非定常对流扩散方程的局部和并行有限元算法

对基于两重网格的非定常对流扩散方程的局部和并行有限元算法进行了研究.算法的理论依据是两重网格的思想,解的低频分量可以用一个整体的粗网格空间来逼近,高频分量可以用局部和并行的细网格空间来逼近.因此,这种局部和并行算法仅仅涉及一个粗网格上的整体逼近和细网格上的局部校正.得到了算法的误差估计,一些数值例子验证了算法的有效性.     

不可压缩流动的并行数值方法

不可压缩流动的数值模拟是计算流体力学的重要组成部分. 基于有限元离散方法, 本文设计了不可压缩Navier-Stokes (N-S)方程支配流的若干并行数值算法. 这些并行算法可归为两大类: 一类是基于两重网格离散方法, 首先在粗网格上求解非线性的N-S方程, 然后在细网格的子区域上并行求解线性化的残差方程, 以校正粗网格的解; 另一类是基于新型完全重叠型区域分解技巧, 每台处理器用一局部加密的全局多尺度网格计算所负责子区域的局部有限元解. 这些并行算法实现简单, 通信需求少, 具有良好的并行性能, 能获得与标准有限元方法相同收敛阶的有限元解. 理论分析和数值试验验证了并行算法的高效性     

大Reynolds数Navier-Stokes方程带回溯技巧的两水平方法

本文考虑了一种求解大Reynolds数定常Navier-Stokes方程带回溯(backtracking)技巧的两水平有限元方法.其基本思想是,首先在一粗网格上求解带有亚格子模型稳定项的Navier-Stokes方程,然后在细网格上求解一个亚格子模型稳定化的线性Newton问题,最后又回到粗网格上求解线性化的校正问题.通过适当的稳定化参数和粗细网格尺寸的选取,本文的算法能取得最优渐近收敛阶.数值实验检验了理论分析的正确性和算法的有效性.     

Navier-Stokes方程的一种并行两水平有限元方法

基于区域分解技巧,提出了一种求解定常Navier-Stokes方程的并行两水平有限元方法．该方法首先在一粗网格上求解Navier-Stokes方程,然后在细网格的子区域上并行求解粗网格解的残差方程,以校正粗网格解．该方法实现简单,通信需求少．使用有限元局部误差估计,推导了并行方法所得近似解的误差界,同时通过数值算例,验证了其高效性．     

三维泊松方程基于Richardson外推法的高阶紧致差分方法

基于Richardson外推法提出了数值求解三维泊松方程的高阶紧致差分方法.方法通过利用四阶和六阶紧致差分格式,分别在细网格和粗网格上求解,然后利用Richardson外推技术和算子插值方法,得到三维泊松方程在细网格上的六阶和八阶精度的数值解.数值实验结果验证了该方法的精确性和有效性.     

二次Lagrangian有限元方程的几何多重网格法

为了构造快速求解二次Lagrangian有限元方程的几何多重网格法,在选择二次Lagrangian有限元空间和一系列线性Lagrangian有限元空间分别作为最细网格层和其余粗网格层以及构造一种新限制算子的基础上,提出了一种新的几何多重网格法,并对它的计算量进行了估计.数值实验结果,与通常的几何多重网格法和AMG01法相比,表明了新算法计算量少且稳健性强.     

求解复系数椭圆方程的两层网格算法

<正>1引言两层网格方法是用来求解非对称不定问题和非线性问题的一种非常有效的数值方法[1,2].其主要思想是,借助于两层网格空间,将细网格上的复杂问题转化为求解一个细网格空间的简单问题和一个粗网格上的问题.由于粗网格空间相对于细网格空间很小,所以减少了计算代价,并且仍能得到原问题的最优解.因此,两层网格算法被广泛研究并被用于求解多种问题,例如,求解非对称和非线性椭圆方程[1,2,3,4],非线性弹性方程[5],Navier-Stokes方程[6,7,8]及特征值问题[9,10].HSS迭代方法是求解大规模稀疏非埃尔米特正定     

热传导-对流问题的回溯二重水平法

该文给出定常的热传导-对流问题的有限元逼近的一种二重水平方法. 这种二重水平方法包括解一个小的非线性的粗网格系统、一个细网格上的线性Oseen问题和一个粗网格上的线性校正问题. 同时,给出了这种近似解的存在性和收敛性分析.     

