多变量函数统计矩点估计法的性能比较

多变量函数的统计矩估计是随机系统分析和可靠度分析中较为普遍的问题,点估计法则是解决这类问题的最为简单、高效的途径。为便于点估计法在实际工程中的合理应用,该文试图通过详细、系统的算例分析,对几类典型点估计法的计算性能展开讨论。通过二次函数和混合函数在多种变量工况下的低阶矩估计的精度比较研究,可以发现:1)点估计方法对高阶矩估计的精度较低阶矩低;2)函数非线性程度、变量类型和变异系数等对点估计法精度均有较为明显的影响,变量数目和相关系数的影响因方法而异;3)相对而言,Zhao&Ono方法精度最优,但用于强非线性、大变异性情形时,精度亦不甚理想,此时应慎用或者增加计算点的数量;4)Harr方法的计算精度在相关系数等于0处存在突变。     

单变量函数统计矩的点估计法性能比较

点估计法是随机系统响应量统计矩计算的方法之一,由于简单、高效而颇受关注,其中单变量函数的统计矩估计则是点估计法的基础.虽然各研究者对各自提出的点估计方法均进行了算例验证,但这些算例验证的普适性值得商榷.该文通过详细、系统的研究,对已有的单变量函数统计矩的点估计方法进行全面的影响因素分析和计算性能评价.大量的算例分析结果表明:1) 函数的非线性程度、随机变量的类型及其变异系数是点估计算法精度的主要影响因素,变量均值影响较小,且本质上是通过改变函数的非线性程度间接影响精度;2) Zhou & Nowak方法(5 个计算点)精度最优;3) 当函数非线性程度较强、变量变异系数较大时,各方法精度均不够理想,此时应慎用点估计法.     

概率信息不完全系统的统计矩估计方法

根据已知变量概率信息的不同,概率信息不完全系统可分为子类I、子类II和子类III。现有的统计矩点估计法可以方便地用于概率信息完全系统和概率信息不完全系统子类I,但是对可能出现的概率信息不完全系统子类II和子类III无能为力。为此,该文在重点研究子类III的等效相关系数求解方法的同时给出了子类II等效相关系数的简化方法,并发展了适用于一般概率信息不完全系统的广义Nataf变换;在此基础上,结合多变量函数的单变量降维近似模型,提出了概率信息不完全系统的统计矩估计方法,并讨论了参考点选择、变量排序等对计算效率的影响;最后,通过算例对建议方法进行了系统的验证。算例结果表明:该文建议的等效相关系数求解方法准确有效、变量排序策略切实可行,统计矩估计法具有广泛适用性且对于低阶矩具有较理想的精度。     

基于双变量降维模型和Kriging近似的统计矩点估计法

对复杂随机系统进行统计矩分析时,双变量降维近似模型一定程度上可以缓解“维数灾难”。但当系统维数较高时,双变量分量函数较多,计算量仍然较大。为此,该文将降维近似和Kriging代理模型有机结合起来,提出了一类高效、合理的改进点估计法。充分考虑函数逼近和数值积分中积分点的特点,提出了“米”字形的选点策略,并基于此发展了双变量分量函数的Kriging近似模型;将此近似模型用于原函数和矩函数的双变量降维近似模型中双变量分量函数的近似,分别建立了基于原函数近似和矩函数近似的统计矩改进点估计法;通过多个算例对该文提出方法进行了效率和精度的分析。算例分析结果表明:基于“米”字形选点策略的双变量分量函数的Kriging近似具有较高的精度;相比于已有的基于双变量降维近似模型的统计矩点估计法,建议方法仅需较少的结构分析即可达到与已有方法相当的精度,能更好地体现精度和效率的平衡。     

基于矩方法的特高压输电塔抗风可靠度分析

该文研究特高压输电塔抗风可靠度。基于等效随机静风荷载模型,引入矩方法分析特高压输电塔的抗风体系可靠度。该方法基于等价极值事件,利用统计矩点估计法求解得到等价极值变量前四阶统计矩信息后,即可方便地获得特高压输电塔体系可靠度指标及相应失效概率。1000kV级特高压SZT2钢管塔的数值算例分析表明:1) 矩方法简单、高效,将其运用于特高压输电塔抗风可靠度分析切实可行,具有重要的理论意义和工程实用价值;2) 在采用点估计法进行等价极值变量统计矩估计时,所取估计点数目应依据失效概率收敛情况而定,并非任意选取或取得越多越好。     

