Efficient stochastic finite element methods for flow in heterogeneous porous media. Part 2: Random lognormal permeability

Efficient and robust iterative methods are developed for solving the linear systems of equations arising from stochastic finite element methods for single phase fluid flow in porous media. Permeability is assumed to vary randomly in space according to some given correlation function. In the companion paper, herein referred to as Part 1, permeability was approximated using a truncated Karhunen-Loève expansion (KLE). The stochastic variability of permeability is modeled using lognormal random fields and the truncated KLE is projected onto a polynomial chaos basis. This results in a stochastic nonlinear problem since the random fields are represented using polynomial chaos containing terms that are generally nonlinear in the random variables. Symmetric block Gauss-Seidel used as a preconditioner for CG is shown to be efficient and robust for stochastic finite element method.     

Control of Navier–Stokes equations by means of mode reduction

In a previous work (Park HM, Lee MW. An efficient method of solving the Navier–Stokes equation for the flow control. International Journal of Numerical Methods in Engineering 1998; 41 : 1131–1151), the authors proposed an efficient method of solving the Navier–Stokes equations by reducing their number of modes. Employing the empirical eigenfunctions of the Karhunen–Loève decomposition as basis functions of a Galerkin procedure, one can a priori limit the function space considered to the smallest linear sub‐space that is sufficient to describe the observed phenomena, and consequently, reduce the Navier–Stokes equations defined on a complicated geometry to a set of ordinary differential equations with a minimum degree of freedom. In the present work, we apply this technique, termed the Karhunen–Loève Galerkin procedure, to a pointwise control problem of Navier–Stokes equations. The Karhunen–Loève Galerkin procedure is found to be much more efficient than the traditional method, such as finite difference method in obtaining optimal control profiles when the minimization of the objective function has been done by using a conjugate gradient method.     

随机杆系结构几何非线性分析的递推求解方法

建立了随机静力作用下考虑几何非线性的随机杆系结构的随机非线性平衡方程. 将和 位移耦合的随机割线弹性模量以及随机响应量表示为非正交多项式展开式，运用传统的摄动方法获 得了关于非正交多项式展式的待定系数的确定性的递推方程. 在求解了待定系数后，利用非 正交多项式展开式和正交多项式展开式的关系矩阵，可以很方便地得到未知响应量的二阶统计矩. 两杆结构和平面桁架拱的算例结果表明，当随机量涨落较大时，递推随机有限元方法比基于 二阶泰勒展开的摄动随机有限元方法更逼近蒙特卡洛模拟结果，显示了该方法对几何非线性 随机问题求解的有效性.     

基于侵入混沌多项式法的随机多孔介质内顺磁性流体热磁对流不确定度量化

目前流体流动与传热问题的研究大都基于确定性工况条件,而现实流体流动与传热问题中存在着大量不确定性因素,计算流体力学的不确定性量化提供了一种理解流体物性、边界条件与初始条件等不确定性因素对模拟结果影响的能力.为揭示随机多孔介质内顺磁性流体热磁对流的传播规律与演化特征,本文发展了一种基于侵入式多项式混沌展开法的热磁对流不确...     

随机结构有限元分析的递推求解方法

提出了一种新的谱随机有限元分析方法——递推求解方法。该方法将随机结构的随机响应表示成非正交多项式展式,建立了和摄动法类似的一系列确定的递推方程,并通过确定性有限元方法对这些递推方程进行静力问题求解。算例表明,当随机量出现较大涨落时,计算结果相对于传统摄动法有不小的改进。     

A proper orthogonal decomposition of a simulated supersonic shear layer

The Karhunen–Loève procedure is applied to the analysis of an ensemble of snapshots obtained from a conditionally sampled localized shear layer simulation. The computed set of optimal basis functions is used to economically characterize sampled flow realizations. Pictorially it is seen that the essential features (and roughly 80% of the energy) of typical flows are captured by retaining roughly 10–20 parameters in the expansion. Smaller-scale features are resolved by retaining more terms in the series.     