带移动奇异源热方程的移动网格方法

研究了一个带若干奇异源热方程的数值求解,其源的移动由一个常微分方程描述.基于移动观察区域和区域分解思想提出了一个移动网格预估校正算法.网格方程可自然的通过并行高效求解,算法避免了跳跃信息[u]的计算而使物理方程的离散格式变得非常简单,且仍保持了空间上的二阶收敛性.数值例子验证了算法的收敛性和高效性,并模拟了非线性源函数带来的爆破现象.     

外部非定常Navier-Stokes方程的非线性Galerkin算法

提出了求解外部非定常Navier-stokes方程的有限元边界元耦合的非线性Galerkin算法,证明了相应变分问题的正则性和数值解的收敛速度。收敛性分析表明如果选取粗网格尺度H是细网格尺度h的开平方数量级,则该算法提供了与古典Galerkin算法同阶的收敛速度。然而非线性Galerkin算法仅仅需要在粗网格解非线性问题,在细网格上解线性问题。因此,该算法可以节省计算工作量。     

一维波动方程联合反演的分步正则化法

本文只用一个纵波信息,对一维波动方程的速度和震源函数进行联合反演.并考虑到波动方程的反问题是一不适定问题,对震源函数和波速分别用正则化法分步迭代求解,大大减少了反问题的计算工作量,改善了该反问题的计算稳定性.为计算实际一维地震数据提供了一种方法.文中给出了只用一个反问题补充条件同时进行多参数反演的详细公式,并对相应的数值算例进行了分析和比较.     

On numerical implementations of a new iterative method with boundary condition splitting for solving the nonstationary stokes problem in a strip with periodicity condition

Based on finite-difference approximations in time and a bilinear finite-element approximation in spatial variables, numerical implementations of a new iterative method with boundary condition splitting are constructed for solving the Dirichlet initial-boundary value problem for the nonstationary Stokes system. The problem is considered in a strip with a periodicity condition along it. At each iteration step of the method, the original problem splits into two much simpler boundary value problems that can be stably numerically approximated. As a result, this approach can be used to construct new effective and stable numerical methods for solving the nonstationary Stokes problem. The velocity and pressure are approximated by identical bilinear finite elements, and there is no need to satisfy the well-known difficult-to-verify Ladyzhenskaya-Brezzi-Babuska condition, as is usually required when the problem is discretized as a whole. Numerical iterative methods are constructed that are first- and second-order accurate in time and second-order accurate in space in the max norm for both velocity and pressure. The numerical methods have fairly high convergence rates corresponding to those of the original iterative method at the differential level (the error decreases approximately 7 times per iteration step). Numerical results are presented that illustrate the capabilities of the methods developed.     

一个带固定步长的ODE型算法

提出了一种新的求解无约束优化问题的ODE型方法,其特点是:它在每次迭代时仅求解一个线性方程组系统来获得试探步;若该试探步不被接受,算法就沿着该试探步的方向求得下一个迭代点,其中步长通过固定公式计算得到.这样既避免了传统的ODE型算法中为获得可接受的试探步而重复求解线性方程组系统,又不必执行线搜索,从而减少了计算量.在适当的条件下,还证明了新算法的整体收敛性和局部超线性收敛性.数值试验结果表明:提出的算法是有效的.     

Some sixth order zero-finding variants of Chebyshev-Halley methods

A zero-finding technique in which the order of convergence is improved and nonlinear equations are solved more efficiently than they are solved by traditional iterative methods is derived. Composing a modified Chebyshev-Halley method with a variant of this method that just introduces one evaluation of the function the iterative methods presented are obtained. By carrying out this procedure the output numerical results show that the new methods compete in both order and efficiency with the modified Chebyshev-Halley methods.     

Block s‐step Krylov iterative methods

Block (including s‐step) iterative methods for (non)symmetric linear systems have been studied and implemented in the past. In this article we present a (combined) block s‐step Krylov iterative method for nonsymmetric linear systems. We then consider the problem of applying any block iterative method to solve a linear system with one right‐hand side using many linearly independent initial residual vectors. We present a new algorithm which combines the many solutions obtained (by any block iterative method) into a single solution to the linear system. This approach of using block methods in order to increase the parallelism of Krylov methods is very useful in parallel systems. We implemented the new method on a parallel computer and we ran tests to validate the accuracy and the performance of the proposed methods. It is expected that the block s‐step methods performance will scale well on other parallel systems because of their efficient use of memory hierarchies and their reduction of the number of global communication operations over the standard methods. Copyright © 2009 John Wiley & Sons, Ltd.     

