Spectral stochastic analysis of structures with uncertain parameters

We present a probabilistic analysis of a structure with uncertain parameters subject to arbitrary stochastic excitations in a frequency domain. The problem of stochastic dynamic analysis of a linear system in a frequency domain is formulated by taking into consideration the uncertainty of structural parameters. The solution is based on the idea of a random frequency response vector for stationary input excitation and a transient random frequency response vector for nonstationary one which are used in the context of spectral analysis in order to determine the influence of structural uncertainty on the random response of structure. The numerical spectral analysis of the building structure under wind and earthquake excitation is provided to demonstrate the described algorithms in the context of computer implementation.     

Efficient spectral response of locally uncertain linear systems

It is difficult to model any real dynamical system with fully deterministic characteristics and yet, capture its behavior reasonably. Randomness arises from many sources, such as uncertain material properties, assumptions involved in structural modeling, and the stochastic nature of input forces. Thus, the random vibration analysis of systems with uncertain parameters is a crucial component of structural design and optimization procedures. In this paper, a new method is presented for fast spectral analysis of locally uncertain systems subjected to random inputs based on the response of one such system (called the nominal system). Unlike other methods, such as modal expansion methods, the proposed method is applicable to general uncertainties in the damping and stiffness matrices with the sole restriction that the system remains stable with probability one. Moreover, the proposed method yields exact responses for the perturbed systems and its accuracy is not affected by the size or magnitude of the uncertainties. However, the degree of locality of the uncertainty dictates the observed gains in computational efficiency when using the proposed method. When the uncertainty is extremely localized, one can expect gains in computational efficiency of two to three orders of magnitude while only modest gains of 2–3 times are observed when half the model is uncertain. Two numerical examples are presented to illustrate the accuracy and gains in computational efficiency of the proposed method.     

Non-Gaussian simulation of local material properties based on a moving-window technique

In this paper, a moving-window micromechanics technique, Monte Carlo simulation, and finite element analysis are used to assess the effects of microstructural randomness on the local stress response of composite materials. The randomly varying elastic properties are characterized in terms of a field of local effective elastic constitutive matrices using a moving-window technique based on a finite element model of a given digitized composite material microstructure. Once the fields are generated, estimates of the random properties are obtained for use as input to a simulation algorithm that was developed to retain spectral, correlation, and non-Gaussian probabilistic characteristics. Rapidly generated Monte Carlo simulations of the constitutive matrix fields are used in a finite element analysis to create a series of local stress fields associated with the random material sample under uniaxial tension. This series allows estimation of the statistical variability in the local stress response for the random composite. The identification of localized extreme stress deviations from those of the aggregate or effective properties approach highlight the importance of modeling the stochastic variability of the microstructure.     

Identification of stochastic loads applied to a non-linear dynamical system using an uncertain computational model and experimental responses

The paper is devoted to the identification of stochastic loads applied to a non-linear dynamical system for which experimental dynamical responses are available. The identification of the stochastic load is performed using a simplified computational non-linear dynamical model containing both model uncertainties and data uncertainties. Uncertainties are taken into account in the context of the probability theory. The stochastic load which has to be identified is modelled by a stationary non-Gaussian stochastic process for which the matrix-valued spectral density function is uncertain and is then modelled by a matrix-valued random function. The parameters to be identified are the mean value of the random matrix-valued spectral density function and its dispersion parameter. The identification problem is formulated as two optimization problems using the computational stochastic model and experimental responses. A validation of the theory proposed is presented in the context of tubes bundles in Pressurized Water Reactors.     

On the relationship of the cumulative jump model for random fatigue to empirical data

The cumulative jump model, consisting of a random sum of random increments, has previously been proposed, in a general format, to model the fatigue crack growth process. In this paper the cumulative jump process for random fatigue is used to model the constant-load amplitude Virkler fatigue crack growth data. It is shown, through the proper choice of the intensity function of the underlying birth process, that the mean crack growth behavior of the model may be specified to match any desired functional form. This assures reasonable agreement with experiments. For fatigue crack growth the intensity function is characterized by a constant and a random variable (this makes the underlying birth process a so-called doubly stochastic counting process). For the case of the ‘simplified' jump model (constant elementary crack increments), the constant and the random variable characterizing the intensity function may be estimated by matching approximate formulae for the mean and the variance of the model with the data. Simulations of the jump model show trajectories which behave qualitatively like the data and yield distribution functions for the crack length which match well the data.     

A study of stochastic fatigue crack growth modeling through experimental data

To capture the statistical nature of fatigue crack growth, many stochastic models have been proposed in the literature. These models may have been verified by only one data set, and therefore not appreciated by other fellow researchers. Part of the reason is the difficulty and time-consuming in obtaining the statistically meaningful fatigue crack growth data. In the present study, experimental work is carried out to obtain the fatigue crack growth data of a batch of 2024-T351 aluminum alloy specimens. A rather universal stochastic fatigue crack growth model proposed by Yang and Manning is employed to analyze the data. The solution of the stochastic differential equation associated with the stochastic model gives us the crack exceedance probability as well as the probability of random time to reach a specified crack size. Through comparison between the analytical and experimental results, it is found the model with a minor modification can fit the experimental data rather well. Once the appropriate stochastic model is established, it can be used for the fatigue reliability prediction of structures made of the tested material. In the present study, in particular, it can be used for the reliability assessment of aging aircraft made of 2024-T351 aluminum alloy.     

