血流中血小板动力学建模：一种未解析离散元方法
Modelling platelet dynamics in blood flow: an unresolved DEM approach
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中文总结 AI 辅助
本研究提出介观尺度CFD-DEM血小板动力学模型，结合可变形红细胞描述，可低成本恢复形状驱动的血小板动力学，量化了血小板边缘化等特性及相关参数的依赖关系。
中文摘要 AI 辅助
血流计算模型始终面临两难选择：完全解析的基于细胞的方法虽能精准再现单个细胞的动力学，但在血管尺度上计算成本过高；连续介质模型虽可扩展，却无法捕捉颗粒运动。我们提出一种未解析的介观尺度计算流体动力学-离散元方法（CFD-DEM）血小板动力学模型，该模型弥合了上述差距，结合现有未解析的可变形红细胞描述，更接近可扩展的全血模型。该模型基于开源OpenFOAM-LIGGGHTS耦合实现，将血小板表示为刚性扁球形颗粒，通过依赖取向的阻力、升力和流体动力扭矩闭合关系推进。首先针对直径100-200μm的圆柱形微血管、壁面剪切率γ̇=150-1650s⁻¹、红细胞压积Ht=10-20%的情况，与解析模拟和实验进行验证，随后用于表征血小板边缘化现象。模型量化了扩散系数、CFL形成及其对剪切率、Ht和通道尺寸的依赖性：血小板扩散率随血管尺寸和剪切率增大而增长，对Ht则不敏感；而CFL厚度随剪切率增大而增加，随Ht增大而变薄。进一步研究表明，即使在介观尺度，模型仍对血小板形状敏感：扁球形血小板比球形替代物边缘化更快，扩散速率快近一个数量级，虽达到的稳态分布相当，但时间路径显著不同。这些结果共同证明，以仅为完全解析方法的一小部分成本，即可恢复形状驱动的血小板动力学。
英文摘要
Computational models of blood flow are caught between fully resolved cell-based methods, which faithfully reproduce the dynamics of individual cells but are computationally prohibitive at vessel scale, and continuum models, scalable yet blind to particle motion. We present an unresolved, mesoscale computational fluid dynamics-discrete element method (CFD-DEM) model of platelet dynamics that bridges this gap and, coupled with an existing unresolved description of deformable red blood cells, moves closer to a scalable model of whole blood. Within this framework, implemented on the open source OpenFOAM-LIGGGHTS coupling, platelets are represented as rigid oblate particles advanced by orientation dependent drag, lift and hydrodynamic torque closures. The model is first validated against resolved simulations and experiments in cylindrical microvessels of diameter $100-200\,μm$, across wall shear rates $\dotγ=150-1650\,s^{-1}$ and hematocrit Ht $=10-20\%$ then used to characterize platelet margination. The model quantifies diffusion coefficient, CFL formation and their dependence on shear rate, Ht and channel size. In particular, platelet diffusivity grows with vessel size and shear rate, while remaining insensitive to Ht, whereas CFL thickens with shear rate and thins with Ht. We further show that even at this mesoscale level, the model remains sensitive to platelet shape: oblate platelets marginate faster and diffuse nearly an order of magnitude more than their spherical surrogates, reaching a comparable steady state distribution but along markedly different temporal paths. Together these results demonstrate that shape driven platelet dynamics can be recovered at a fraction of the cost of fully resolved methods.
