一类Markov过程的最大绝对值过程概率密度求解的新方法

随机过程或随机系统响应的最大绝对值概率分布往往是科学与工程中关心的重要挑战性问题.本文从理论与数值上进行了Markov过程的时变最大绝对值过程及其概率分布研究.文中,通过引入扩展状态向量,构造了最大绝对值$\!$-$\!$-$\!$状态量联合向量过程,由此将不具有Markov性的最大值过程转化为具有Markov性的向量随机过程.在此基础上,通过最大绝对值$\!$-$\!$-$\!$状态量之间的关系,建立了联合向量过程的转移概率密度函数.进而,结合Chapman-Kolmogorov方程和路径积分方法,提出了最大绝对值概率密度函数求解的数值方法.由此,可以得到Markov过程最大绝对值过程的时变概率密度函数,可进一步用于结构动力可靠度分析等.通过数值算例,验证了本文所提方法的有效性. 该方法有望推广到更一般随机系统的极值分布估计之中.      

Path integral solution of vibratory energy harvesting systems

A transition Fokker-Planck-Kolmogorov(FPK) equation describes the procedure of the probability density evolution whereby the dynamic response and reliability evaluation of mechanical systems could be carried out. The transition FPK equation of vibratory energy harvesting systems is a four-dimensional nonlinear partial differential equation. Therefore, it is often very challenging to obtain an exact probability density. This paper aims to investigate the stochastic response of vibration energy harvesters(VEHs)under the Gaussian white noise excitation. The numerical path integration method is applied to different types of nonlinear VEHs. The probability density function(PDF)from the transition FPK equation of energy harvesting systems is calculated using the path integration method. The path integration process is introduced by using the GaussLegendre integration scheme, and the short-time transition PDF is formulated with the short-time Gaussian approximation. The stationary probability densities of the transition FPK equation for vibratory energy harvesters are determined. The procedure is applied to three different types of nonlinear VEHs under Gaussian white excitations. The approximately numerical outcomes are qualitatively and quantitatively supported by the Monte Carlo simulation(MCS).     

On statistical quasi-linearization

In carrying out the statistical linearization procedure to a non-linear system subjected to an external random excitation, a Gaussian probability distribution is assumed for the system response. If the random excitation is non-Gaussian, however, the procedure may lead to a large error since the response of bother the original non-linear system and the replacement linear system are not Gaussian distributed. It is found that in some cases such a system can be transformed to one under parametric excitations of Gaussian white noises. Then the quasi-linearization procedure, proposed originally for non-linear systems under both external and parametric excitations of Gaussian white noises, can be applied to these cases. In the procedure, exact statistical moments of the replacing quasi-linear system are used to calculate the linearization parameters. Since the assumption of a Gaussian probability distribution is avoided, the accuracy of the approximation method is improved. The approach is applied to non-linear systems under two types of non-Gaussian excitations: randomized sinusoidal process and polynomials of a filtered process. Numerical examples are investigated, and the calculated results show that the proposed method has higher accuracy than the conventional linearization, as compared with the results obtained from Monte Carlo simulations.     

非Gauss分布随机波浪力对海洋平台的非线性作用

为避免求解决定Maikov过程转移概率密度的Fokker—Planck方程，基于尺度分离的假设导出了一组描述非线性海洋平台受非Gauss分布随机波浪载荷作用所产生响应的矩量的常微分方程组。矩量方程清楚地反映出分别对应随机载荷和结构响应的两种不同统计特性的相互关系。由于矩量方程不依赖载荷的概率分布的具体细节，以它来模拟随机激励作用下的非线性系统将免于Monte Carlo方法所面临的正确模拟载荷概率分布的困难任务。将摄动法用于矩量方程可使线性化不再需要，这样就不会因为线性化而产生不可预料的误差。     