2.5维C/SiC复合材料弹性参数不确定性识别方法研究

提出了一种2.5维C/SiC编织复合材料弹性参数不确定性识别方法。采用刚度平均法获得复合材料等效弹性参数理论预测值。选取对结构动态特性影响较大的3个弹性参数E11,E22和G12作为待识别参数;在确定性识别结果基础上,采用拉丁超立方体采样构造随机试验样本,开展不确定性参数识别方法仿真研究。仿真结果表明,针对考虑弹性参数不确定性的2.5维C/SiC复合材料,采用所提出的方法能够准确识别材料弹性参数的均值与标准差,建立反映实际结构动态特性统计意义的精确动力学模型。     

Uncertainty quantification in dynamic system risk assessment: a new approach with randomness and fuzzy theory

Quantifying uncertainty during risk analysis has become an important part of effective decision-making and health risk assessment. However, most risk assessment studies struggle with uncertainty analysis and yet uncertainty with respect to model parameter values is of primary importance. Capturing uncertainty in risk assessment is vital in order to perform a sound risk analysis. In this paper, an approach to uncertainty analysis based on the fuzzy set theory and the Monte Carlo simulation is proposed. The question then arises as to how these two modes of representation of uncertainty can be combined for the purpose of estimating risk. The proposed method is applied to a propylene oxide polymerisation reactor. It takes into account both stochastic and epistemic uncertainties in the risk calculation. This study explores areas where random and fuzzy logic models may be applied to improve risk assessment in industrial plants with a dynamic system (change over time). It discusses the methodology and the process involved when using random and fuzzy logic systems for risk management.     

Quantification of uncertainty modelling in stochastic analysis of FRP composites

The extensive use of FRP composite materials in a wide range of industries, and their inherent variability, has prompted many researchers to assess their performance from a probabilistic perspective. This paper attempts to quantify the uncertainty in FRP composites and to summarise the different stochastic modelling approaches suggested in the literature. Researchers have considered uncertainties starting at a constituent (fibre/matrix) level, at the ply level or at a coupon or component level. The constituent based approach could be further classified as a random variable based stochastic computational mechanics approach (whose usage is comparatively limited due to complex test data requirements and possible uncertainty propagation errors) and the more widely used morphology based random composite modelling which has been recommended for exploring local damage and failure characteristics. The ply level analysis using either stiffness/strength or fracture mechanics based models is suggested when the ply characteristics influence the composite properties significantly, or as a way to check the propagation of uncertainties across length scales. On the other hand, a coupon or component level based uncertainty modelling is suggested when global response characteristics govern the design objectives. Though relatively unexplored, appropriate cross-fertilisation between these approaches in a multi-scale modelling framework seems to be a promising avenue for stochastic analysis of composite structures. It is hoped that this review paper could facilitate and strengthen this process.     

Effective properties of linear random materials: application to Al/SiC and resin/glass composites

Here we present a statistical study on the effective linear properties of random materials, i.e., microstructures which are random lattices described by a stochastic process. The local numerical procedure associated with the homogenization methods used here was based on a wavelet-element method. The resulting numerical results are compared with those obtained using classical theories. A new approach was developed in order to determine the effective properties in cases where the characteristics of the microstructure are not known.     

智能仪器测量信号功率的不确定度评定模型

针对基于交流采样原理的智能仪器，提出一种新的测量不确定度评定模型。以信号功率测量为例，受硬件条件以及信号频率波动的影响，无法确保同步采样，利用已有测量算法将使测量结果出现误差。将该误差视为系统效应，通过近似处理，提出简单且实用的修正算法。将测量过程中的量化噪声、信号传输中的干扰当作具有已知分布特征的随机变量，利用统计方法，并依据测量不确定度传播定律，评定了经修正算法修正后的测量结果的不确定度。这种先修正系统效应、再评定随机因素造成不确定度的模型，更符合测量过程的实际情况。物理实验和仿真计算均验证了所得结论的有效性。     

Uncertainty in analyses of one-dimensional steady-state seepage through random porous media

The aim of this paper is to assess the propagation of uncertainty in analyses of one-dimensional steady-state flow through random porous media. It is considered that uncertainty originates from lack of precise knowledge of soil hydraulic conductivity. This uncertainty is modelled by means of random variables. Taking into account experimental evidences, it is accepted that hydraulic conductivity has a log-normal probability distribution. The paper focuses on propagation of uncertainty from hydraulic conductivity to the computed flow rate through homogeneous and stratified random materials. The most common techniques available to evaluate propagation of uncertainty are briefly reviewed. The applicability and limitations of these techniques are assessed. Parametric studies to gauge the effect of uncertainty on soil hydraulic conductivity on the output statistics of flow rate are performed. Attenuation of uncertainty on flow rate as the number of materials in stratified soils increases is evaluated. Conclusions are presented regarding this attenuation and the usefulness of the different stochastic techniques employed, which proved to be more complementary than antagonistic.     