Wavelet basis expansion-based spatio-temporal Volterra kernels identification for nonlinear distributed parameter systems

In practical industries, there are many systems belong to nonlinear distributed parameter systems (DPS); unfortunately, modeling of nonlinear DPS is a challenging task because of the infinite-dimensional and nonlinear properties. To model the nonlinear DPS, a spatio-temporal Volterra model is presented with a series of spatio-temporal kernels. It can be considered as a spatial extension of the traditional Volterra model. One question involved in modeling a spatio-temporal functional relationship between the input and output of nonlinear distributed parameter systems using spatio-temporal Volterra series is to identify the spatio-temporal Volterra kernel functions. In addition, in order to derive a low-order model, the Karhunen–Loève (KL) decomposition is used for the time/space separation. The basic routine of the approach is that, first, from the system outputs, KL decomposition is used for the time/space separation, where the spatio-temporal output is decomposed into a few dominant spatial basis functions with temporal coefficients. Second, according to temporal coefficients of outputs under multilevel excitations, the Volterra series outputs of different orders are estimated with the wavelet balance method. Third, the Volterra kernel functions of different orders are separately estimated through their corresponding Volterra series outputs by expanding them with four-order B-spline wavelet on the interval (BSWI). Finally, the spatio-temporal Volterra model can be reconstructed using the time/space synthesis. The simulation studies verify the effectiveness of the presented identification method.     

Hopf bifurcation of nonlinear system with multisource stochastic factors

The article mainly explores the Hopf bifurcation of a kind of nonlinear system with Gaussian white noise excitation and bounded random parameter. Firstly, the nonlinear system with multisource stochastic factors is reduced to an equivalent deterministic nonlinear system by the sequential orthogonal decomposition method and the Karhunen–Loeve (K-L) decomposition theory. Secondly, the critical conditions about the Hopf bifurcation of the equivalent deterministic system are obtained. At the same time the influence of multisource stochastic factors on the Hopf bifurcation for the proposed system is explored. Finally, the theorical results are verified by the numerical simulations.     

Karhunen–Loève Galerkin method with decimated sampling technique for the simulation of complex fluids defined in the phase space

In complex fluids, solute molecules with structural length scales much larger than atomic are dispersed in solvents of simple fluids such as water. The rheological properties of complex fluids are determined by dynamics of solute molecules which can be modeled by the Fokker–Planck equation defined in a six-dimensional phase space. In the present investigation, we devise a method of efficient simulation of complex fluid flows employing the Karhunen–Loève Galerkin (KLG) method. Adopting the decimated sampling of solvent flow fields, a reduced-order model for the Fokker–Planck equation is obtained, which can be employed for the the simulation of complex fluids with a decent computer time. As a specific example, we consider a flow of dilute polymeric liquids over a cylinder, whose constitutive equation is the FENE (finitely extensible nonlinear elastic) model. It is found that the KLG method with the decimated sampling technique yields accurate results at a computational cost less than a hundredth of that for the numerical simulation using the Fokker–Planck equation. The KLG method supplemented by the decimated sampling technique is an efficient method of coarse-graining for equations of complex fluids defined in the phase space.     

Spatiotemporal combination synchronization of different nonlinear objects

This paper proposes a novel secure communication scheme based on the Karhunen–Loéve decomposition and the synchronization of a master and a slave hyperchaotic Lü systems. First, the Karhunen–Loéve decomposition is used as a data reduction tool to generate data coefficients and eigenfunctions that capture the essence of grayscale and color images in an optimal manner. It is noted that the original images can be reproduced using only the most energetic eigenfunctions; this results in computational savings. The data coefficients are encrypted and transmitted using a master hyperchaotic Lü system. These coefficients are then recovered at the receiver end using a sliding mode controller to synchronize two hyperchaotic Lü systems. Simulation results are presented to illustrate the ability of the proposed control law to synchronize the master and slave hyperchaotic Lü systems. Moreover, the original images are recovered by using the decrypted data coefficients in conjunction with the eigenfunctions of the image. Computer simulation results are provided to show the excellent performance of the proposed scheme.     

