A nearest-neighbour discretisation of the regularized stokeslet boundary integral equation

A nearest-neighbour discretisation of the regularized stokeslet boundary integral equation
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正则化斯托克斯勒边界积分方程的最近邻离散化

DOI:
10.1016/j.jcp.2017.12.008
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发表时间:
2018
影响因子:
4.1
通讯作者:
Smith D
Smith D
中科院分区:
物理与天体物理2区
文献类型:
--
作者:
Smith D

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正则化stokeslet方法因其概念简单、无网格性而广泛应用于生物流体动力学。这种简洁性带来了一定程度的计算费用和精度成本,因为用于离散未知表面牵引力的自由度通常比边界元方法所需的自由度要高得多。我们描述了一种基于最近邻插值的无网格方法,该方法显着减少了离散未知牵引力所需的自由度,增加了可以实际解决的问题范围,而不会使建模者的任务过度复杂化。最近邻技术针对浸入粘性流体的球体刚体运动的经典问题进行了测试,然后应用于计算由三个紧密间隔的非细长棒模拟的大分子结构的旋转扩散时间尺度的更复杂的生物物理问题。提出了一种启发式的方法,通过数值细化求出所需的力密度和正交点。提供了算法关键步骤的Matlab/GNU Octave代码,主要使用基本的线性代数运算,并在github上提供了完整的实现。与标准Nyström离散化相比,相对于正交离散化,通过对力离散化进行细化,可以获得更准确、更有效的结果:在提高精度的同时,成本降低了10倍以上。这种改进以最小的额外技术复杂性实现。然后讨论了未来发展算法的途径。
The method of regularized stokeslets is extensively used in biological fluid dynamics due to its conceptual simplicity and meshlessness. This simplicity carries a degree of cost in computational expense and accuracy because the number of degrees of freedom used to discretise the unknown surface traction is generally significantly higher than that required by boundary element methods. We describe a meshless method based on nearest-neighbour interpolation that significantly reduces the number of degrees of freedom required to discretise the unknown traction, increasing the range of problems that can be practically solved, without excessively complicating the task of the modeller. The nearest-neighbour technique is tested against the classical problem of rigid body motion of a sphere immersed in very viscous fluid, then applied to the more complex biophysical problem of calculating the rotational diffusion timescales of a macromolecular structure modelled by three closely-spaced non-slender rods. A heuristic for finding the required density of force and quadrature points by numerical refinement is suggested. Matlab/GNU Octave code for the key steps of the algorithm is provided, which predominantly use basic linear algebra operations, with a full implementation being provided on github. Compared with the standard Nyström discretisation, more accurate and substantially more efficient results can be obtained by de-refining the force discretisation relative to the quadrature discretisation: a cost reduction of over 10 times with improved accuracy is observed. This improvement comes at minimal additional technical complexity. Future avenues to develop the algorithm are then discussed.
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