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

JOURNAL OF COMPUTATIONAL PHYSICS

卷:358

Article

Smith, David J.¹ 

[1] Univ Birmingham, Sch Math, Birmingham B15 2TT, W Midlands, England

关键词: Stokes flow;    Regularized stokeslet;    Boundary integral;    Meshfree;   

DOI  :  10.1016/j.jcp.2017.12.008

来源: Elsevier

【 摘 要 】

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. Aheuristic 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 Nystrom 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. (C) 2017 The Author(s). Published by Elsevier Inc.

【 授权许可】

Free