基于分位点的全局灵敏度及其求解方法

为探讨输入变量在随机不确定性环境下对产品的分位点(产品满足一定概率要求的输出性能的上限值)的影响,定义了基于分位点的输入变量的全局灵敏度分析(GSA)指标.该指标能够在给定的概率要求下全面衡量输入变量在其分布域中变化时对输出性能分位点的平均影响程度.揭示了该指标与已有的基于分布函数的全局灵敏度指标和基于失效概率的全局灵敏度指标的内在联系,并利用维度缩减方法和基于分数阶矩的极大熵算法以及Nataf变换来高效求解所提指标.通过数值和工程算例说明了基于分位点的全局灵敏度指标的物理意义,并验证了求解方法的精度和效率.     

石英纤维/环氧树脂复合材料结构静强度的可靠度计算及全局灵敏度分析

以石英纤维/环氧树脂复合材料结构为研究对象,考虑设计参数的随机性,采用全局灵敏度分析理论,研究了各输入随机因素对石英纤维/环氧树脂复合材料结构静强度响应的影响。首先利用MATLAB和NASTRAN的联合仿真得到各输入变量样本值对应的输出响应值,结合自适应Kriging模型构建极限状态函数的代理模型,在此基础上实现石英纤维/环氧树脂复合材料结构静强度可靠度及各输入变量的不确定性对输出响应及失效概率全局灵敏度的计算,得到输入变量的全局灵敏度排序结果,为工程实际中复合材料结构的优化设计提供一定指导。      

基于改进混沌粒子群的混合神经网络和蒙特卡洛法的结构随机灵敏度分析方法

针对不确定性参数对结构力学性能的随机影响,该文利用混合神经网络良好的小样本学习和泛化能力构建结构响应复杂的函数关系,采用改进的混沌粒子群算法优化网络寻址结构。结合蒙特卡洛法对结构进行随机性分析,并根据该文提出的新的灵敏度度量参数计算随机变量的全局灵敏度系数。通过数学算例和工程算例验证了所提方法的可行性,且结构响应的概率分布曲线也可以真实的反应实际情况。同时,利用该文所提出的随机灵敏度计算方法可以更好的反应各随机变量对结构响应的相关性和敏感性。     

含非闭合隶属函数模糊变量的结构失效概率分布研究

结合工程实际,提出了非闭合隶属函数的截断可能性分布模型,并对模糊强度和模糊应力进行截断处理,给出了结构模糊随机失效概率随截断参数的分布,并给出了结构模糊随机失效概率分布的数值计算方法。所提出的方法不仅可以考虑基本变量的随机模糊性,而且可以考虑安全和失效状态的随机模糊性。关于强度和应力两个基本变量的情况易于推广应用到多个变量的情况,以解决多变量体系中含有非闭合隶属函数模糊变量的安全分析问题。     

大跨屋盖结构风振响应参数灵敏度分析

对典型大跨屋盖结构风振响应开展参数灵敏度研究,目的是定量评估各种不确定因素对结构风振响应不确定性的贡献率,获得不确定性在风荷载与风振响应间的传递规律。首先结合Sobol'方差分解法和拉丁超立方抽样技术建立适用于大跨屋盖结构的全局灵敏度分析方法,通过多次采样风洞试验获得大量脉动风荷载时程,作为灵敏度分析的输入变量。合理建立结构参数概率统计模型,分别应用局部和全局灵敏度分析方法对典型大跨屋盖结构极值风振响应进行了参数灵敏度分析,研究发现:多个参数共同随机变化时,结构极值风振响应近似服从广义极值分布;结构风振响应的不确定性主要受风荷载不确定性控制;结构风振响应的参数灵敏度与共振响应在总响应中的比重有关,共振响应占比越大,结构对风荷载越敏感。     

