Tail-equivalent linearization method for nonlinear random vibration

A new, non-parametric linearization method for nonlinear random vibration analysis is developed. The method employs a discrete representation of the stochastic excitation and concepts from the first-order reliability method, FORM. For a specified response threshold of the nonlinear system, the equivalent linear system is defined by matching the “design points” of the linear and nonlinear responses in the space of the standard normal random variables obtained from the discretization of the excitation. Due to this definition, the tail probability of the linear system is equal to the first-order approximation of the tail probability of the nonlinear system, this property motivating the name Tail-Equivalent Linearization Method (TELM). It is shown that the equivalent linear system is uniquely determined in terms of its impulse response function in a non-parametric form from the knowledge of the design point. The paper examines the influences of various parameters on the tail-equivalent linear system, presents an algorithm for finding the needed sequence of design points, and describes methods for determining various statistics of the nonlinear response, such as the probability distribution, the mean level-crossing rate and the first-passage probability. Applications to single- and multi-degree-of-freedom, non-degrading hysteretic systems illustrate various features of the method, and comparisons with results obtained by Monte Carlo simulations and by the conventional equivalent linearization method (ELM) demonstrate the superior accuracy of TELM over ELM, particularly for high response thresholds.     

A dual criterion of equivalent linearization method for nonlinear systems subjected to random excitation

A dual criterion of equivalent linearization method is suggested. The mean-square responses of Duffing, Van der Pol and Lutes-Sarkani oscillators subjected to random excitation are considered. The obtained results are compared with the numerical calculations of original systems and approximate solutions obtained by three different methods including the conventional linearization technique, energy method and regulation linearization method. The results show that in those nonlinear systems the accuracy of the mean-square response is significantly improved by the proposed criterion.     

非对称非线性系统非平稳随机响应的中心差分法

利用随机中心差分法离散化格式,结合时变等价线性化步骤,建立了多自由度非线性系统受非白噪声随机激励作用下非平稳响应协方差矩阵的递推算法.在考虑到非对称项引起的非零均值响应条件下,对等价线性化方法进行修正,使之适用于非对称系统.算例表明:该方法计算结果和Monte Carlo模拟结果符合较好,且对所给算例,此方法比原中心差分法精确.该方法计算步骤简单,便于在计算机上实现,计算效率高.     

随机与谐和联合激励下分数阶非线性系统的统计线性化方法

提出了一种计算随机与谐和联合激励下非线性分数阶系统响应二阶矩的统计线性化方法.假定位移响应可写为确定性均值和零均值随机分量之和的形式,原运动微分方程可化为关于均值分量的确定性微分方程和关于随机分量的随机微分方程组合.分别利用谐波平衡法和统计线性化方法对上述两类方程求解后,可得响应的确定性均值与随机分量.Monte Ca...     

Monte Carlo filters for identification of nonlinear structural dynamical systems

The problem of identification of parameters of nonlinear structures using dynamic state estimation techniques is considered. The process equations are derived based on principles of mechanics and are augmented by mathematical models that relate a set of noisy observations to state variables of the system. The set of structural parameters to be identified is declared as an additional set of state variables. Both the process equation and the measurement equations are taken to be nonlinear in the state variables and contaminated by additive and (or) multiplicative Gaussian white noise processes. The problem of determining the posterior probability density function of the state variables conditioned on all available information is considered. The utility of three recursive Monte Carlo simulation-based filters, namely, a probability density function-based Monte Carlo filter, a Bayesian bootstrap filter and a filter based on sequential importance sampling, to solve this problem is explored. The state equations are discretized using certain variations of stochastic Taylor expansions enabling the incorporation of a class of non-smooth functions within the process equations. Illustrative examples on identification of the nonlinear stiffness parameter of a Duffing oscillator and the friction parameter in a Coulomb oscillator are presented. This paper is dedicated to Prof R N Iyengar of the Indian Institute of Science on the occasion of his formal retirement.     

Stochastic analysis of nonlinear vehicle systems using a generalized harmonic linearization technique

The discrete harmonic linearization method which is applicable to stochastic systems could not provide an equivalent stiffness coefficient for a nonlinear restoring element. In this paper, this technique is generalized to obtain linear representations of both nonlinear restoring and damping elements, based on a principle of energy similarity of dynamic elements. A numerical iterative procedure is presented to compute the local linear coefficients of nonlinear dynamic elements. A nonlinear system is then represented by a set of its complex frequency response function matrices, as functions of the excitation frequency. Stochastic analysis of general multi-DOF nonlinear vehicle systems is established in terms of response PSD characteristics.     

