| Literature DB >> 32783371 |
Rongliang Chen1,2, Bokai Wu1, Zaiheng Cheng1, Wen-Shin Shiu1, Jia Liu1, Liping Liu3, Yongjun Wang3, Xinhong Wang4, Xiao-Chuan Cai5.
Abstract
Numerical simulation of blood flows in patient-specific arteries can be useful for the understanding of vascular diseases, as well as for surgery planning. In this paper, we simulate blood flows in the full cerebral artery of stroke patients. To accurately resolve the flow in this rather complex geometry with stenosis is challenging and it is also important to obtain the results in a short amount of computing time so that the simulation can be used in pre- and/or post-surgery planning. For this purpose, we introduce a highly scalable, parallel non-nested two-level domain decomposition method for the three-dimensional unsteady incompressible Navier-Stokes equations with an impedance outlet boundary condition. The problem is discretized with a stabilized finite element method on unstructured meshes in space and a fully implicit method in time, and the large nonlinear systems are solved by a preconditioned parallel Newton-Krylov method with a two-level Schwarz method. The key component of the method is a non-nested coarse problem solved using a subset of processor cores and its solution is interpolated to the fine space using radial basis functions. To validate and verify the proposed algorithm and its highly parallel implementation, we consider a case with available clinical data and show that the computed result matches with the measured data. Further numerical experiments indicate that the proposed method works well for realistic geometry and parameters of a full size cerebral artery of an adult stroke patient on a supercomputers with thousands of processor cores.Entities:
Keywords: domain decomposition method; finite element on unstructured meshes; full cerebral artery with stenosis; fully implicit method; non-nested coarse space; parallel processing
Year: 2020 PMID: 32783371 DOI: 10.1002/cnm.3392
Source DB: PubMed Journal: Int J Numer Method Biomed Eng ISSN: 2040-7939 Impact factor: 2.747