基于改进统计矩点估计法和最大熵原理的结构整体可靠度分析

结构整体可靠度评估一直以来是结构可靠度领域研究的热点与难点。该文将结构整体可靠度分类,并给出其对应功能函数的统一描述;结合提出的有效维度两步分析法和共轭无迹变换法,发展了改进统计矩点估计法;结合最大熵原理和改进统计矩点估计法,提出了适用于两类结构整体可靠度的统一分析方法;通过2个数值算例对该文方法进行了验证。算例分析结果表明:同一精度水平下,该文方法的计算效率较传统的三变量降维近似统计矩点估计法高2.3倍~2.6倍;该文方法具有高的精度水平,其最大相对误差低于2%,适用于结构整体可靠度评估。     

基于扩展型共轭无迹变换的随机不确定性传播分析方法

响应的统计矩是描述随机结构系统响应的主要方式之一,相对于响应的概率密度函数,结构响应的统计矩能够较容易获取,因而颇受研究人员的关注,而其中结构响应统计矩的高效计算方法一直是研究的热点。该文以可兼顾精度与效率的共轭无迹变换方法为基础,通过引入正态-非正态变换,发展了可适用于涉及任意随机变量分布类型统计矩估计的第Ⅰ类扩展型共轭无迹变换方法;将第Ⅰ类扩展型共轭无迹变换方法与高维分解模型相结合,发展了可适用于任意维度随机系统统计矩估计的第Ⅱ类扩展型共轭无迹变换方法;通过3个数值算例对建议方法进行了验证。算例分析结果表明:建议的两类方法均可以在拓展共轭无迹变换方法适用范围的基础上兼顾计算精度和效率;对于低维和高维问题,分别建议采用第Ⅰ类和第Ⅱ类扩展型共轭无迹变换方法进行响应统计矩估计。     

利用矩点估计法简化响应面可靠度指标的计算

针对响应面可靠度指标计算方法的缺陷,将罗森布鲁斯统计矩点估计法引入到经典响应面方法中对其进行改进。改进后响应面方法,考虑基本随机变量偏态情况,改变了模拟试验中随机变量抽样值的计算方法;直接采用了统计矩计算可靠度指标,使可靠度指标的计算在真实极限状态曲面的某些特殊点上进行;在计算过程中不需拟合近似极限状态曲面和对非线性方程进行线性化处理,不产生迭代误差和累积误差,计算过程简洁明了。最后分别利用改进的响应面方法和经典响应面方法分析了某一矿山大型工程的稳定可靠性,并以蒙特卡洛法计算结果作为准精确解进行了比较,计算结果满足工程要求。     

随机结构随机激励下的响应灵敏度分析

对于随机激励下随机结构动力响应的灵敏度分析问题,提出基于点估计法的随机结构随机动力响应灵敏度分析方法.所提方法从随机结构响应均方值的均值表达式出发,首先将随机结构响应均方值的均值对基本随机变量分布参数灵敏度转化成求期望值问题,再由求解随机变量函数矩的点估计方法导出求解动力响应灵敏度的计算公式.算例分析表明该方法的计算结果是合理的,并且由于点估计法具有较高的效率和精度,因而所提方法具有一定的工程意义.     

失效相关的齿轮一次四阶矩可靠性分析

齿轮是机械传动的关键零件之一,为了分析其可靠性,在改进的验算点一次二阶矩可靠性方法基础上,应用Taylor级数展开和Hermite多项式近似等方法,推导了齿轮功能函数的验算点前四阶矩,分析了具有齿根断裂和齿面点蚀两种主要相关失效模式的齿轮传动可靠性,给出了其相关系数和齿轮的可靠度。另一方面,因影响齿轮失效的因素繁杂众多,计算齿轮的可靠度时,不管是用一次二阶矩可靠性方法还是四阶矩可靠性方法,其计算量均偏大,且容易出错。针对该问题,提出了一种分类变差系数验算点一次四阶矩可靠性分析方法,该方法对齿轮功能函数的不同基本随机变量进行分类综合,减少了设计变量,使计算量明显减小,解决了用矩方法分析结构可靠性时计算量偏大且易出错的难题。最后应用所提分类变差系数验算点一次四阶矩可靠性分析法估计了某车床主轴箱一对传动齿轮的可靠性,计算结果显示该对齿轮齿根弯曲疲劳强度和齿面接触疲劳强度有一定的相关性,所用一次四阶矩方法因包含偏度、峰度等更高阶的统计信息可进一步提高估计精度。     

