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Ability of a pore network model to predict fluid flow and drag in saturated granular materials

机译:孔隙网络模型预测饱和粒状物料中流体流动和阻力的能力

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The local flow field and seepage induced drag obtained from Pore Network Models (PNM) is compared to Immersed Boundary Method (IBM) simulations, for a range of linear graded and bimodal samples. PNM were generated using a weighted Delaunay Tessellation (DT), along with the Modified Delaunay Tessellation (MDT) which considers the merging of tetrahedral Delaunay cells. Two local conductivity models are compared in simulating fluid flow in the PNM. The local pressure field was very accurately captured, while the local flux (flow rate) exhibited more scatter and sensitivity to the choice of the local conductance model. PNM based on the MDT clearly provided a better correlation with the IBM. There was close similarity in the network shortest paths, indicating that the PNM captures dominant flow channels. Comparison of streamline profiles demonstrated that local pressure drops coincided with the pore constrictions. A rigorous validation was undertaken for the drag force calculated from the PNM by comparing with analytical solutions for ordered array of spheres. This method was subsequently applied to all samples, and the calculated force was compared with the IBM data. Linear graded samples were able to calculate the force with reasonable accuracy, while the bimodal samples exhibited slightly more scatter.
机译:对于一系列线性渐变和双峰样本,将从孔网络模型(PNM)获得的局部流场和渗流诱导阻力与沉浸边界方法(IBM)仿真进行了比较。 PNM使用加权Delaunay细分(DT)以及考虑了四面体Delaunay细胞融合的改良Delaunay细分(MDT)生成。在模拟PNM中的流体流动时,比较了两个局部电导率模型。可以非常精确地捕获局部压力场,而局部通量(流速)则表现出更大的分散性和对局部电导模型选择的敏感性。显然,基于MDT的PNM提供了与IBM更好的关联。网络最短路径存在相似性,这表明PNM捕获了主要的流动通道。流线轮廓的比较表明局部压降与孔隙收缩相吻合。通过与有序排列的球体的解析解进行比较,对从PNM计算出的阻力进行了严格的验证。随后将该方法应用于所有样品,并将计算出的力与IBM数据进行比较。线性分级的样本能够以合理的精度计算力,而双峰样本的散点略大。

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