Identification of stiffness,dissipation and input parameters of multi degree of freedom civil systems under unmeasured base excitations

A time domain dynamic identification technique based on a statistical moment approach has been formulated for civil systems under base random excitations in the linear state. This technique is based on the use of classically damped models characterized by a mass proportional damping. By applying the Itô stochastic calculus, special algebraic equations that depend on the statistical moments of the response can be obtained. These equations can be used for the dynamic identification of the mechanical parameters that define the structural model, in the case of unmeasured input as well, and the identification of the input itself. Furthermore, the above equations demonstrate the possibility of identifying the dissipation characteristics independently from the input characteristics. This paper discusses how the equations for solving the identification problem can be obtained and gives an application.     

Linear systems with polynomials of filtered Poisson processes

Moment equations are calculated exactly for the response of linear systems subjected polynomials of filtered Poisson processes. The Itô formula for stochastic differential equations driven by Poisson white noise is applied to derive moment equations. It is shown that the set of moment equations is closed. The proposed method is used to calculate moments up to the fourth order for the response of two linear systems subjected to quadratic forms of filtered Poisson processes. Results by Monte Carlo simulations are also presented for comparison.     

Response of linear dynamic systems to non-Erlang renewal impulses: Stochastic equations approach

Linear dynamical systems under random trains of impulses driven by a class of non-Erlang renewal processes are considered. The class considered is the one where the renewal events are selected from an Erlang renewal process. The original train of impulses is recast, with the aid of an auxiliary stochastic variable, in terms of two independent Poisson processes. Thus, by augmenting the state vector of the dynamic system with the auxiliary stochastic variables, the original non-Markov problem is converted to a Markov one.

The differential equations for the response statistical moments can then be derived from the generalised Ito's differential rule.

Numerical results obtained for a few different models and various sets of parameters, show that the present approach allows to account for a variety of inter arrival time's probability distributions. Transient mean value and variance of the response of a linear oscillator have been obtained from the equations for moments.     

Correlation-based phase space beam characterization

A generalized correlation-based definition for moments of arbitrary order is introduced that can also accommodate mixed spatial and angular moment. Moreover, a transformation law forthese moments for propagation through linear optical systems is derived. This law has the same form as the corresponding propagation law of the moments defined in terms of the Wigner distribution function. The correlation-based moments can be used to fully characterize beams of arbitrary states of coherence.     

Uncertainty propagation for deterministic models of biochemical networks using moment equations and the extended Kalman filter

Differential equation models of biochemical networks are frequently associated with a large degree of uncertainty in parameters and/or initial conditions. However, estimating the impact of this uncertainty on model predictions via Monte Carlo simulation is computationally demanding. A more efficient approach could be to track a system of low-order statistical moments of the state. Unfortunately, when the underlying model is nonlinear, the system of moment equations is infinite-dimensional and cannot be solved without a moment closure approximation which may introduce bias in the moment dynamics. Here, we present a new method to study the time evolution of the desired moments for nonlinear systems with polynomial rate laws. Our approach is based on solving a system of low-order moment equations by substituting the higher-order moments with Monte Carlo-based estimates from a small number of simulations, and using an extended Kalman filter to counteract Monte Carlo noise. Our algorithm provides more accurate and robust results compared to traditional Monte Carlo and moment closure techniques, and we expect that it will be widely useful for the quantification of uncertainty in biochemical model predictions.     

Estimation of biological properties by means of chromatographic systems: evaluation of the factors that contribute to the variance of biological-chromatographic correlations

The performance of chromatographic systems to emulate biological systems is evaluated in terms of the precision that can be achieved. The variance obtained when biological parameters are correlated against physicochemical ones can be decomposed in three terms: the variance of the biological data, the variance of the physicochemical data, and the variance caused by the dissimilarity between the two correlated systems (biological and physicochemical). The three terms contribute to the overall variance observed when measurements in chromatographic systems are correlated with experimental biological properties. The Abraham linear free energy relationships (LFERs) provide a very good approach to characterize biological and physicochemical systems and thus the variance of the analyzed data and the similarity/dissimilarity between them. The contribution of the three variances to the precision of the biological parameter estimated in this way is evaluated from the characterization of the biological and chromatographic systems by means of the Abraham model. The proposed method is able to estimate the goodness of chromatographic systems to predict particular biological properties. In particular, this method is illustrated by comparison of toxicity data (-log LC(50)) for the fish fathead minnow with retention data (log k) in several micellar electrokinetic chromatography (MEKC) systems and also by correlations between retention data (log k) in the sodium taurocholate (STC) MEKC system and data of several biological systems.     