非线性随机结构动力可靠度的密度演化方法

建议了一类新的非线性随机结构动力可靠度分析方法。基于非线性随机结构反应分析的概率密度演化方法,根据首次超越破坏准则对概率密度演化方程施加相应的边界条件,求解带有初、边值条件的概率密度演化方程,可以给出非线性随机结构的动力可靠度。研究了数值计算技术,建议了具有自适应功能的TVD差分格式。以具有双线型恢复力性质的8层框架结构为例进行了地震作用下的动力可靠度分析,与随机模拟结果的比较表明,所建议的方法具有较高的精度和效率。     

On Double Crater-Like Probability Density Functions of a Duffing Oscillator Subjected to Harmonic and Stochastic Excitation

It is a well-known phenomenon of the Duffing oscillator under harmonic excitation,that there is a frequency range, where two stable and one unstable stationarysolution coexist. If the Duffing oscillator is harmonically excited in thisfrequency range and additionally excited, e.g. by white noise, a double crater-likeprobability density function can be observed, if the noise intensity is smallcompared to the harmonic excitation. The aim of this paper is to calculate thisprobability density function approximately using perturbation techniques. Thestationary solutions in the deterministic case are calculated using theperturbation technique for the resonance case. In a second step, the probabilitydensity function of the perturbation of each of those stationary solutions iscalculated using the perturbation technique for the nonresonance case. This resultsin two crater-like probability density functions which are superimposed by usingthe probability of realization of each of the stationary solutions in thedeterministic case. The probability is calculated using numerical integration orthe method of slowly changing phase and amplitude. Finally, probability densityfunctions obtained in this manner are compared to Monte Carlo simulations.     

随机结构动力反应分析的概率密度演化方法

提出了随机结构动力反应分析的概率密度演化方法．基于有限单元法基本原理，导出了含有随机参数的结构反应状态方程，进而，通过引入扩展状态向量，建立了随机结构反应的概率密度演化方程．将精细时程积分方法与Lax-Wendroff差分格式相结合，探讨了求解概率密度演化方程的数值方法．对一个8层层间剪切型随机结构进行了算例分析，并与Monte Carlo方法的结果进行了比较．研究表明，随机结构反应的概率密度具有演化特征，且概率密度曲线与正态分布差异甚大，甚至可能出现双峰曲线．     

Nonlinearly damped systems under simultaneous broad-band and harmonic excitations

The probability distribution of the response of a nonlinearly damped system subjected to both broad-band and harmonic excitations is investigated. The broad-band excitation is additive, and the harmonic excitations can be either additive or multiplicative. The frequency of a harmonic excitation can be either near or far from a resonance frequency of the system. The stochastic averaging method is applied to obtain the Itô type stochastic differential equations for an averaged system described by a set of slowly varying variables, which are approximated as components of a Markov vector. Then, a procedure based on the concept of stationary potential is used to obtain the exact stationary probability density for a class of such averaged systems. For those systems not belonging to this class, approximate solutions are obtained using the method of weighted residuals. Application of the exact and approximate solution procedures are illustrated in two specific cases, and the results are compared with those obtained from Monte Carlo simulations.     

Reliability of a class of nonlinear systems under switching random excitations

Systems subjected to switching random excitations are practically significant because they include many safety-critical systems such as power plants and communication networks. In this paper, the reliability of multi-degree, nonlinear, non-integrable Hamiltonian systems subjected to switching random excitations is investigated. Such a system is formulated as a continuous–discrete hybrid based upon the Markov jump theory. Stochastic averaging is applied to suppress the rapidly varying parameters of the Markov jump process in order to generate a probability-weighted diffusion equation. The associated backward Kolmogorov equation is then set up, from which the approximate reliability function and probability density of first passage time are obtained. The utility and accuracy of this approximate procedure are demonstrated by two examples.     

Markov chain-based analysis of a modified Cooper-Frieze model

From the perspective of probability, the stability of a modified Cooper- Frieze model is studied in the present paper. Based on the concept and technique of the first-passage probability in the Markov theory, we provide a rigorous proof for the exis- tence of the steady-state degree distribution, and derive the explicit formula analytically. Moreover, we perform extensive numerical simulations of the model, including the degree distribution and the clustering.     