基于概率配点法的岩土材料参数随机场及其响应分析

概率配点法是进行不确定性问题分析的一种有效方法。通过对输入参数场进行Karhunen-Loeve展开,将其表示为一系列独立随机变量在不同权重下的线性组合,再以与之相同的随机变量组合形成混沌多项式展开对输出随机场进行分解,通过某种算法选取随机变量的值,将其作为插值点的组合(配点),在这些配点上,概率方程演化为一个确定性问题方程。由此,可以直接利用现有软件或者确定性问题计算程序进行求解,生成混沌多项式的系数矩阵后,即可得到该随机问题的各阶统计矩,从而实现参数随机场的不确定性分析。本文将该方法引进岩土工程材料参数随机场,将体积模量视为输入随机场,位移视为输出场,分别进行了弹性及塑性变形计算。结果表明该方法极大地降低了随机问题的求解难度,与MC法(Mento Carlo)相比,减少了运算消耗,提高了计算效率,具有明显的优越性。     

HIERARCHICAL STOCHASTIC FINITE ELEMENT METHOD FOR STRUCTURAL ANALYSIS

In this paper, the hierarchical approach is adopted for series representation of the stochastic nodal displacement vector using the hierarchical basis vectors, while the Karhunen-Lòeve series expansion technique is employed to discretize the random field into a set of random variables. A set of hierarchical basis vectors are defined to approximate the stochastic response quantities. The stochastic variational principle instead of the projection scheme is adopted to develop a hierarchical stochastic finite element method (HSFEM) for stochastic structures under stochastic loads. Simplified expressions of coefficients of governing equations and the first two statistical moments of the response quantities in the schemes of the HSFEM are developed, so that the time consumed for computation can be greatly reduced. Investigation in this paper suggests that the HSFEM yields a series of stiffness equations with similar dimensionality as the perturbation stochastic finite element method (PSFEM). Two examples are presented for numerical study on the performance of the HSFEM in elastic structural problems with stochastic Young’s Modulus and external loads. Results show that the proposed method can achieve higher accuracy than the PSFEM for cases with large coefficients of variation, and yield results agreeing well with those obtained by the Monte Carlo simulation (MCS).     

复合材料层合板的谱随机无网格伽辽金法

针对基于摄动理论的计算方法不适用于分析随机结构参数大变异问题,提出谱随机无网格伽辽金法,该方法基于随机场正交分解理论,将随机场采用Karhunen-Loève级数展开为一系列不相关随机变量,再引入结构位移随机响应的混沌多项式分解,结合无网格伽辽金法,从而导出含随机变量的复合材料层合板的谱随机无网格伽辽金法,给出结构响应的统计特征值的计算公式,该方法既适用于随机参数大变异情况,又具有无网格法的优势;数值算例结果表明该方法是正确有效的。     

Unified Analysis of Complex Nonlinear Motions via Densities

A unified approach of using densities to analyze bothdeterministic and stochastic complex responses including chaotic andrandom motions of nonlinear engineering systems is illustrated in thisstudy. Motivations to examine deterministic nonlinear dynamical systemsvia densities are first discussed. Essential mathematical background andtechniques pertinent to the analyses of both deterministic chaos andrandom chaotic processes are briefly summarized. Densities of nonlinearresponses are computed by numerically solving the Fokker–Planckequation to examine stochastic properties of random chaotic responses.It is demonstrated that, by introducing random perturbations in anotherwise deterministic excitation, the existence of attractors can beefficiently and clearly depicted by the evolution of a uniqueprobability density over the physical phase space. Two distinctasymptotic behaviors of densities: (i) invariance and (ii) sweeping, ofcomplex motions and their relationship to response stabilities predictedby the Foguel Alternative Theorem are numerically demonstrated.Applications using the probability densities to compute reliabilityindices of an engineering system are demonstrated.     