New moment equations for chromatography using various stationary phases of different structural characteristics

New moment equations were systematically developed for chromatography using various types of separation media having different structural characteristics, i.e., shape (spherical particle, cylindrical fiber, flat plate) and porous structure (full-porous, partially porous (pellicular), nonporous). First, a set of basic equations of the general rate model of chromatography representing the mass balance and the mass-transfer kinetics were analytically solved in the Laplace domain. Then, the moment equations in the real-time domain of the first absolute moment and the second central moment were derived from the analytical solution in the Laplace domain. The new moment equations were used for predicting the chromatographic behaviors of benzene in the hypothetical RPLC systems using the full-porous, partially porous (pellicular), and nonporous spherical particles as packing materials. The influence of the difference in their structure on the total performance of the three types of spherical particles as the separation media for the fast HPLC with a high efficiency was quantitatively evaluated from the viewpoints of the column efficiency, column back pressure, and sample retention strength. The framework of the new moment equations can provide not only the qualitative but also the quantitative information about the intrinsic characteristics of the chromatographic behaviors of various separation media having the different shapes and structures. This study is devoted to demonstrating the important advantage of the moment analysis strategy over the conventional plate theory and rate models of chromatography.     

Response cumulants of nonlinear systems subject to external and multiplicative excitations

A general framework is presented for deriving the differential equations governing the evolution of the response cumulants of linear and nonlinear dynamical systems subjected to external and multiplicative non-Gaussian delta-correlated processes. Significant simplifications of these equations are given based on using appropriate recursive relationships for joint cumulants involving products of one or more variables. A compact form of the equations for the response cumulants is presented which provides insight into the structure of the cumulant equations for specific types of dynamical systems. The procedure developed can easily be implemented in computer software to derive symbolic cumulant equations and to estimate numerically the response cumulants of systems with power-law nonlinearities using approximate cumulant-neglect closure schemes. Comparison between the equations for cumulants and the equations for moments are also presented, with particular emphasis on the advantages and disadvantages of each formulation. Suggestions are given regarding the choice to use cumulant or moment equations for analysing the stochastic response of dynamical systems. The preferred formulation is shown to depend on the type of system analysed (linear or nonlinear), the type of system nonlinearity (polynomial or non-polynomial), and the type of excitation (external or multiplicative, delta-correlated or filtered).     

Stochastic response analysis of nonlinear systems under Gaussian inputs

In this paper the extension of Itô's rule for the case of vector real valued functions of the response of nonlinear systems excited by zero-mean Gaussian white noise processes is presented. A suitable particularization of the vector function, in order to obtain the statistical moments of every order to the response, is treated, obtaining the differential equations of the response moments in an elegant and compact form. Polynomial expansion and closure schemes are framed in the context outlined here in order to obtain an effective procedure from a computational point of view. An application to a trigonometric nonlinear system, solved in the literature by the stochastic averaging method, is treated here by the moment equation approach using the polynomial expansion of the nonlinear terms in order to evidence the validity of this approach.     

A novel mean square formulation of stochastic nonlinear dynamic systems based on Adomian decomposition

