基于Monte-Carlo随机有限元方法的随机边界条件下自然对流换热不确定性研究

为分析边界条件不确定性对方腔内自然对流换热的影响,发展了一种求解随机边界条件下自然对流换热不确定性传播的Monte-Carlo随机有限元方法.通过对输入参数场随机边界条件进行Karhunen-Loeve展开及基于Latin(拉丁)抽样法生成边界条件随机样本,数值计算了不同边界条件随机样本下方腔内自然对流换热流场与温度场,并用采样统计方法计算了随机输出场的平均值与标准偏差.根据计算框架编写了求解随机边界条件下方腔内自然对流换热不确定性的MATLAB随机有限元程序,分析了随机边界条件相关长度与方差对自然对流不确定性的影响.结果表明:平均温度场及流场与确定性温度场及流场分布基本相同;随机边界条件下Nu数概率分布基本呈现正态分布,平均Nu数随着相关长度和方差增加而增大;方差对自然对流换热的影响强于相关长度的影响.     

一种新的变域传热问题的边界条件

回顾古典热传导方程建立的假设条件的基础上,分析了热层材料由于表面烧蚀而引起的传热区域内部的热漏现象.基于能量守恒原理,利用有限元分析法推导出变域热传导方程,并得到了热漏函数的表达式,提出了变域传热问题边界条件的改进形式.为了检验这种边界条件的合理性,利用Crank-Nicholson法对此数学模型进行空间和时间离散化,并进行了数值仿真求解.仿真结果证实,基于边界条件改进形式的数学模型使计算更方便,结果更符合实际,从而为工程应用提供理论分析的依据.同时,该数学模型也为研究动边界发汗冷却控制问题奠定了理论研究的基础.     

Wick型随机组合ZK方程的白噪声泛函解

对变系数组合ZK方程进行白噪声扰动得到的Wick型随机组合ZK方程进行了研究．在Kondratiev分布空间（S）-1中利用白噪声分析,Hermite变换和多项式展开法,得到Wick型随机组合ZK方程的白噪声泛函解和变系数组合ZK方程的精确解．     

随机平面线弹性问题的一类弱Galerkin方法

本文针对带有随机杨氏模量和荷载的平面线弹性问题,提出了一类随机弱Galerkin有限元方法.先利用Karhunen-Loève展开把随机项参数化,将方程转化为一个确定性问题;再采用弱Galerkin有限元法和k-/p-型方法分别离散空间区域和随机场.在弱Galerkin离散中,用分片s(s≥1)和s+1次多项式逼近单元...     

基于多项式混沌展开的结构动力特性高阶统计矩计算

结构参数的不确定性会导致其动力特性的不确定性,量化动力特性的不确定性能为结构动力设计分析提供准确的动力信息.统计矩是表征结构动力特性不确定性非常重要的统计量,比如均值和方差.传统的Monte-Carlo(蒙特-卡洛)模拟方法需要大量次数的模型运算来保证结果的收敛,其用于复杂结构的动力特性统计矩计算因耗时太高而使用受限.该文采用多项式混沌展开替代模型来取代计算花费高的有限元模型,然后在替代模型框架下快速有效地计算结构动力特性的统计矩.该方法在建立替代模型之初只需要少量次数有限元分析,后续的统计矩计算无需有限元模型,因此从根本上解决了动力特性统计矩计算花费高的难题.该文的多项式混沌展开方法适用于参数服从任意概率分布,能够有效地计算高阶统计矩,为量化结构动力特性不确定性提供更多统计矩信息.最后通过平铝板算例验证了此方法的有效性.     

一类板弯曲方程的辛本征函数展开方法

本文研究一边简支对边滑支边界条件的矩形板方程的无穷维Hamilton算子本征函数系,证明该无穷维Hamilton算子广义本征函数系在Cauchy主值意义下是完备的,为应用辛本征函数展开法求解该平面弹性问题提供理论基础.进而推导出原方程的通解,并对该平面弹性问题指出什么样的边界条件可按此方法求解.最后应用具体的算例说明所得结论的合理性.     

基于Bell多项式的一类(3+1)维变系数广义浅水波方程的可积性研究

本文基于Bell多项式研究了一类(3+1)维变系数广义浅水波方程的可积性问题.首先,引入变量变换,借助Bell多项式与Hirota双线性算子之间的关系,导出方程的Hirota双线性形式,求出方程的N-孤子解,并对单孤子、双孤子和三孤子在不同情形下的传播进行图像模拟;其次,基于双线性方程,结合Bell多项式获得方程的双线性B?cklund变换;然后,通过Hopf-Cole变换,将双线性Backlund变换线性化,求出方程的Lax对;最后,利用级数展开法得到方程的无穷守恒律.从而证明该方程具有可积性.     