Stochastic sensitivity analysis using HDMR and score function

Probabilistic sensitivities provide an important insight in reliability analysis and often crucial towards understanding the physical behaviour underlying failure and modifying the design to mitigate and manage risk. This article presents a new computational approach for calculating stochastic sensitivities of mechanical systems with respect to distribution parameters of random variables. The method involves high dimensional model representation and score functions associated with probability distribution of a random input. The proposed approach facilitates first-and second-order approximation of stochastic sensitivity measures and statistical simulation. The formulation is general such that any simulation method can be used for the computation such as Monte Carlo, importance sampling, Latin hypercube, etc. Both the probabilistic response and its sensitivities can be estimated from a single probabilistic analysis, without requiring gradients of performance function. Numerical results indicate that the proposed method provides accurate and computationally efficient estimates of sensitivities of statistical moments or reliability of structural system.     

A univariate dimension-reduction method for multi-dimensional integration in stochastic mechanics

This paper presents a new, univariate dimension-reduction method for calculating statistical moments of response of mechanical systems subject to uncertainties in loads, material properties, and geometry. The method involves an additive decomposition of a multi-dimensional response function into multiple one-dimensional functions, an approximation of response moments by moments of single random variables, and a moment-based quadrature rule for numerical integration. The resultant moment equations entail evaluating N number of one-dimensional integrals, which is substantially simpler and more efficient than performing one N-dimensional integration. The proposed method neither requires the calculation of partial derivatives of response, nor the inversion of random matrices, as compared with commonly used Taylor expansion/perturbation methods and Neumann expansion methods, respectively. Nine numerical examples involving elementary mathematical functions and solid-mechanics problems illustrate the proposed method. Results indicate that the univariate dimension-reduction method provides more accurate estimates of statistical moments or multidimensional integration than first- and second-order Taylor expansion methods, the second-order polynomial chaos expansion method, the second-order Neumann expansion method, statistically equivalent solutions, the quasi-Monte Carlo simulation, and the point estimate method. While the accuracy of the univariate dimension-reduction method is comparable to that of the fourth-order Neumann expansion, a comparison of CPU time suggests that the former is computationally far more efficient than the latter.     

Stochastic sensitivity analysis by dimensional decomposition and score functions

This article presents a new class of computational methods, known as dimensional decomposition methods, for calculating stochastic sensitivities of mechanical systems with respect to probability distribution parameters. These methods involve a hierarchical decomposition of a multivariate response function in terms of variables with increasing dimensions and score functions associated with probability distribution of a random input. The proposed decomposition facilitates univariate and bivariate approximations of stochastic sensitivity measures, lower-dimensional numerical integrations or Lagrange interpolations, and Monte Carlo simulation. Both the probabilistic response and its sensitivities can be estimated from a single stochastic analysis, without requiring performance function gradients. Numerical results indicate that the decomposition methods developed provide accurate and computationally efficient estimates of sensitivities of statistical moments or reliability, including stochastic design of mechanical systems. Future effort includes extending these decomposition methods to account for the performance function parameters in sensitivity analysis.     

Novel computational methods for high‐dimensional stochastic sensitivity analysis

This paper presents three new computational methods for calculating design sensitivities of statistical moments and reliability of high‐dimensional complex systems subject to random input. The first method represents a novel integration of the polynomial dimensional decomposition (PDD) of a multivariate stochastic response function and score functions. Applied to the statistical moments, the method provides mean‐square convergent analytical expressions of design sensitivities of the first two moments of a stochastic response. The second and third methods, relevant to probability distribution or reliability analysis, exploit two distinct combinations built on PDD: the PDD‐saddlepoint approximation (SPA) or PDD‐SPA method, entailing SPA and score functions; and the PDD‐Monte Carlo simulation (MCS) or PDD‐MCS method, utilizing the embedded MCS of the PDD approximation and score functions. For all three methods developed, the statistical moments or failure probabilities and their design sensitivities are both determined concurrently from a single stochastic analysis or simulation. Numerical examples, including a 100‐dimensional mathematical problem, indicate that the new methods developed provide not only theoretically convergent or accurate design sensitivities, but also computationally efficient solutions. A practical example involving robust design optimization of a three‐hole bracket illustrates the usefulness of the proposed methods. Copyright © 2014 John Wiley & Sons, Ltd.     