灵敏度的模态区间分析方法及其在不确定性参数识别中的应用EI北大核心CSCD

由于存在区间过估计及无法获知结构响应表达式的问题,区间灵敏度分析方法难以广泛地应用于实际复杂结构中。为此,提出一种基于响应面的灵敏度模态区间分析方法,该方法在区间响应面模型上分别对每个区间参数进行模态区间扩张得到响应区间,进而计算相对模态区间灵敏度,通过比较相对模态区间灵敏度即可判断结构响应对参数的敏感程度。通过数值算例探讨响应面形式对计算结果的影响,阐述灵敏度区间分析与灵敏度模态区间分析的优缺点。最后以钢板试验及钢筋混凝土拱桥不确定性参数识别算例来验证所提方法在复杂结构分析中的可行性。灵敏度分析结果表明该方法有效地解决区间过估计问题,提高了灵敏度分析的精度。对参数在多个范围内的灵敏度分析,所提方法具有较高的计算效率。参数识别结果表明将逆响应面与模态区间分析结合可避免区间优化过程,在保证精度的前提下,提高了参数识别效率。     

基于失效概率积分和马尔可夫链模拟的可靠性灵敏度分析

可靠性灵敏度可以被表达为失效概率对基本随机变量分布参数的偏导数的形式,利用失效概率为基本变量的联合概率密度函数在失效域上的积分表达式,并且利用马尔可夫链能够高效模拟感兴趣区域样本的性质,一种针对单个失效模式和系统多个失效模式的可靠性灵敏度分析方法被提出。由于可靠性参数灵敏度可以表达为一个与联合概率密度函数相关的函数在失效域中的数学期望的形式,所提方法采用马尔可夫链来高效模拟失效域中的样本,进而采用样本均值替代总体均值的方法来得到可靠性灵敏度的估计值。与已有的基于Monte-Carlo模拟的可靠性灵敏度分析方法相比,所提方法在保证计算精度的基础上计算效率有显著提高,尤其是针对小失效概率的可靠性灵敏度分析问题。该算例充分说明了所提方法的合理可行性。     

随机激励下的随机结构全局灵敏度分析

为了考察处于随机激励下的结构系统的随机不确定性输入参数对结构动力响应的影响,提出了基于动力响应均值的全局灵敏度指标和基于动力响应方差的全局灵敏度指标。结合这两个指标对随机结构在随机激励下的响应进行灵敏度分析,能够对输入参数的重要性作出合理评估,以便为改善结构设计提供有益指导。为求解所提出的全局灵敏度指标,提出了一种基于点估计的高效方法。该方法将所提出的动力响应灵敏度指标视为均值和方差算子的嵌套,通过嵌套使用点估计的方法进行分解降维。通过对某发动机管路系统的算例分析,验证了上述全局灵敏度指标及其求解方法的工程适用性。     

不确定条件下复合材料结构的全局灵敏度分析

基于不确定条件下结构的全局灵敏度分析理论,研究了输入变量的不确定性对复合材料结构输出响应量方差和失效概率的影响。考虑材料力学性能、铺设角、铺层厚度及加载载荷的不确定性,利用基于方差和基于失效概率的全局灵敏度分析方法,对复合材料结构输出位移和强度比的不确定性来源进行分析,得到输入变量的全局灵敏度排序结果。对复合材料工字梁结构进行算例分析,验证了所得排序结果的有效性,为工程实际中复合材料结构稳定性优化设计提供了一定的指导。     

分数阶导数系统非平稳随机振动灵敏度分析的时域显式方法EI北大核心CSCD

分数阶导数模型是描述黏弹性材料本构关系的理想模型。进行了分数阶导数线性系统非平稳随机振动的灵敏度分析。建立分数阶导数系统动力响应的时域显式表达式;采用灵敏度分析的直接求导法或伴随变量法,推导系统动力响应灵敏度的时域显式表达式;提出分数阶导数系统响应统计矩灵敏度高效计算的时域显式方法。所提出的基于直接求导法和伴随变量法的时域显式方法,分别适用于少设计变量和多设计变量两种情况下的响应统计矩灵敏度分析。以非平稳地震激励下设置分数阶导数黏弹性阻尼器的层剪切结构为数值算例,验证了所提方法的计算精度和计算效率。     

Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.     

