非线性对流扩散问题的差分-流线扩散法

1．引言流线扩散法（简称SD方法）是由Huzhes和Brooks在1980年前后提出的一种数值求解对流占优扩散问题的新型有限元算法．随后，Johnson和N8vert将SD方法推广到发展型对流扩散问题（［1］，［2］，［3］）．熟知，对于对流扩散问题，标准有限元法虽具有高阶精度，但常产生数值振荡；古典人工粘性Galerkin法更具有较好的稳定性，但仅具有一阶精度．而（SD方法兼具良好的数值稳定性和高阶精度，因此得到了越来越多的重视，对于发展型对流扩散问题，传统的SD方法均采用时空有限元．这样做，虽然可使时间和空间方向上的精度很好的协调起…     

对流扩散方程一类改进的特征线修正有限元方法

１引言在地下水污染,地下渗流驱动,核污染,半导体等问题的数值模拟中,均涉及抛物型对流扩散方程（或方程组）的数值求解问题．这些对流扩散型偏微分方程（或方程组）具有共同的特点：对流的影响远大于扩散的影响,即对流占优性,对流占优性给问题的数值求解带来许多困难,因此对流占优问题的有效数值解法一直是计算数学中重要的研究内容．用通常的差分法或有限元法进行数值求解将出现数值振荡．为了克服数值振荡,提出各种迎风方法和修正的特征方法并在这些问题上得到成功的实际应用、８０年代,Ｄｏｕｇｌａｓ和Ｒｕｓｓｅｌｌ［２］等…     

非定常对流扩散问题差分-流线扩散法的线性三角元解的最优误差估计

讨论了差分-流线扩散法(FDSD)求解线性对流占优扩散问题解的精度,利用插值后处理技术,使该格式解的空间精间达到最优.     

一类非线性对流扩散问题的FDSD预测校正格式

1．引言由Hughes和Brooks门提出,并经Johnson等人［‘－‘1发展的流线扩散法（Streamline－DiffusionMetho人以下简称SD方法）是求解对流占优扩散问题（包括纯双曲问题）的一种有效的数值方法．由于良好的数值稳定性及其高阶收敛率,SD方法已广泛地应用于计算流体等诸多科学工程计算．然而,传统的sD方法利用时一空有限元求解发展型问题,导致对高维问题工作量过于庞大;其编程实现较复杂,对非线性问题也不便进行线性化处理．为使SD方法能够较简便地应用于高维和非线性问题,孙撒问提出了仅对空间域作有限元离散,而对时间域作差分…     

非线性对流扩散问题的预测校正型FDSD格式

1 引言 流线扩散法(简称SD方法)是由Hughes和Brooks提出,并经Johnson等人发展的一种数值求解对流占优扩散问题的一种有效的数值方法。然而,传统的SD方法利用时-空有限元求解发展型问题,导致对高维问题工作量过于庞大,其编程实现较复杂,对非线性问题也不便进行线性化处理,为此,孙澈提出了仅对空间域作有限元离散,而对时间域作差分离散的差分-流线扩散法(以下简称FDSD方法),主要讨论了拟线性     

带有对流项的拟线性Sobolev方程的差分-流线扩散法

1　引　　言流线扩散法 (Stream line- Diffusion method,简称 SD方法 )是由 Hughes和 Brooks在文献 [1]中提出 ,并由 Johnson等人 (文献 [2 ]- [4])发展起来的求解对流占优扩散问题 (包括双曲型问题 )的一种有效的数值方法 .SD方法兼具有良好的数值稳定性和高阶收敛精度已广泛地应用于计算流体等诸多问题 .然而 ,传统的 SD方法均采用时空有限元求解发展型问题 .这样 ,虽可使时间和空间方向上的精度很好的协调起来 ,却增大了实际计算的复杂程度 ;对非线性问题也不便进行线性化处理 .孙澈等人在文献 [5 ]中对对流扩散问题提出了仅对空间…     

对流扩散问题的流线扩散有限元分析

对流扩散问题的流线扩散有限元分析孙澈，张阳，魏强（南开大学）ＴＨＥＳＴＲＥＡＭＬＩＮＥ－ＤＩＦＦＵＳＩＯＮＦ．Ｅ．ＡＮＡＬＹＳＩＳＦＯＲＣＯＮＶＥＣＴＩＯＮＤＩＦＦＵＳＩＯＮＰＲＯＢＬＥＭＳ￥ＳｕｎＣｈｅ；ＺｈａｎｇＹａｎｇ；ＷｅｉＱｉａｎｇ（Ｎａｎ...     

对流占优问题的无网格稳定化方法

应用标准的无网格方法求解对流占优问题时会出现数值伪振荡.针对此问题,给出了无网格方法中消除非稳定数值解的4种技术,即节点加密、增大节点影响半径、完全迎风无网格稳定化方法、自适应无网格稳定化方法.并将这4种技术应用于径向点插值方法求解一维或二维对流扩散方程.数值结果表明这4种技术均能有效地消除对流占优时的数值伪振荡现象,且自适应迎风无网格稳定化方法是4种技术中最有效的.     