An efficient approach for statistical moments estimation of structural response based on a novel adaptive hybrid dimension-reduction method

Statistical moments estimation is one of the main topics for the analysis of a stochastic system, but the balance among the accuracy, efficiency, and versatility for different methods of statistical moments estimation still remains a challenge. In this paper, a novel point estimate method (PEM) based on a new adaptive hybrid dimension-reduction method (AH-DRM) is proposed. Firstly, the adaptive cut-high-dimensional model representation (cut-HDMR) is briefly reviewed, and a novel AH-DRM is developed, where the high-order component functions of the adaptive cut-HDMR are further approximated by multiplicative forms of the low-order component functions. Secondly, a new point estimation method (PEM) based on the AH-DRM is proposed for statistical moments estimation. Finally, several examples are investigated to demonstrate the performance of the proposed PEM. The results show the proposed PEM has fairly high accuracy and good versatility for statistical moments estimation.     

A polynomial dimensional decomposition framework based on topology derivatives for stochastic topology sensitivity analysis of high-dimensional complex systems and a type of benchmark problems

In this paper, a new computational framework based on the topology derivative concept is presented for evaluating stochastic topological sensitivities of complex systems. The proposed framework, designed for dealing with high dimensional random inputs, dovetails a polynomial dimensional decomposition (PDD) of multivariate stochastic response functions and deterministic topology derivatives. On one hand, it provides analytical expressions to calculate topology sensitivities of the first three stochastic moments which are often required in robust topology optimization (RTO). On another hand, it offers embedded Monte Carlo Simulation (MCS) and finite difference formulations to estimate topology sensitivities of failure probability for reliability-based topology optimization (RBTO). For both cases, the quantification of uncertainties and their topology sensitivities are determined concurrently from a single stochastic analysis. Moreover, an original example of two random variables is developed for the first time to obtain analytical solutions for topology sensitivity of moments and failure probability. Another 53-dimension example is constructed for analytical solutions of topology sensitivity of moments and semi-analytical solutions of topology sensitivity of failure probabilities in order to verify the accuracy and efficiency of the proposed method for high-dimensional scenarios. Those examples are new and make it possible for researchers to benchmark stochastic topology sensitivities of existing or new algorithms. In addition, it is unveiled that under certain conditions the proposed method achieves better accuracies for stochastic topology sensitivities than for the stochastic quantities themselves.     

A generalized dimension‐reduction method for multidimensional integration in stochastic mechanics

A new, generalized, multivariate dimension‐reduction method is presented for calculating statistical moments of the response of mechanical systems subject to uncertainties in loads, material properties, and geometry. The method involves an additive decomposition of an N‐dimensional response function into at most S‐dimensional functions, where S?N; an approximation of response moments by moments of input random variables; and a moment‐based quadrature rule for numerical integration. A new theorem is presented, which provides a convenient means to represent the Taylor series up to a specific dimension without involving any partial derivatives. A complete proof of the theorem is given using two lemmas, also proved in this paper. The proposed method requires neither the calculation of partial derivatives of response, as in commonly used Taylor expansion/perturbation methods, nor the inversion of random matrices, as in the Neumann expansion method. Eight numerical examples involving elementary mathematical functions and solid‐mechanics problems illustrate the proposed method. Results indicate that the multivariate dimension‐reduction method generates convergent solutions and provides more accurate estimates of statistical moments or multidimensional integration than existing methods, such as first‐ and second‐order Taylor expansion methods, statistically equivalent solutions, quasi‐Monte Carlo simulation, and the fully symmetric interpolatory rule. While the accuracy of the dimension‐reduction method is comparable to that of the fourth‐order Neumann expansion method, a comparison of CPU time suggests that the former is computationally far more efficient than the latter. Copyright © 2004 John Wiley & Sons, Ltd.