An Adomian decomposition based mathematical framework to derive the mean square responses of nonlinear structural systems subjected to stochastic excitation is presented. The exact mean square response estimation of certain class of nonlinear stochastic systems is achieved using Fokker–Planck–Kolmogorov (FPK) equations resulting in analytical expressions or using Monte Carlo simulations. However, for most of the nonlinear systems, the response estimation using Monte Carlo simulations is computationally expensive, and, also, obtaining solution of FPK equation is mathematically exhaustive owing to the requirement to solve a stochastic partial differential equation. In this context, the present work proposes an Adomian decomposition based formalism to derive semi-analytical expressions for the second order response statistics. Further, a derivative matching based moment approximation technique is employed to reduce the higher order moments in nonlinear systems into functions of lower order moments without resorting to any sort of linearization. Three case studies consisting of Duffing oscillator with negative stiffness, Rayleigh Van-der Pol oscillator and a Pendulum tuned mass damper inerter system with linear auxiliary spring–damper arrangement subjected to white noise excitation are undertaken. The accuracy of the closed form expressions derived using the proposed framework is established by comparing the mean square responses of the systems with the exact solutions. The results demonstrate the robustness of the proposed framework for accurate statistical analysis of nonlinear systems under stochastic excitation.     

An automatic formulation of inverse free second moment method for algebraic systems

In systems with probabilistic uncertainties, an estimation of reliability requires at least the first two moments. In this paper, we focus on probabilistic analysis of linear systems. The important tasks in this analysis are the formulation and the automation of the moment equations. The main objective of the formulation is to provide at least means and variances of the output variables with at least a second-order accuracy. The objective of the automation is to reduce the storage and computational complexities required for implementing (automating) those formulations. This paper extends the recent work done to calculate the first two moments of a set of random algebraic linear equations by developing a stamping procedure to facilitate its automation. The new method has an additional advantage of being able to solve problems when the mean matrix of a system is singular. Lastly, from storage and computational complexities and accuracy point of view, a comparison between the new method and another recently developed first order second moment method is made with numerical examples.     

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。     

Delay-independent stability of moments of a linear oscillator with delayed state feedback and parametric white noise

The stability of a linear oscillator with delayed state feedback driven by parametric Gaussian white noise is studied in this paper. The first and second order moment equations of the system response are derived by using moment method and Itô differential rule. Based on the moment equations, the delay-independent stable conditions of both moments are proposed: For the first order moment, the sufficient and necessary condition that guarantee delay-independent stability is identified to that of the deterministic system; for the second order moment, the sufficient condition that ensure delay-independent stability depends on noise intensity. The theoretical results are also illustrated with numerical simulations.     

A solution method for linear and geometrically nonlinear MDOF systems with random properties subject to random excitation

A method for computing the lower-order moments of response of randomly excited multi-degree-of-freedom (MDOF) systems with random structural properties is proposed. The method is grounded in the techniques of stochastic calculus, utilizing a Markov diffusion process to model the structural system with random structural properties. The resulting state-space formulation is a system of ordinary stochastic differential equations with random coefficients and deterministic initial conditions which are subsequently transformed into ordinary stochastic differential equations with deterministic coefficients and random initial conditions. This transformation facilitates the derivation of differential equations which govern the evolution of the unconditional statistical moments of response. Primary consideration is given to linear systems and systems with odd polynomial nonlinearities, for in these cases there is a significant reduction in the number of equations to be solved. The method is illustrated for a five-story shear-frame structure with nonlinear interstory restoring forces and random damping and stiffness properties. The results of the proposed method are compared to those estimated by extensive Monte-Carlo simulation.     

Linear systems driven by martingale noise

A method is developed for calculating second moment properties and moments of order three and higher of the state X of a linear filter driven by martingale noise. The martingale noise is interpreted as the formal derivative of a square integrable martingale with continuous samples. The Gaussian white noise is an example of a martingale noise. It is shown that the differential equations of the mean and correlation functions of the state X developed in the paper resemble the corresponding equations of the classical linear random vibration and coincide with these equations if the input is a Gaussian white noise. The moment equations are derived by (1) the Itô formula for semimartingales and (2) the classical Itô formula applied to a diffusion process whose coordinates include X. An advantage of the second method is use of more familiar concepts. However, this method requires to calculate unnecessary moments and can be applied only for a class of martingale noise processes. Examples are presented to illustrate and evaluate the two methods for calculating moments of X and demonstrate the use of these methods in linear random vibration.     