The asymptotic stability analysis in stochastic logistic model with Poisson growth coefficient

The asymptotic stability of a discrete logistic model with random growth coefficient is studied in this paper. Firstly, the discrete logistic model with random growth coefficient is built and reduced into its deterministic equivalent system by orthogonal polynomial approximation. Then, the linear stability theory and the Jury criterion of nonlinear deterministic discrete systems are applied to the equivalent one. At last, by mathematical analysis, we find that the parameter interval for asymptotic stability of nontrivial equilibrium in stochastic logistic system gets smaller as the random intensity or statistical parameters of random variable is increased and the random parameter's influence on asymptotic stability in stochastic logistic system becomes prominent.     

Bifurcation and Chaos Analysis of Stochastic Duffing System Under Harmonic Excitations

The Chebyshev polynomial approximation is applied to the dynamic response problem of a stochastic Duffing system with bounded random parameters, subject to harmonic excitations. The stochastic Duffing system is first reduced into an equivalent deterministic non-linear one for substitution. Then basic non-linear phenomena, such as stochastic saddle-node bifurcation, stochastic symmetry-breaking bifurcation, stochastic period-doubling bifurcation, coexistence of different kinds of steady-state stochastic responses, and stochastic chaos, are studied by numerical simulations. The main feature of stochastic chaos is explored. The suggested method provides a new approach to stochastic dynamic response problems of some dissipative stochastic systems with polynomial non-linearity.     

Réduction a priori de modèles thermomécaniquesAn a priori model reduction method for thermomechanical problems

A model reduction method is proposed for finite element models. A previous computation of the state of the structure is not necessary. Residuals defined over the entire time interval and the Karhunen–Loève method provide basis functions. A non-incremental algorithm, from the LATIN method, is used to compute this basis functions. Because of the non-incremental feature, the reduced order model is representative for a large evolution of the state of the structure. To cite this article: D. Ryckelynck, C. R. Mecanique 330 (2002) 499–505.     

Feedback control of the Rayleigh–Bénard convection by means of mode reduction

An optimal feedback control is synthesized for the Rayleigh–Bénard convection by means of empirical reduction of modes. The Boussinesq equation is reduced to a minimal set of ordinary differential equations by using the Karhunen–Loève Galerkin procedure. The state feedback control synthesis, that drives the intensity of convection to a preset trajectory by adjusting heat flux at the bottom of the system, is constructed using this low‐dimensional dynamic model by first performing an extended Kalman filter estimate of the velocity and temperature fields and then developing the optimal feedback law by means of the linear regulator theory. The present technique allows for the practical implementation of modern control concepts to the natural convection and is found to yield satisfactory results. Copyright © 2002 John Wiley & Sons, Ltd.     

Eléments finis stochastiques en élasticité linéaireA stochastic finite element method in linear mechanics

The stochastic finite element method presented in this Note consists in representing in a probabilistic form the response of a linear mechanical system whose material properties and loading are random. Each input random variable is expanded into a Hermite polynomial series in standard normal random variables. The response (e.g., the nodal displacement vector) is expanded onto the so-called polynomial chaos. The coefficients of the expansion are obtained by a Galerkin-type method in the space of probability. To cite this article: B. Sudret et al., C. R. Mecanique 332 (2004).     

含有界随机参数的双势井Duffing-Van der pol系统的对称破裂分岔

讨论谐和激励作用下含有界随机参数的双势井Duffing-Van der pol系统的对称破裂分岔现象。首先用Chebyshev多项式逼近法将随机系统化成与其等价的确定性系统,然后通过等价确定性系统来探索随机Duffing-Van der pol系统的对称破裂分岔现象。数值模拟显示随机Duffing-Van der pol系统与确定性均值参数系统有着类似的对称破裂分岔行为,文中的主要数值结果表明Chebyshev多项式逼近法是研究非线性随机参数系统动力学问题的一种有效方法。