Hitting times for random dynamical systems

The purpose of this article is to study the hitting times for random dynamical systems. For general systems we give a lower bound in terms of the local dimension. For fast mixing systems we obtain an equality. Moreover, under a power law decay of correlations we obtain lower and upper bounds of the hitting times for absolutely continuous stationary measures.     

基于统计线性化的非线性随机振动虚拟激励法研究及应用

传统的虚拟激励法成功地解决了结构线性随机地震响应计算效率低的问题,却无法将非线性因素考虑在内。以统计线性化理论为基础,提出了一种可以考虑结构非线性的改进的绝对位移直接求解的虚拟激励法,采用不同的抗震分析方法,对一座单塔双索面预应力混凝土斜拉桥进行位移、速度、加速度功率谱密度以及位移、轴力响应值的计算,并进行对比分析,结果表明所提出改进的虚拟激励法不仅可以考虑结构的非线性,而且精确、高效,可以广泛地应用于桥梁工程的抗震分析中。     

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.     

A dual criterion of stochastic linearization method for multi-degree-of-freedom systems subjected to random excitation

In this paper, a dual criterion of stochastic linearization method is developed for multi-degree-of-freedom systems subjected to random excitation. A closed system is obtained for determining coefficient matrices of the linearized system. Two examples of two-degree-of-freedom systems subjected to random excitation are presented. The mean-square responses obtained by the dual approach are compared with those obtained by two other methods, namely, conventional linearization technique and energy method. The results show that the dual criterion gives a good prediction on mean-square responses of the original nonlinear system when the nonlinearity is increasing.     

A semi‐analytical locally transversal linearization method for non‐linear dynamical systems

An numeric‐analytical, implicit and local linearization methodology, called the locally transversal linearization (LTL), is developed in the present paper for analyses and simulations of non‐linear oscillators. The LTL principle is based on deriving the locally linearized equations in such a way that the tangent space of the linearized equations transversally intersects that of the given non‐linear dynamical system at that particular point in the state space where the solution vector is sought. For purposes of numerical implementation, two different numerical schemes, namely LTL‐1 and LTL‐2 schemes, based on the LTL methodology are presented. Both LTL‐1 and LTL‐2 procedures finally reduce the given set of non‐linear ordinary differential equations (ODEs) to a set of transcendental algebraic equations valid over a short interval of time or over a short segment of the evolving trajectories as projected on the phase space. While in the LTL‐1 scheme the desired solution vector at a forward time point enters the linearized differential equations as an unknown parameter, in the LTL‐2 scheme a set of unknown residues enters the linearized system as parameters. A limited set of examples involving a few well‐known single‐degree‐of‐freedom (SDOF) non‐linear oscillators indicate that the LTL methodology is capable of accurately predicting many complicated non‐linear response patterns, including limit cycles, quasi‐periodic orbits and even strange attractors. Copyright © 2001 John Wiley & Sons, Ltd.     

Approximate solution for nonlinear random vibration problems by partial stochastic linearization

The accuracy of the stochastic linearization methods is improved by the proposed method of partial stochastic linearization, in which only the nonlinear damping force in the original system is replaced by a linear viscous damping, while the nonlinear restoring force remains unchanged. The replacement is based on the criterion of equal mean work, performed by the nonlinear damping force in the original system and its linear counterpart. The resulting nonlinear stochastic differential equation is then solved exactly, keeping the equivalent damping coefficient as a parameter, which can be determined for a specific system by solving a nonlinear algebraic equation.     

Extended homotopy analysis method for multi-degree-of-freedom non-autonomous nonlinear dynamical systems and its application

In normal circumstances, numerous practical engineering problems are multi-degree-of-freedom (MDOF) nonlinear non-autonomous dynamical systems. Generally, exact solutions for MDOF dynamical systems are hardly obtained; thus, the development of analytical approximations becomes a robust and appealing avenue for an analysis of these systems. The homotopy analysis method (HAM) is one of the analytical methods, which can overcome the foregoing barriers of conventional asymptotic techniques. It has been widely used for solving various nonlinear problems in physical science and engineering. In this paper, the extended homotopy analysis method (EHAM) is presented to establish the analytical approximate solutions for MDOF weakly damped non-autonomous dynamical systems. In terms of its flexibility and applicability, the EHAM is also applied to derive the approximate solutions of parametrically and externally excited thin plate systems. Besides, comparisons are performed between the results obtained by the EHAM and the numerical integration (i.e. Runge–Kutta) method. The present findings show that the analytical approximate solutions of the EHAM agree well with the numerical integration solutions.     