Analysis of Bernoulli beams with 3D stochastic heterogeneity

The behavior of stochastically heterogeneous beams, composed of isotropic sub-elements of randomly distributed stiffness is studied. Cross sectional as well as longitudinal heterogeneity are included. Average displacements, reaction forces and their statistical variance are found analytically by a functional perturbation method. Ratio of sub-element to beam characteristic size is not negligible and the use of an equivalent homogeneous structure with the classical effective material properties is not sufficient. The major aim is to study the relation between various microstructure properties (grain size, shape, modulus, statistical correlation lengths etc.) and the overall behavior of linear elastic Bernoulli beams. For the statically determinate case, only cross sectional 2D microstructure statistics is found to affect the elastic response, so that an equal average displacement can be achieved by an equivalent, non-isotropic homogeneous beam. For the indeterminate case, the average values of macro properties are affected by the 3D morphological features. Therefore, the proper equivalent homogeneous beam has to include non-local elastic properties. A simple reciprocal relation, connecting two separate loading systems is found, relating their external forces and displacement statistical variances. Morphological parameters, like two point probability moments, used in the final results are derived analytically, and their physical interpretations are discussed.     

Stochastic projection schemes for deterministic linear elliptic partial differential equations on random domains

We present stochastic projection schemes for approximating the solution of a class of deterministic linear elliptic partial differential equations defined on random domains. The key idea is to carry out spatial discretization using a combination of finite element methods and stochastic mesh representations. We prove a result to establish the conditions that the input uncertainty model must satisfy to ensure the validity of the stochastic mesh representation and hence the well posedness of the problem. Finite element spatial discretization of the governing equations using a stochastic mesh representation results in a linear random algebraic system of equations in a polynomial chaos basis whose coefficients of expansion can be non‐intrusively computed either at the element or the global level. The resulting randomly parametrized algebraic equations are solved using stochastic projection schemes to approximate the response statistics. The proposed approach is demonstrated for modeling diffusion in a square domain with a rough wall and heat transfer analysis of a three‐dimensional gas turbine blade model with uncertainty in the cooling core geometry. The numerical results are compared against Monte–Carlo simulations, and it is shown that the proposed approach provides high‐quality approximations for the first two statistical moments at modest computational effort. Copyright © 2010 John Wiley & Sons, Ltd.     

Path integral, functional method, and stochastic dynamical systems

The path-integral approach to dynamical behavior of systems subject to Gaussian white noise is presented in a straightforward manner. Starting from the Chapman-Kolmogorov equation, the transition probability density, and therefore moments and other statistics of the random response are ultimately expressed in terms of functional integrals over the sample-path space. Accordingly, various characteristic functions are replaced by a single generating functional from which moments of all orders are simply calculated through functional differentiation. This generating functional is proven to satisfy a closed system of functional differential equations. These equations are solved in the case of linear systems, their generating functional being obtained in explicit form. Also given in this paper is an integral equation satisfied by the probability densities. Three kinds of approximation method, namely perturbation expansion, Feynman's variational method, and the WKB method, are developed based on the path-integral formalism. They can be used to study the transient as well as stationary behavior of nonlinear systems.     

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。     

Space and contact networks: capturing the locality of disease transmission.

While an arbitrary level of complexity may be included in simulations of spatial epidemics, computational intensity and analytical intractability mean that such models often lack transparency into the determinants of epidemiological dynamics. Although numerous approaches attempt to resolve this complexity-tractability trade-off, moment closure methods arguably offer the most promising and robust frameworks for capturing the role of the locality of contact processes on global disease dynamics. While a close analogy may be made between full stochastic spatial transmission models and dynamic network models, we consider here the special case where the dynamics of the network topology change on time-scales much longer than the epidemiological processes imposed on them; in such cases, the use of static network models are justified. We show that in such cases, static network models may provide excellent approximations to the underlying spatial contact process through an appropriate choice of the effective neighbourhood size. We also demonstrate the robustness of this mapping by examining the equivalence of deterministic approximations to the full spatial and network models derived under third-order moment closure assumptions. For systems where deviation from homogeneous mixing is limited, we show that pair equations developed for network models are at least as good an approximation to the underlying stochastic spatial model as more complex spatial moment equations, with both classes of approximation becoming less accurate only for highly localized kernels.