对流扩散问题的Crank-Nicolson差分-流线扩散法

1　引　言Streamline- Diffusion method (SD方法 )是近年来 Hughes和 Brooks提出的一种求解定常的对流占优和对流扩散问题的人工粘性有限元方法[1 ] ,[2 ] ,它具有标准有限元方法的高阶精度特点和人工粘性 Galerkin方法的稳定性特点 ,因此越来越受到人们的重视 .现在 ,SD方法已被推广到 Euler方程和 Navier- Stokes方程等发展型对流扩散问题[3 ] [4] ,但是常常采用时空有限元 [3 ] [5] ,这样能把时间和空间的精度很好地统一起来 ,却增大了数值计算的复杂性 ,基于此 [6 ]对非线性的对流占优扩散问题提出一种 Finite Difference- Strea…     

对流扩散问题的交替方向差分-流线扩散格式

1.引 言 差分-流线扩散法（Finite Difference-Streamline Diffusion Method,简称FDSD方法）于1998年由文[1]提出并对线性对流占优扩散问题给出分析,随后文[2],[3]就非线性问题的FDSD格式及FDSD预测-校正格式,分别作出了分析,文[4]讨论了FDSD方法的后验估计及自适应技术,[5],[6]则分别讨论了FDSD方法的某些重要应用.与基于时-空有限元的传统流线扩散法相比,FDSD方法的计算工作量已有成数量级的减少,且较易于推广到非线性问题,然而,对于高维问题,在每一时间层,仍然需要求解一大型线性或非线性方程组,工作量仍然很大.参照J.Douglas与T.Dupont关于抛物问题交替方向     

扩散速度与组元的质量守恒方程

在混合物流动中,某组元i的质量迁移速度(绝对速度)等于对流速度(牵连速度)与扩散速度(相对速度)之和.扩散速度——以及扩散系数——依对流速度取法之不同而不同.在湍流中,组元i的质量迁移速度是(?)(质量加权的时间平均速度).扩散速度((?)-a)是由湍流扩散速度(?)和分子扩散速度v_1-a所组成((?)是组元i的时平均速度,a是某种取法的对流速度).因此,湍流扩散速度与对流速度的取法无关.组元i的质量守恒方程,其右端的扩散须依其左端对流速度取法之不同而不同.在湍流情况下,它可能没有扩散项,或只有湍流扩散项,或既有湍流扩散项又有分子扩散项,如果我们取(?),或(?),或任何其它速度作为对流速度的话.在层流中会遇到只有分子扩散项而无湍流扩散项的例子.分子扩散总是依赖于对流速度的选取.与混合气体情况不同,在二相流中.分子扩散项的重要性相当于、或甚至超过湍流扩散项.     

Construction of Generalized Diffusion Processes: the Resolvent Approach

In this paper, we define the generalized diffusion operator ■ for two suitable measures on the line, which includes the generators of the birth-death processes, the one-dimensional diffusion and the gap diffusion among others. Via the standard resolvent approach, the associated generalized diffusion processes are constructed.     

Solutions for a fractional diffusion equation with noninteger dimensions

We investigate a fractional diffusion equation with a nonlocal reaction term by using the Green function approach. We also consider a modified spatial operator in order to cover situations characterized by a noninteger dimension. The results show a nonusual spreading of the initial condition which can be connected to a rich class of anomalous diffusive processes.     

Inference in lognormal multidimensional diffusion processes with exogenous factors: Application to modelling in economics

The multidimensional lognormal diffusion process with exogenous factors is treated using the Kolmogorov equations, and the mean vector and covariance matrix are estimated using discrete sampling by the maximum-likelihood method. Also, this process is constructed as a solution of a multidimensional stochastic differential equation, and an estimation is made through the maximum-likelihood method to infer the parameters of the exogenous factors, this time using continuous sampling. Finally, a test for a hypothesis based on these parameters is constructed.     

A Carleman estimate of some anisotropic space-fractional diffusion equations

This paper is concerned with Carleman estimates for some anisotropic space-fractional diffusion equations, which are important tools for investigating the corresponding control and inverse problems. By employing a special weight function and the nonlocal vector calculus, we prove a Carleman estimate and apply it to build a stability result for a backward diffusion problem.     

Solutions for a generalized fractional anomalous diffusion equation

In this paper, we investigate the solutions for a generalized fractional diffusion equation that extends some known diffusion equations by taking a spatial time-dependent diffusion coefficient and an external force into account, which subjects to the natural boundaries and the generic initial condition. We obtain explicit analytical expressions for the probability distribution and study the relation between our solutions and those obtained within the maximum entropy principle by using the Tsallis entropy.     

A variable nonlinear splitting algorithm for reaction diffusion systems with self‐ and cross‐ diffusion

Self‐ and cross‐diffusion are important nonlinear spatial derivative terms that are included into biological models of predator–prey interactions. Self‐diffusion models overcrowding effects, while cross‐diffusion incorporates the response of one species in light of the concentration of another. In this paper, a novel nonlinear operator splitting method is presented that directly incorporates both self‐ and cross‐diffusion into a computational efficient design. The numerical analysis guarantees the accuracy and demonstrates appropriate criteria for stability. Numerical experiments display its efficiency and accuracy.     

Complex diffusion filtering of three dimensional turbulent flows

The linear and nonlinear complex diffusion filtering methods are proposed to extract the organized coherent part as well as the random incoherent part from forced and decaying turbulent flows. An attempt to examine the robustness of the two methods in filtering the turbulent flow field without the transformation into the frequency domain is carried out. The velocity fields of the forced and decaying cases are decomposed into coherent and incoherent parts in the spatial domain. The complex diffusion process can be obtained by combining the linear diffusion equation and the free particle Schrodinger equation. The imaginary parts in the two methods serve as a robust edge-detector with increasing confidence in time. The numerical implementations of the 3D linear and nonlinear complex diffusion partial differential equations are done using the finite difference method. The flatness, skewness and spectrum of the extracted fields are also studied for each filtering method. Results show that the two filtering methods can identify the coherent fields and preserve the features of the turbulent fields. Comparisons to the wavelet and Fourier decompositions are also considered.