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Parallel meshless EFG solution for fluid flow problems

机译:并行无网格EFG解决方案,解决流体流动问题

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This article deals with the parallel computing solution of fluid flow problems using a meshless element-free Galerkin (EFG) method. The EFG method utilizes moving least- square (MLS) approximants to approximate the unknown field variables. The MLS approximant consists of three components: a weight function, a basis function, and a set of nonconstant coefficients. A new parallel algorithm is proposed for the EFG method. The code has been written in FORTRAN language using MPI message passing library and implemented on a MIMD (multiple-instruction multiple- data)-type PARAM 10000 supercomputer. The code has been validated by solving three model fluid flow problems. A comparison is made among the results (velocities values) obtained by the EFG method with those obtained by the finite-element method (FEM) at a few typical locations. For 8 processors, speedup and efficiency have been obtained as 2.73 and 34.22% for N 1,552 in 1-D (Example I), 7.20 and 90.00% for N 1,462 in 2- D (Example II), and 7.30 and 91.27% for N 1,346 in 2- D (Example III), respectively.
机译:本文使用无网格无网格Galerkin(EFG)方法处理流体流动问题的并行计算解决方案。 EFG方法利用移动最小二乘(MLS)近似值来近似未知字段变量。 MLS近似值由三个部分组成:权函数,基函数和一组非恒定系数。针对EFG方法提出了一种新的并行算法。该代码已使用MPI消息传递库以FORTRAN语言编写,并在MIMD(多指令多数据)型PARAM 10000超级计算机上实现。该代码已通过解决三个模型流体流动问题进行了验证。在几个典型位置上,将通过EFG方法获得的结果(速度值)与通过有限元方法(FEM)获得的结果进行比较。对于8个处理器,一维中的N 1,552(示例I)的加速和效率为2.73和34.22%,二维中N 1,462的N 1,462(示例II)的为7.20和90.00%,N为7.30和91.27% 1,346 in 2-D(实施例III)。

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