Time-variant reliability global sensitivity analysis with single-loop Kriging model combined with importance sampling

This paper develops a novel failure probability-based global sensitivity index by introducing the Bayes formula into the moment-independent global sensitivity index to approximate the effect of input random variables or stochastic processes on the time-variant reliability. The proposed global sensitivity index can estimate the effect of uncertain inputs on the time-variant reliability by comparing the difference between the unconditional probability density function of input variables and the conditional probability density function in failure state of input variables. Furthermore, a single-loop active learning Kriging method combined with metamodel-based importance sampling is employed to improve the computational efficiency. The accuracy of the results obtained by Kriging model is verified by the reference results provided by the Monte Carlo simulation. Four examples are investigated to demonstrate the significance of the proposed failure probability-based global sensitivity index and the effectiveness of the computational method.     

Uncertainty and sensitivity analysis for models with correlated parameters

When conducting sensitivity and uncertainty analysis, most of the global sensitivity techniques assume parameter independence. However, it is common that the parameters are correlated with each other. For models with correlated inputs, we propose that the contribution of uncertainty to model output by an individual parameter be divided into two parts: the correlated contribution (by the correlated variations, i.e. variations of a parameter which are correlated with other parameters) and the uncorrelated contribution (by the uncorrelated variations, i.e. the unique variations of a parameter which cannot be explained by any other parameters). So far, only a few studies have been conducted to obtain the sensitivity index for a model with correlated input. But these studies do not distinguish between the correlated and uncorrelated contribution of a parameter. In this study, we propose a regression-based method to quantitatively decompose the total uncertainty in model output into partial variances contributed by the correlated variations and partial variances contributed by the uncorrelated variations. The proposed regression-based method is then applied in three test cases. Results show that the regression-based method can successfully measure the uncertainty contribution in the case where the relationship between response and parameters is approximately linear.     

Global Sensitivity Analysis for Mixture Experiments

A mixture experiment is characterized by having two or more inputs that are specified as a percentage contribution to a total amount of material. In such situations, the input variables are correlated because they must sum to one. Consequently, additional care must be taken when fitting statistical models or visualizing the effect of one or more inputs on the response. In this article, we consider the use of a Gaussian process to model the output from a computer simulator taking a mixture input. We introduce a procedure to perform global sensitivity analysis of the code output providing main effects and revealing interactions. The resulting methodology is illustrated using a function with analytically tractable results for comparison, a chemical compositional simulator, and a physical experiment. Supplementary materials providing assistance with implementing this methodology are available online.     

Uncertainty-based sensitivity indices for imprecise probability distributions

An uncertainty-based sensitivity index represents the contribution that uncertainty in model input X_i makes to the uncertainty in model output Y. This paper addresses the situation where the uncertainties in the model inputs are expressed as closed convex sets of probability measures, a situation that exists when inputs are expressed as intervals or sets of intervals with no particular distribution specified over the intervals, or as probability distributions with interval-valued parameters. Three different approaches to measuring uncertainty, and hence uncertainty-based sensitivity, are explored. Variance-based sensitivity analysis (VBSA) estimates the contribution that each uncertain input, acting individually or in combination, makes to variance in the model output. The partial expected value of perfect information (partial EVPI), quantifies the (financial) value of learning the true numeric value of an input. For both of these sensitivity indices the generalization to closed convex sets of probability measures yields lower and upper sensitivity indices. Finally, the use of relative entropy as an uncertainty-based sensitivity index is introduced and extended to the imprecise setting, drawing upon recent work on entropy measures for imprecise information.     

模糊权重约束的DEA模型及其应用

基于Zadeh模糊优越集定义,应用模糊权重约束DEA模型对医院服务效率进行评价.通过最大化模糊隶属函数来确定投入与产出权重的上限和下限,以避免传统DEA模型中权重为0的缺陷.实例选取期末实有床位数和医护人员数为两个投入变量,总诊疗人次和总出院人次为两个产出变量,评价广州市15所医院的相对服务效率.评价结果对医院管理决策更具客观性和实用性.     