随机Bernstein多项式的依分布收敛问题

利用随机的Bernstein多项式研究随机逼近问题具有一定的意义.借助弱收敛的概念,从分布函数的角度,讨论了随机Bernstein多项式依分布收敛问题.同时,与依概率收敛结果相比较,以此说明Bernstein多项式序列依分布收敛适用的范围更广.     

接触表面的边界条件奇异性及其随机扰动

碰撞表面的随机边界条件反映了粘弹性材料在不同碰撞条件下的复杂性质.数值的不确定性和确定模型的渐近估计都可以利用计算机系统来计算.运用有限元方法来模拟碰撞表面的变形,得出远离接触表面部分的结构保持稳定.     

Stokes Flow in Cylindrical Containers With Rotating Ends北大核心CSCD

该文以端部旋转的圆柱形容器内的Stokes流为研究对象,根据流动的特点,将轴向坐标模拟为时间,则问题归结为Hamilton对偶方程的本征值和本征解问题.利用本征解空间的完备性和本征解之间的共轭辛正交关系,给出了问题解的展开形式,并建立了展开系数的数值求解方法.采用该方法研究了单端旋转、两端以相同或相反角速度旋转时不同外形比(容器的高度与半径之比)时圆柱形容器内流动速度和应力的分布情况,展示了不同边界条件下流场的一些特点.     

Stochastic Approaches to Uncertainty Quantification in CFD Simulations

This paper discusses two stochastic approaches to computing the propagation of uncertainty in numerical simulations: polynomial chaos and stochastic collocation. Chebyshev polynomials are used in both cases for the conventional, deterministic portion of the discretization in physical space. For the stochastic parameters, polynomial chaos utilizes a Galerkin approximation based upon expansions in Hermite polynomials, whereas stochastic collocation rests upon a novel transformation between the stochastic space and an artificial space. In our present implementation of stochastic collocation, Legendre interpolating polynomials are employed. These methods are discussed in the specific context of a quasi-one-dimensional nozzle flow with uncertainty in inlet conditions and nozzle shape. It is shown that both stochastic approaches efficiently handle uncertainty propagation. Furthermore, these approaches enable computation of statistical moments of arbitrary order in a much more effective way than other usual techniques such as the Monte Carlo simulation or perturbation methods. The numerical results indicate that the stochastic collocation method is substantially more efficient than the full Galerkin, polynomial chaos method. Moreover, the stochastic collocation method extends readily to highly nonlinear equations. An important application is to the stochastic Riemann problem, which is of particular interest for spectral discontinuous Galerkin methods.     

Stochastic approaches to uncertainty quantification in CFD simulations

This paper discusses two stochastic approaches to computing the propagation of uncertainty in numerical simulations: polynomial chaos and stochastic collocation. Chebyshev polynomials are used in both cases for the conventional, deterministic portion of the discretization in physical space. For the stochastic parameters, polynomial chaos utilizes a Galerkin approximation based upon expansions in Hermite polynomials, whereas stochastic collocation rests upon a novel transformation between the stochastic space and an artificial space. In our present implementation of stochastic collocation, Legendre interpolating polynomials are employed. These methods are discussed in the specific context of a quasi-one-dimensional nozzle flow with uncertainty in inlet conditions and nozzle shape. It is shown that both stochastic approaches efficiently handle uncertainty propagation. Furthermore, these approaches enable computation of statistical moments of arbitrary order in a much more effective way than other usual techniques such as the Monte Carlo simulation or perturbation methods. The numerical results indicate that the stochastic collocation method is substantially more efficient than the full Galerkin, polynomial chaos method. Moreover, the stochastic collocation method extends readily to highly nonlinear equations. An important application is to the stochastic Riemann problem, which is of particular interest for spectral discontinuous Galerkin methods.     