Stochastic data assimilation of the random shallow water model loads with uncertain experimental measurements

This paper is concerned with the estimation of a parametric probabilistic model of the random displacement source field at the origin of seaquakes in a given region. The observation of the physical effects induced by statistically independent realizations of the seaquake random process is inherent with uncertainty in the measurements and a stochastic inverse method is proposed to identify each realization of the source field. A statistical reduction is performed to drastically lower the dimension of the space in which the random field is sought and one is left with a random vector to identify. An approximation of the vector components is determined using a polynomial chaos decomposition, solution of an optimality system to identify an optimal representation. A second order gradient-based optimization technique is used to efficiently estimate this statistical representation of the unknown source while accounting for the non-linear constraints in the model parameters. This methodology allows the uncertainty associated with the estimates to be quantified and avoids the need for repeatedly solving the forward model.     

Reduced chaos expansions with random coefficientsin reduced‐dimensional stochastic modeling of coupled problems

We address the curse of dimensionality in methods for solving stochastic coupled problems with an emphasis on stochastic expansion methods such as those involving polynomial chaos expansions. The proposed method entails a partitioned iterative solution algorithm that relies on a reduced‐dimensional representation of information exchanged between subproblems to allow each subproblem to be solved within its own stochastic dimension while interacting with a reduced projection of the other subproblems. The proposed method extends previous work by the authors by introducing a reduced chaos expansion with random coefficients. The representation of the exchanged information by using this reduced chaos expansion with random coefficients enables an expeditious construction of doubly stochastic polynomial chaos expansions that separate the effect of uncertainty local to a subproblem from the effect of statistically independent uncertainty coming from other subproblems through the coupling. After laying out the theoretical framework, we apply the proposed method to a multiphysics problem from nuclear engineering. Copyright © 2013 John Wiley & Sons, Ltd.     

Non-linear response statistics of composite laminates with random material properties under random loading

Laminated composite plates find extensive use in many engineering applications. Some of these incorporate large deflections that may not be in the linear range. The external loading may be random in nature. The laminate material properties show an inherent dispersion around a mean value. In this paper the static response of laminated composite flat plates to transverse random loading has been studied. The material properties have been taken as random variables for accurate prediction of the system behaviour. The basic formulation of the problem has been developed based on the classical laminate theory and the Von-Karman non-linear strain–displacement relationship. A first order perturbation technique has been used to obtain the second order response statistics. Typical results have been presented for a plate with all edges simply supported. A comparison has been drawn with Monte Carlo simulation results for validation of the proposed approach. The effects of side-to-thickness ratio, aspect ratio and change in standard deviation of input random variables have been investigated for cross-ply symmetric and anti-symmetric laminates.     

Model selection for a class of stochastic processes or random fields with bounded range

Methods are developed for finding an optimal model for a non-Gaussian stationary stochastic process or homogeneous random field under limited information. The available information consists of: (i) one or more finite length samples of the process or field; and (ii) knowledge that the process or field takes values in a bounded interval of the real line whose ends may or may not be known. The methods are developed and applied to the special case of non-Gaussian processes or fields belonging to the class of beta translation processes. Beta translation processes provide a flexible model for representing physical phenomena taking values in a bounded range, and are therefore useful for many applications. Numerical examples are presented to illustrate the utility of beta translation processes and the proposed methods for model selection.     

Simulation of random fields on random domains

This paper focuses on the simulation of random fields on random domains. This is an important class of problems in fields such as topology optimization and multiphase material analysis. However, there is still a lack of effective methods to simulate this kind of random fields. To this end, we extend the classical Karhunen–Loève expansion (KLE) to this class of problems, and we denote this extension as stochastic Karhunen–Loève expansion (SKLE). We present three numerical algorithms for solving the stochastic integral equations arising in the SKLE. The first algorithm is an extension of the classical Monte Carlo simulation (MCS), which is used to solve the stochastic integral equation on each sampled domain. However, such approach demands remeshing each sampled domain and solving the corresponding integral equation, which can become computationally very demanding. In the second algorithm, a domain transformation is used to map the random domain into a reference domain, and only one mesh for the reference domain is required. In this way, remeshing different sample realizations of the random domain is avoided and much computational effort is thus saved. MCS is then adopted to solve the corresponding stochastic integral equation. Further, to avoid the computational effort of MCS, the third algorithm proposed in this contribution involves a reduced-order method to solve the stochastic integral equation efficiently. In this third algorithm, stochastic eigenvectors are represented as a sum of products of unknown random variables and deterministic vectors, where the deterministic vectors are efficiently computed by solving deterministic eigenvalue problems. The random variables and stochastic eigenvalues that appear in this third algorithm are calculated by a reduced-order stochastic eigenvalue problem constructed by the obtained deterministic vectors. Based on the obtained stochastic eigenvectors, the target random field is then simulated and reformulated as a classical KLE-like representation. Finally, three numerical examples are presented to demonstrate the performance of the proposed methods.