Second-level global sensitivity analysis of numerical simulators with application to an accident scenario in a sodium-cooled fast reactor

Numerical simulators are widely used to model physical phenomena and global sensitivity analysis (GSA) aims at studying the global impact of the input uncertainties on the simulator output. To perform GSA, statistical tools based on inputs/output dependence measures are commonly used. We focus here on the Hilbert–Schmidt independence criterion (HSIC). Sometimes, the probability distributions modeling the uncertainty of inputs may be themselves uncertain and it is important to quantify their impact on GSA results. We call it here the second-level global sensitivity analysis (GSA2). However, GSA2, when performed with a Monte Carlo double-loop, requires a large number of model evaluations, which is intractable with CPU time expensive simulators. To cope with this limitation, we propose a new statistical methodology based on a Monte Carlo single-loop with a limited calculation budget. First, we build a unique sample of inputs and simulator outputs, from a well-chosen probability distribution of inputs. From this sample, we perform GSA for various assumed probability distributions of inputs by using weighted HSIC measures estimators. Statistical properties of these weighted estimators are demonstrated. Subsequently, we define 2nd-level HSIC-based measures between the distributions of inputs and GSA results, which constitute GSA2 indices. The efficiency of our GSA2 methodology is illustrated on an analytical example, thereby comparing several technical options. Finally, an application to a test case simulating a severe accidental scenario on nuclear reactor is provided.     

Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

For fuzzy systems to be implemented effectively, the fuzzy membership function (MF) is essential. A fuzzy system (FS) that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output (SISO) FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output. Utilizing a variety of non-linear techniques, a SISO FS is simulated. The results of FS experiments conducted in comparable conditions are then compared. The simulated results and the results of the experimental setup agree fairly well. The findings of the suggested model demonstrate that the relative error is abated to a sufficient range (≤ ± 10%) and that the mean absolute percentage error (MPAE) is reduced by around 66.2%. The proposed strategy to reduce MAPE using an FS improves the system’s performance and control accuracy. By using the best input and output MFs protocol, the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs. The proposed fuzzy system performed far better than other modern days approaches available in the literature.     

A mathematical programming approach to supply chain models with fuzzy parameters

Supply chain (SC) models play an important role in supply chain management (SCM) for reducing costs and finding better ways to create and deliver value to customers. An approach to deriving the membership function of the fuzzy minimum total cost of the multi-product, multi-echelon, and multi-period SC model with fuzzy parameters is proposed in this article. On the basis of α-cut representation and the extension principle, a pair of mathematical programs are formulated to calculate the lower and upper bounds of the fuzzy minimum total cost at possibility level α. The membership function of the fuzzy minimum total cost is constructed by enumerating different values of α. To demonstrate the validity of the proposed procedure, a four-echelon five-period SC model with fuzzy parameters is solved successfully. Since the objective value is expressed by membership functions rather than by crisp values, they completely conserve the fuzziness of input information when some of the SC data are ambiguous. Thus the proposed approach can represent SCs with fuzzy parameters more accurately, and more information is provided for designing SCs in real-world applications.     

变量相关情况下方差贡献的参数影响分析

为控制输出方差需进行输入变量对输出方差的贡献分析, 而输入变量对输出方差的贡献由其分布参数决定, 因此研究输入变量的分布参数对方差贡献的影响具有重要意义。该文针对二次不含交叉项的多项式, 将相关变量按照一定的次序变换成独立的变量, 推导了各变量对输出方差主贡献和总贡献对分布参数的灵敏度的解析解, 得到了基本正态相关变量的分布参数对方差贡献的影响的一般规律, 并对这些规律进行了分析解释, 指出了所得规律的应用价值。最后应用数值算例和工程算例验证了所推得解析解的正确性和合理性。