流体流动。然而，由于多孔介质的复杂结构，为特定应用优化设计参数仍然具有挑战性。孔隙解析CFD揭示了其结构对流动特性的影响，但受到为如此复杂几何结构生成网格算法性能的限制。为缓解这一问题，我们使用一种尖锐浸没边界方法，该方法能够在大规模并行有限元框架内使用笛卡尔非符合网格。该方法保持了方案的收敛阶数，并允许自适应网格细化（AMR）。我们引入了一种基于径向基函数的固体表示方法，使得通过复杂几何结构精确求解流动成为可能。我们通过制造解方法验证了该方法。我们通过压力降测量验证了该方法，利用X射线计算微断层扫描数字化的多孔硅单体进行测试，孔隙雷诺数可达到30。使用分布在8000个核心上的2亿个单元网格进行模拟，这将需要没有AMR的情况下16倍更多的单元。结果表明，孔隙网络结构是描述压力演变的主要因素，而在这一尺度上，优先通道占主导地位。在本工作中，我们展示了多孔单体孔隙解析模拟的鲁棒且高效的流程。本工作连接了亚毫米级流动与宏观性质，这将通过使用基于物理的复杂多孔介质数字双胞胎来设计和优化过程打开大门。
英文摘要：
Porous media are ubiquitous in energy storage and conversion, catalysis, biomechanics, hydrogeology, as well as many other fields. These materials possess high surface-to-volume ratios and their complex channels can restrict and guide the flow. However, optimizing design parameters for specific applications remains challenging due to the intricate structure of porous media. Pore-resolved CFD reveals the effects of their structure on flow characteristics, but is limited by the performance of mesh generation algorithms for such complex geometries. To alleviate this issue, we use a sharp immersed boundary method which enables usage of Cartesian, non-conformal grids, within a massively parallel finite element framework. This method preserves the order convergence of the scheme and allows for adaptive mesh refinement (AMR). We introduce a radial basis function-based representation of solids that allows to solve the flow through complex geometries with precision. We verify the method using the method of manufactured solutions. We validate it using pressure drop measurements through porous silicone monoliths digitized by X-ray computed microtomography, for pore Reynolds numbers up to 30. Simulations are conducted using grids of 200M cells distributed over 8k cores, which would require 16 times more cells without AMR. Results reveal that pore network structure is the principal factor describing pressure evolution and that preferential channels are dominant at this scale. In this work, we demonstrate a robust and efficient workflow for pore-resolved simulations of porous monoliths. This work bridges the gap between sub-millimetric flow and macroscopic properties, which will open the door to design and optimize processes through the usage of physics-based digital twins of complex porous media.
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