Non-stationary response of a van der Pol-Duffing oscillator under Gaussian white noise

This paper investigates the probability density evolution process of a van der Pol-Duffing oscillator under Gaussian white noise. A path integration method is employed with the Gauss–Legendre integration scheme. In the path integration method, the short-time Gaussian approximation scheme is used for computing the one-step transition probability density. Two cases are considered with slight nonlinearity or strong nonlinearity in displacement. The stationary and non-stationary responses of the oscillator are studied. Compared with the simulation result, the path integration method can present a satisfactory probability density function (PDF) solution for each case. Different probability density evolution processes are observed correspondingly. In the case of slight nonlinearity, the PDF undergoes a clockwise motion around the origin. The peak region gradually expands and the PDF eventually forms a circle. By contrast, the strong nonlinearity drives the oscillator to oscillate around the limit cycle. In such a case, the PDF rapidly forms a circle. The circle keeps its shape and develops until the oscillator becomes stationary. More complicated phenomena can be studied by the adopted path integration method.     

Stochastic Analysis of Self-Induced Vibrations

Vortex-induced vibrations of a structural element are modelled as a non-linear stochastic single-degree-of-freedom system. The deterministic part of the governing equation represents laminar flow conditions with a stationary non-zero solution corresponding to lock-in. Across-wind turbulence is included as an additive excitation and along-wind turbulence is introduced as a parametric excitation term, both assumed to be white noise processes. An approximate closed-form solution to the corresponding Fokker–Planck equation in terms of the stationary probability density of the energy is obtained. The auto spectral density of the position at a particular energy-level is approximated by the spectral density of a linear system with energy dependent damping. The spectral density is then obtained by integration of the energy conditional spectral density over all energies weighted by the probability density. The approximate theoretical expressions for the probability density of the energy and the auto spectral density of the position compare favourably with results obtained by numerical simulation.     

Mean wave propagation in a slab of one-dimensional discrete random medium

A study has been made of the propagation of time harmonic waves through a one-dimensional medium of discrete scatterers randomly positioned over a finite interval L. The random medium is modeled by a Poisson impulse process with density λ. The invariant imbedding procedure is employed to obtain a set of initial value stochastic differential equations for the field inside the medium and the reflection coefficient of the layer. By using the Markov properties of the Poisson impulse process. exact integro-differential equations of the Kolmogorov-Feller type are derived for the probability density function of the reflection coefficient and the field. When the concentration of the scatterers is low, a two variable perturbation method in small λ is used to obtain an approximate solution for the mean field. It is shown that this solution, which varies exponentially with respect to λL, agrees exactly with the mean field obtained by Feldy's approximate method.     

An importance sampling procedure for estimating failure probabilities of non-linear dynamic systems subjected to random noise

The first passage problem for linear and non-linear oscillators excited by white and coloured noise are considered. An iterative variance reduction scheme is used in a framework of a measure change in the space of sample functions according to the Girsanov transformation, which is based on introducing a Markov control process. It is proved that a good approximation to the optimal stochastic control process can be obtained from an equivalent white noise excited linear oscillator. It is shown that this leads to very accurate estimates of the failure probability of the original system. The advantage of this procedure is that expressions for the parameters of the equivalent linear system and the design point oscillations, which are needed to find the control process, are available analytically. The number of samples, the variance of the failure probability estimates and the computational time are reduced significantly compared with direct Monte Carlo simulations.     

Generalized sensitivity and probabilistic analysis of buckling loads of structures

The imperfection sensitivity law by Koiter played a pivotal role in the early stage of research on initial post-buckling behaviors of structures, but seems somewhat overshadowed by numerical approaches in the computer age. In this paper, to make this law consistent with practical application, the law is extended to implement the influence of a number of imperfections, and the second-order (minor) imperfections are considered, in addition to the first-order (major) imperfections considered in the Koiter law. Explicit formulas are presented to be readily applicable to the numerical evaluation of imperfection sensitivity. A procedure to describe the probabilistic variation of critical loads is presented for the case where initial imperfections of structures are subject to a multivariate normal distribution; the formula for the probability density function of critical loads is derived by considering up to the second-order imperfections. The validity and usefulness of the present procedure are demonstrated through the application to truss structures.     

