Microstructure-informed probability-driven point-particle model for hydrodynamic forces and torques in particle-laden flows
Microstructure-informed probability-driven point-particle model for hydrodynamic forces and torques in particle-laden flows
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DOI：
10.1017/jfm.2020.453
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发表时间：
2020-08
影响因子：
3.7
通讯作者：
Arman Seyed-Ahmadi;A. Wachs
中科院分区：
文献类型：
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作者：
Arman Seyed-Ahmadi;A. Wachs
摘要：我们提出了一种新颖的确定性模型，能够预测随机分布的单分散球体固定床上的粒子间力和扭矩波动。首先，我们通过对中等惯性状态下的静止球体阵列进行粒子解析直接数值模拟 (PR-DNS) 来生成数据集，雷诺数范围为 $2 \leq \textit {Re} \leq 150$，固体体积分数范围为 $0.1 \leq \phi \leq 0.4$。我们的模型利用的关键思想是，虽然每个粒子周围的相邻粒子的排列是均匀且随机的，但将参考球上施加的调节力或扭矩调节到特定范围的值会导致相邻粒子出现明显不均匀的分布。基于概率论证，我们利用从 PR-DNS 中提取的统计信息来构建力/扭矩条件概率分布图，最终用作回归的基函数。考虑到周围粒子的位置作为模型的输入，我们的结果表明，在最佳情况下，当前的概率驱动框架能够预测高达 85% 的实际观察到的力和扭矩变化。由于在欧拉-拉格朗日 (EL) 模拟中已知每个粒子的精确位置，因此我们的模型将能够相当好地估计未解析的子网格力和扭矩波动，从而通过改进的相间耦合显着提高 EL 模拟的保真度。
Abstract We present a novel deterministic model that is capable of predicting particle-to-particle force and torque fluctuations in a fixed bed of randomly distributed monodisperse spheres. First, we generate our dataset by performing particle-resolved direct numerical simulations (PR-DNS) of arrays of stationary spheres in moderately inertial regimes with a Reynolds number range of $2 \leq \textit {Re} \leq 150$ and a solid volume fraction range of $0.1 \leq \phi \leq 0.4$. The key idea exploited by our model is that, while the arrangement of neighbours around each particle is uniform and random, conditioning forces or torques exerted on a reference sphere to specific ranges of values results in the emergence of significantly non-uniform distributions of neighbouring particles. Based on probabilistic arguments, we take advantage of the statistical information extracted from PR-DNS to construct force/torque-conditioned probability distribution maps, which are ultimately used as basis functions for regression. Given the locations of surrounding particles as input to the model, our results demonstrate that the present probability-driven framework is capable of predicting up to 85 % of the actual observed force and torque variation in the best cases. Since the precise location of each particle is known in an Eulerian–Lagrangian (EL) simulation, our model would be able to estimate the unresolved subgrid force and torque fluctuations reasonably well, and thereby considerably enhance the fidelity of EL simulations via improved interphase coupling.