A new class of massively parallel direction splitting for the incompressible Navier–Stokes equations
Type
ArticleAuthors
Guermond, J.L.Minev, P.D.
KAUST Grant Number
KUS-C1-016-04Date
2011-06Permanent link to this record
http://hdl.handle.net/10754/597336
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Show full item recordAbstract
We introduce in this paper a new direction splitting algorithm for solving the incompressible Navier-Stokes equations. The main originality of the method consists of using the operator (I-∂xx)(I-∂yy)(I-∂zz) for approximating the pressure correction instead of the Poisson operator as done in all the contemporary projection methods. The complexity of the proposed algorithm is significantly lower than that of projection methods, and it is shown the have the same stability properties as the Poisson-based pressure-correction techniques, either in standard or rotational form. The first-order (in time) version of the method is proved to have the same convergence properties as the classical first-order projection techniques. Numerical tests reveal that the second-order version of the method has the same convergence rate as its second-order projection counterpart as well. The method is suitable for parallel implementation and preliminary tests show excellent parallel performance on a distributed memory cluster of up to 1024 processors. The method has been validated on the three-dimensional lid-driven cavity flow using grids composed of up to 2×109 points. © 2011 Elsevier B.V.Citation
Guermond JL, Minev PD (2011) A new class of massively parallel direction splitting for the incompressible Navier–Stokes equations. Computer Methods in Applied Mechanics and Engineering 200: 2083–2093. Available: http://dx.doi.org/10.1016/j.cma.2011.02.007.Sponsors
This publication is based on work partially supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).The work of this author is also supported by fellowships from the Institute of Applied Mathematics and Computational Science and the Institute of Scientific Computing at Texas A&M University, and a Discovery grant of NSERC.Publisher
Elsevier BVae974a485f413a2113503eed53cd6c53
10.1016/j.cma.2011.02.007