基于马尔科夫链与鞍点逼近的模糊随机可靠性分析方法

针对同时存在随机不确定性和模糊不确定性的可靠性分析问题,提出了两种高效解决方法。一种是迭代马尔科夫链鞍点逼近法,该方法的基本思想是给定隶属水平下由迭代马尔科夫链和一次鞍点逼近法求得可靠度上下限,不同的隶属水平对应不同的可靠度上下限,遍历隶属水平的取值区间[0,1]即可求得可靠度隶属函数,与传统的两相Monte Carlo数字模拟法和迭代一次二阶矩法相比,该方法具有效率高和对非正态基本随机变量不需要进行正态转换的优点;第二种方法是迭代条件概率马尔科夫链模拟法,该方法在求解给定隶属度水平下的可靠度上下限时,由条件概率公式引入一个非线性修正因子,该因子的引入大大提高了功能函数为非线性的可靠性问题的求解精度。本文算例验证了所提方法的优越性。     

随机振动结构Von Mises应力过程峰值概率密度函数的研究

随机振动载荷作用下结构的多轴疲劳分析非常复杂,利用Von Mises应力准则将多轴应力转换为单轴应力是一条简单而有效的途径。在频域利用Von Mises应力对结构进行多轴疲劳分析的前提是必须获得Von Mises应力过程中应力循环的概率密度函数。本文利用平稳随机过程的穿越分析和极值的概率分析,给出了计算Von Mises应力过程峰值概率密度函数的公式,为进一步进行疲劳损伤及寿命分析打下了基础。     

非平稳随机激励下结构体系动力可靠度时域解法

将结构动力方程写成状态方程形式,采用精细积分法对其进行数值求解,导出了非平稳激励下结构随机响应的时域显式表达式,该过程的计算量仅相当于两次确定性时程分析的计算量. 基于该显式表达式,结合首次超越失效准则,提出了非平稳随机激励下结构体系动力可靠度的数值模拟算法. 与功率谱方法相比,该方法无需同时在时频域内进行大量数值积分,也无需引入关于响应过程跨越界限次数概率分布, 以及各失效模式相关性等方面的假定. 通过数值算例, 对比了该方法与泊松过程法、马尔可夫过程法、传统蒙特卡罗法的计算精度和计算效率,结果显示该方法具有理想的精度和相当高的效率.      

随机结构非线性动力响应的概率密度演化分析

提出了随机结构非线性动力响应分析的概率密度演化方法．根据结构动力响应的随机状态方程，利用概率守恒原理，建立了随机结构非线性动力响应的概率密度演化方程．结合Newmark-Beta时程积分方法与Lax-Wendroff差分格式，提出了概率密度演化方程的数值分析方法．通过与Monte Carlo分析方法对比，表明所给出的概率密度演化方法具有良好的计算精度和较小的计算工作量．研究表明：随机结构非线性动力响应概率密度具有典型的演化特征，随着时间增长，概率密度曲线分布趋于复杂．     

结构随机反应概率密度演化分析的数论选点法

密度演化方法可以直接获取结构的线性和非线性响应概率密度函数解答及其演化过程。当结构参数与激励中含有多个随机变量时，在多维随机变量空间中的离散代表点选点规则对密度演化分析的精度和效率至关重要。基于高维数值积分的数论方法，建议了多维随机变量空间的数论选点方法。利用多维随机变量空间的联合概率密度函数的球对称性或近似辐射衰减性质，对数论方法给出的单位超立方体中的分布点集进行筛选，可大幅度减少选点数目，从而将具有多个随机变量的结构随机响应分析问题计算工作量降低到与单一随机变量结构随机响应分析问题相当的水平。