AI 中文总结
研究刚性聚集体悬浮液动力学和流变学，提出快速斯托克斯动力学框架，扩展公式到多珠刚体，开发预处理器，经多验证准确捕捉行为，不同系统尺寸下有不同缩放特性，实现准确可扩展模拟。
AI 中文摘要
我们提出了一个用于刚性聚集体悬浮液动力学和流变学的快速斯托克斯动力学（FSD）框架。该方法将Fiore和Swan（2019）的球体级公式扩展到多珠刚体。通过几何约束隐式强制刚性，实现稳定高效的时间积分。我们为所得鞍点系统开发了块三角分解预处理器。该方法结合了远场迁移率的近似逆和舒尔补的块对角近似，通过LU分解实现每个聚集体子块的独立求逆。该方法作为HOOMD-blue软件套件的开源插件实现，并针对基准问题进行了验证，包括剪切流中的双峰动力学、对沉降、布朗扩散以及稀溶液和结构化体系中的悬浮液流变学，准确捕捉了确定性和随机行为。该框架还针对炭黑浆料的实验流变学进行了验证，通过增强润滑明确考虑了范德华内聚力、赫兹接触和切向摩擦。模拟准确再现了剪切变稀和高剪切粘性区域。该方法在小系统尺寸下具有良好的GPU扩展性，在达到饱和之前每个珠子的运行时间减少。与恒定的埃瓦尔德分裂相比，尺寸依赖的埃瓦尔德分裂参数在低体积分数下加速了模拟，实现了高达一个数量级 的加速。对于较大的系统，恒定的埃瓦尔德分裂产生与粒子数成线性比例的缩放，而尺寸依赖的选择由于远场成本增加导致二次比例缩放。总体而言，所提出的框架能够在斯托克斯流中对刚性聚集体悬浮液进行准确且可扩展的模拟。
英文摘要
We present a fast Stokesian dynamics (FSD) framework for the dynamics and rheology of suspensions of rigid aggregates. The method extends the sphere-level formulation of Fiore and Swan (2019) to multi-bead rigid bodies. Rigidity is enforced implicitly through geometric constraints, enabling stable and efficient time integration. We develop a block-triangular factorization preconditioner for the resulting saddle-point system. The approach combines an approximate inverse of the far-field mobility with a block-diagonal approximation of the Schur complement, enabling independent inversion of each aggregate sub-block via LU decomposition. The method is implemented as an open-source plugin for the HOOMD-blue software suite, and validated against benchmark problems, including doublet dynamics in shear flow, pair sedimentation, Brownian diffusion, and suspension rheology across dilute and structured regimes, accurately capturing both deterministic and stochastic behavior. The framework is further validated against experimental rheology of carbon black slurries, explicitly accounting for van der Waals cohesion, Hertzian contact, and tangential friction via enhanced lubrication. The simulations accurately reproduce the shear-thinning and high-shear viscous regimes. The method exhibits favorable GPU scaling for small system sizes, with decreasing runtime per bead prior saturation. A size-dependent Ewald splitting parameter accelerates simulations at low volume fractions, yielding up to an order-of-magnitude speedup compared to constant Ewald splitting. For larger systems, a constant Ewald splitting produces linear scaling with particle number, whereas the size-dependent choice leads to quadratic scaling due to increased far-field cost. Overall, the proposed framework enables accurate and scalable simulation of rigid aggregate suspensions in Stokes flow.
Comments33 pages, 9 figures (including 1 supplemental figure)
