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EFFICIENT ITERATIVE METHODS APPLIED TO THE SOLUTION OF TRANSONIC FLOWS

机译:有效的迭代方法,用于解决跨音速流动

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We investigate the use of an inexact Newton's method to solve the potential equations in the transonic regime. As a test case, we solve the two-dimensional steady transonic small disturbance equation. Approximate factorization/ADI techniques have traditionally been employed for implicit solutions of this nonlinear equation. Instead, we apply Newton's method using an exact analytical determination of the Jacobian with preconditioned conjugate gradient-like iterative solvers for solution of the linear systems in each Newton iteration. Two iterative solvers are tested; a block s-step version of the classical Orthomin(k) algorithm called orthogonal s-step Orthomin (OSOmin) and the well-known GMRES method. The preconditioner is a vectorizable and parallelizable version of incomplete LU (ILU) factorization. Efficiency of the Newton-iterative method on vector and parallel computer architectures is the main issue addressed. In vectorized tests on a single processor of the Gray C-90, the performance of Newton-OSOmin is superior to Newton-GMRES and a more traditional monotone AF/ADI method (MAF) for a variety of transonic Mach numbers and mesh sizes. Newton-GMRES is superior to MAF for some cases. The parallel performance of the Newton method is also found to be very good on multiple processors of the Gray C-90 and on the massively parallel thinking machine CM-5, where very fast execution rates (up to 9 Gflops) are found for large problems. (C) 1996 Academic Press, Inc. [References: 38]
机译:我们研究了使用不精确的牛顿法求解跨音速状态下的势方程。作为测试案例,我们求解了二维稳态跨音速小扰动方程。传统上,近似分解/ ADI技术已用于该非线性方程的隐式解。取而代之的是,我们使用牛顿法,该方法使用具有预条件的共轭梯度式迭代求解器的Jacobian精确解析确定法来求解每次牛顿迭代中的线性系统。测试了两个迭代求解器;经典Orthomin(k)算法的块s步版本,称为正交s步Orthomin(OSOmin)和著名的GMRES方法。前置条件是不完整LU(ILU)分解的向量化和可并行化版本。牛顿迭代方法在矢量和并行计算机体系结构上的效率是解决的主要问题。在灰色C-90的单个处理器上进行的矢量化测试中,对于多种跨音速马赫数和网格尺寸,Newton-OSOmin的性能优于Newton-GMRES和更传统的单调AF / ADI方法(MAF)。在某些情况下,Newton-GMRES优于MAF。还发现,在Gray C-90的多个处理器上以及大规模并行思维机CM-5上,牛顿法的并行性能非常好,在大型并行思维机CM-5上,大型问题的执行速度非常快(高达9 Gflops) 。 (C)1996 Academic Press,Inc. [参考:38]

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