Direct control method for improving stability and reliability of nonlinear stochastic dynamical systems

Optimal control for improving the stability and reliability of nonlinear stochastic dynamical systems is of great significance for enhancing system performances. However, it has not been adequately investigated because the evaluation indicators for stability (e.g. maximal Lyapunov exponent) and for reliability (e.g. mean first-passage time) cannot be explicitly expressed as the functions of system states. Here, a unified procedure is established to derive optimal control strategies for improving system stability and reliability, in which a physical intuition-inspired separation technique is adopted to split feedback control forces into conservative components and dissipative components, the stochastic averaging is then utilized to express the evaluation indicators of performances of controlled system, the optimal control strategies are finally derived by minimizing the performance indexes constituted by the sigmoid function of maximal Lyapunov exponent (for stability-based control)/the reciprocal of mean first-passage time (for reliability-based control), and the mean value of quadratic form of control force. The unified procedure converts the original functional extreme problem of optimal control into an extremum value problem of multivariable function which can be solved by optimization algorithms. A numerical example is worked out to illustrate the efficacy of the optimal control strategies for enhancing system performance.     

Transient responses of dynamical systems with random uncertainties

A new approach is presented for modeling random uncertainties by a nonparametric model allowing transient responses of mechanical systems submitted to impulsive loads to be predicted in the context of linear structural dynamics. The probability model is deduced from the use of the entropy optimization principle whose available information involves the algebraic properties related to the generalized mass, damping and stiffness matrices which have to be positive-definite symmetric matrices, and the knowledge of these matrices for the mean reduced matrix model. An explicit construction and representation of the probability model have been obtained and are very well suited to algebraic calculus and to Monte Carlo numerical simulation in order to compute the transient responses of structures submitted to impulsive loads. Finally, a simple example is presented.     

Centre manifolds for infinite dimensional random dynamical systems

Stochastic centre manifolds theory are crucial in modelling the dynamical behaviour of complex systems under stochastic influences. The existence of stochastic centre manifolds for infinite dimensional random dynamical systems is shown under the assumption of exponential trichotomy. The theory provides a support for the discretisations of nonlinear stochastic partial differential equations with space–time white noise.     

Random chain recurrent sets for random dynamical systems

It is known by the Conley's theorem that the chain recurrent set CR(?) of a deterministic flow ? on a compact metric space is the complement of the union of sets B(A) ? A, where A varies over the collection of attractors and B(A) is the basin of attraction of A. It has recently been shown that a similar decomposition result holds for random dynamical systems (RDSs) on non-compact separable complete metric spaces, but under a so-called absorbing condition. In the present article, the authors introduce a notion of random chain recurrent sets for RDSs, and then prove the random Conley's theorem on non-compact separable complete metric spaces without the absorbing condition.     

Accident prediction models with random corridor parameters

Recent research advocates the use of count models with random parameters as an alternative method for analyzing accident frequencies. In this paper a dataset composed of urban arterials in Vancouver, British Columbia, is considered where the 392 segments were clustered into 58 corridors. The main objective is to assess the corridor effects with alternate specifications. The proposed models were estimated in a Full Bayes context via Markov Chain Monte Carlo (MCMC) simulation and were compared in terms of their goodness of fit and inference. A variety of covariates were found to significantly influence accident frequencies. However, these covariates resulted in random parameters and thereby their effects on accident frequency were found to vary significantly across corridors. Further, a Poisson-lognormal (PLN) model with random parameters for each corridor provided the best fit. Apart from the improvement in goodness of fit, such an approach is useful in gaining new insights into how accident frequencies are influenced by the covariates, and in accounting for heterogeneity due to unobserved road geometrics, traffic characteristics, environmental factors and driver behavior. The inclusion of corridor effects in the mean function could also explain enough variation that some of the model covariates would be rendered non-significant and thereby affecting model inference.     

迟滞非线性系统参数识别方法的研究

迟滞非线性既是非线性的,又是非解析的,关于它的参数识别一直是人们关注却又难以解决的问题,本文提出 了一种较好的识别迟滞非线性系统参数的方法,就是根据实测得到的系统输入－输出数据来直接识别系统的物理参数。