TY - JOUR
T1 - Parallel-in-time simulation of biofluids
AU - Liu, Weifan
AU - Rostami, Minghao W.
N1 - Funding Information:
Liu and Rostami were supported, in part, by the National Science Foundation under award DMS-1818833 (PI: Rostami). Rostami was also supported, in part, by the Simons Foundation under award 527247 and by the Oak Ridge Associated Universities under a Ralph E. Powe Junior Faculty Enhancement Award. Access to a supercomputer was provided by the Extreme Science and Engineering Discovery Environment under startup allocation DMS-200009 (PI: Liu, co-PI: Rostami).
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - We extend Parareal, a parallel-in-time method that alternates between a serial sweep and a parallel sweep, to simulate the fluid flow around bio-inspired, dynamic structures over a period of time. Our main contributions include demonstrating the applicability of Parareal to the simulation of biofluids and developing novel solvers for the serial sweeps of Parareal. We propose to construct non-intrusive solvers by extrapolating a parametrized family of existing solvers. Compared to the existing solvers, they either allow the use of larger time steps, have a higher order of accuracy in time, or both. They are also straightforward to implement and parallelize. Numerical results show that when the number of biological structures is small or the number of computer cores employed is sufficiently large, the proposed variant of Parareal can achieve a significantly higher parallel speedup than the more commonly used spatial parallelization.
AB - We extend Parareal, a parallel-in-time method that alternates between a serial sweep and a parallel sweep, to simulate the fluid flow around bio-inspired, dynamic structures over a period of time. Our main contributions include demonstrating the applicability of Parareal to the simulation of biofluids and developing novel solvers for the serial sweeps of Parareal. We propose to construct non-intrusive solvers by extrapolating a parametrized family of existing solvers. Compared to the existing solvers, they either allow the use of larger time steps, have a higher order of accuracy in time, or both. They are also straightforward to implement and parallelize. Numerical results show that when the number of biological structures is small or the number of computer cores employed is sufficiently large, the proposed variant of Parareal can achieve a significantly higher parallel speedup than the more commonly used spatial parallelization.
KW - Biofluid
KW - Fluid-structure interaction
KW - Lagrangian extrapolation
KW - Method of regularized Stokeslets
KW - Parareal
KW - Richardson extrapolation
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U2 - 10.1016/j.jcp.2022.111366
DO - 10.1016/j.jcp.2022.111366
M3 - Article
AN - SCOPUS:85132241399
SN - 0021-9991
VL - 464
JO - Journal of Computational Physics
JF - Journal of Computational Physics
M1 - 111366
ER -