An octree-based immersogeometric approach for modeling inertial migration of particles in channels
An octree-based immersogeometric approach for modeling inertial migration of particles in channels
复制标题
基于八叉树的浸没几何方法,用于模拟通道中粒子的惯性迁移
DOI:
10.1016/j.compfluid.2020.104764
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发表时间:
2021
影响因子:
2.8
通讯作者:
Ganapathysubramanian, Baskar
中科院分区:
文献类型:
--
作者:
Xu, Songzhe;Gao, Boshun;Lofquist, Alec;Fernando, Milinda;Hsu, Ming-Chen;Sundar, Hari;Ganapathysubramanian, Baskar
In this paper, we develop a scalable, adaptively refined, octree-based finite element approach with immersogeometric analysis to track inertial migration of particles in microchannels. Fluid physics is modeled using a residual-based variational multiscale method, and the particle movement is modeled as rigid body motion. A parallel, hierarchically refined octree mesh is employed as the background mesh, on which a variationally consistent immersogeometric formulation is adopted for tracking the particle motion in the fluid. Adaptations of immersogeometric analysis on an octree background mesh are developed to enable efficient searching of background element for a given surface quadrature point, as well as a distribution of surface quadrature points over processors to reduce memory overhead and better parallelize the surface assembly. An octree-based adaptive mesh refinement algorithm adapted to in-out test in the immersogeometric approach is also developed. The validation of our octree-based immersogeometric approach is carried out using a benchmark case of a sphere settling in quiescent fluid, with good agreement presented. In addition, good strong (and weak) scalability on supercomputing resources for this benchmark case up to 16,384 processes is demonstrated. The proposed method is further deployed for exploring particle migration in straight and converging-diverging channels. This example illustrates the potential of the octree-based immersogeometric approach for efficiently tracking particle motion in complex channel flows – a problem with a diverse array of applications.
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影响因子:
2.8
作者:
Jinhui Yan;Stephen Lin;Y. Bazilevs;G. Wagner
通讯作者:
Jinhui Yan;Stephen Lin;Y. Bazilevs;G. Wagner
影响因子:
--
作者:
C. Thieulot;P. Fullsack;J. Braun
通讯作者:
J. Braun
DOI:
10.1007/978-3-540-75757-3_115
发表时间:
2007-10
期刊:
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子:
--
作者:
H. Sundar;D. Shen;G. Biros;Chenyang Xu-;C. Davatzikos
通讯作者:
H. Sundar;D. Shen;G. Biros;Chenyang Xu-;C. Davatzikos
影响因子:
3.5
作者:
Fei Xu;Y. Bazilevs;M. Hsu
通讯作者:
M. Hsu
DOI:
10.1145/3330345.3330346
发表时间:
2019-06
期刊:
Proceedings of the ACM International Conference on Supercomputing
影响因子:
--
作者:
Milinda Fernando;D. Neilsen;E. Hirschmann;H. Sundar
通讯作者:
Milinda Fernando;D. Neilsen;E. Hirschmann;H. Sundar