Accuracy of multivalue methods during change of time‐step size: Adams‐Moulton

Multivalue methods are a class of time‐stepping procedures for numerical solution of differential equations that progress to a new time level using the approximate solution for the function of interest and its derivatives at a single time level. The methods differ from multistep procedures, which make use of solutions to the differential equation at multiple time levels to advance to the new time level. Multistep methods are difficult to employ when a change in time‐step is desired, because the standard formulas (e.g., Adams‐Moulton or Gear) must be modified to accommodate the change. Multivalue methods seem to possess the desirable feature that the time‐step may be changed arbitrarily as one proceeds, since the solution proceeds from a single time level. However, in practice, changes in the time‐step introduce lower order errors or alter the coefficient in the truncation error term. Here, the multivalue Adams‐Moulton method is presented based on a general interpolation procedure. Modifications required to retain the high‐order accuracy of these methods during a change in time‐step are developed. Additionally, a formula for the unknown initial derivatives is presented. Finally, two examples are provided to illustrate the potential merit of the modification to the standard multivalue methods. © 2000 John Wiley & Sons, Inc. Numer Methods Partials Differential Eq 16: 312–326, 2000     

Goldstein, Levitin-Polyak投影法中的一个可行步长准则

１引　言设Ω是Ｒｎ空间的一个非空的凸闭紧子集,Ｆ是Ｒｎ→Ｒｎ的算子．我们考虑变分不等式问题： 变分不等式问题在数学规划中起着很重要的作用,因此,长期以来一直受到广泛的重视．求解变分不等式问题的方法中,有一类投影迭代方法,例如［１,４,６,９］．在所有的投影迭代方法中,Ｇｏｌｄｓｔｅｉｎ［６］,Ｌｅｖｉｔｉｎ－Ｐｏｌｙａｋ［９］所提出的方法;是最简单的．这里,ＰΩ（ｘ）是ｘ在　上的投影,即　的唯一解． 我们称算子Ｆ在集合Ω上是单调的,若在用Ｇｏｌｄｓｔｅｉｎ,Ｌｅｖｉｔｉｎ－Ｐｏｌｙａｋ方法（２）求…     

Waveform relaxation methods for implicit differential equations

We apply a Runge-Kutta-based waveform relaxation method to initial-value problems for implicit differential equations. In the implementation of such methods, a sequence of nonlinear systems has to be solved iteratively in each step of the integration process. The size of these systems increases linearly with the number of stages of the underlying Runge-Kutta method, resulting in high linear algebra costs in the iterative process for high-order Runge-Kutta methods. In our earlier investigations of iterative solvers for implicit initial-value problems, we designed an iteration method in which the linear algebra costs are almost independent of the number of stages when implemented on a parallel computer system. In this paper, we use this parallel iteration process in the Runge-Kutta waveform relaxation method. In particular, we analyse the convergence of the method. The theoretical results are illustrated by a few numerical examples.     

线性方程组迭代方法残差光滑技术的一个推广

1　引　　言我们考虑求解线性方程组Ax=b,A∈Rn×n,b,x∈Rn.(1)的迭代方法.迭代序列{xk}的性态常常由与之对应的残差范数序列{‖rk‖}的特性来决定.人们自然希望{‖rk‖}光滑地(单调地)收敛到0.在所有Krylov子空间方法中,GMRES[7]方法因为可使{‖rk‖}最优地趋于0,故是一个较为成功的方法.但是,GMRES方法的工作量和存贮量却随着迭代步数的增加而迅速增加.而BCG[4]和CGS[10]等方法具有运算量小,收敛快等突出优点.但它们的残差范数性态却很不规则,{‖rk‖}振荡不定.这给判断收敛性及何时停机带来很大的不便.残差光滑技术是一个行之有…     

High performance solution of partial differential equations discretized using a Chebyshev spectral collocation method

When a Chebyshev spectral collocation method is applied to a flow problem in a rectangularly decomposable domain it leads to the solution of a structured linear system. Since the linear system is solved at each step of a Newton-type iterative process an efficient method to solve it needs to be provided. A level 3 BLAS-based algorithm is presented and its efficiency is studied.