Practical uncertainty quantification analysis involving statistically dependent random variables

This article presents a practical refinement of generalized polynomial chaos expansion for uncertainty quantification under dependent input random variables. Unlike the Rodrigues-type formula, which exists for select probability measures, a three-step computational algorithm is put forward to generate a sequence of any approximate measure-consistent multivariate orthonormal polynomials. For uncertainty quantification analysis under dependent random variables, two regression methods, comprising existing standard least-squares and newly developed partitioned diffeomorphic modulation under observable response preserving homotopy (D-MORPH), are proposed to estimate the coefficients of generalized polynomial chaos expansion for the very first time. In contrast to the existing regression devoted so far to the classical polynomial chaos expansion, no tensor-product structure is required or enforced. The partitioned D-MORPH regression is applicable to either an underdetermined or overdetermined system, thus substantially enhancing the ability of the original D-MORPH regression. Numerical results obtained for Gaussian and non-Gaussian probability measures with rectangular or non-rectangular domains point to highly accurate orthonormal polynomials produced by the three-step algorithm. More significantly, the generalized polynomial chaos approximations of mathematical functions and stochastic responses from solid-mechanics problems, in tandem with the partitioned D-MORPH regression, provide excellent estimates of the second-moment properties and reliability from only hundreds of function evaluations or finite element analyses.     

Solution of a stochastic Darcy equation by polynomial chaos expansion

This paper deals with solving a boundary value problem for the Darcy equation with a random hydraulic conductivity field.We use an approach based on polynomial chaos expansion in a probability space of input data.We use a probabilistic collocation method to calculate the coefficients of the polynomial chaos expansion. The computational complexity of this algorithm is determined by the order of the polynomial chaos expansion and the number of terms in the Karhunen–Loève expansion. We calculate various Eulerian and Lagrangian statistical characteristics of the flow by the conventional Monte Carlo and probabilistic collocation methods. Our calculations show a significant advantage of the probabilistic collocation method over the directMonte Carlo algorithm.     

Dependence of polynomial chaos on random types of forces of KdV equations

In this study, one-dimensional stochastic Korteweg–de Vries equation with uncertainty in its forcing term is considered. Extending the Wiener chaos expansion, a numerical algorithm based on orthonormal polynomials from the Askey scheme is derived. Then dependence of polynomial chaos on the distribution type of the random forcing term is inspected. It is numerically shown that when Hermite (Laguerre or Jacobi) polynomial chaos is chosen as a basis in the Gaussian (Gamma or Beta, respectively) random space for uncertainty, the solution to the KdV equation converges exponentially. If a proper polynomial chaos is not used, however, the solution converges with slower rate.     

A fictitious domain approach to the numerical solution of PDEs in stochastic domains

We present an efficient method for the numerical realization of elliptic PDEs in domains depending on random variables. Domains are bounded, and have finite fluctuations. The key feature is the combination of a fictitious domain approach and a polynomial chaos expansion. The PDE is solved in a larger, fixed domain (the fictitious domain), with the original boundary condition enforced via a Lagrange multiplier acting on a random manifold inside the new domain. A (generalized) Wiener expansion is invoked to convert such a stochastic problem into a deterministic one, depending on an extra set of real variables (the stochastic variables). Discretization is accomplished by standard mixed finite elements in the physical variables and a Galerkin projection method with numerical integration (which coincides with a collocation scheme) in the stochastic variables. A stability and convergence analysis of the method, as well as numerical results, are provided. The convergence is “spectral” in the polynomial chaos order, in any subdomain which does not contain the random boundaries.     

A Legendre–Galerkin Chebyshev collocation method for the Burgers equation with a random perturbation on boundary condition

In the paper, we apply the generalized polynomial chaos expansion and spectral methods to the Burgers equation with a random perturbation on its left boundary condition. Firstly, the stochastic Galerkin method combined with the Legendre–Galerkin Chebyshev collocation scheme is adopted, which means that the original equation is transformed to the deterministic nonlinear equations by the stochastic Galerkin method and the Legendre–Galerkin Chebyshev collocation scheme is used to deal with the resulting nonlinear equations. Secondly, the stochastic Legendre–Galerkin Chebyshev collocation scheme is developed for solving the stochastic Burgers equation; that is, the stochastic Legendre–Galerkin method is used to discrete the random variable meanwhile the nonlinear term is interpolated through the Chebyshev–Gauss points. Then a set of deterministic linear equations can be obtained, which is in contrast to the other existing methods for the stochastic Burgers equation. The mean square convergence of the former method is analyzed. Numerical experiments are performed to show the effectiveness of our two methods. Both methods provide alternative approaches to deal with the stochastic differential equations with nonlinear terms.     

Polynomial Chaos for Analysing Periodic Processes of Differential Algebraic Equations with Random Parameters

Mathematical models of dynamical systems typically include technical parameters. Assuming an uncertainty, some parameters are replaced by random variables and the solution of the time–dependent system becomes a stochastic process. We consider forced oscillators, which are modelled by systems of differential algebraic equations. Consequently, periodic boundary conditions are imposed on the system. We apply the technique of the generalised polynomial chaos to resolve the stochastic model. Numerical simulations based on the electric circuit of a transistor amplifier are presented. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)