非高斯随机激励下滞迟系统响应与首次穿越失效

针对由有界噪声、泊松白噪声和高斯白噪声共同构成的非高斯随机激励,通过Monte Carlo数值模拟方法研究了此激励作用下双线性滞迟系统和Bouc-Wen滞迟系统这两类经典滞迟系统的稳态响应与首次穿越失效时间。一方面,分析了有界噪声和泊松白噪声这两种分别具有连续样本函数和非连续样本函数的非高斯随机激励,在不同激励参数条件下对双线性滞迟系统和Bouc-Wen滞迟系统的稳态响应概率密度、首次穿越失效时间概率密度及其均值的不同影响;另一方面,揭示了在这类非高斯随机激励荷载作用下,双线性滞迟系统的首次穿越失效时间概率密度将出现与Bouc-Wen滞迟系统的单峰首次穿越失效时间概率密度截然不同的双峰形式。     

非高斯随机激励下滞迟系统响应与首次穿越失效

针对由有界噪声、泊松白噪声和高斯白噪声共同构成的非高斯随机激励,通过Monte Carlo数值模拟方法研究了此激励作用下双线性滞迟系统和Bouc-Wen滞迟系统这两类经典滞迟系统的稳态响应与首次穿越失效时间。一方面,分析了有界噪声和泊松白噪声这两种分别具有连续样本函数和非连续样本函数的非高斯随机激励,在不同激励参数条件下对双线性滞迟系统和Bouc-Wen滞迟系统的稳态响应概率密度、首次穿越失效时间概率密度及其均值的不同影响;另一方面,揭示了在这类非高斯随机激励荷载作用下,双线性滞迟系统的首次穿越失效时间概率密度将出现与Bouc-Wen滞迟系统的单峰首次穿越失效时间概率密度截然不同的双峰形式。     

基于state-space-split法的滞回非线性系统随机动力响应分析

采用SSS(state-space-split)法,建立了引入Bouc-Wen滞回模型的杜芬非线性系统在高斯白噪声激励下的概率密度函数(PDF)的近似求解方法,分析了其随机动力响应变化规律.首先,将Bouc-Wen滞回模型引入杜芬非线性系统,分别考虑非线性系统中的几何非线性和材料非线性对动力响应的影响.随后,对该模型进行了等效线性化(EQL)处理,基于等效线性化法结果,介绍了SSS法的简化方法和计算原理,并通过该方法求解了三维FPK方程的近似联合概率密度函数.最后,将该方法应用于三个案例和一个工程实例,通过求解其概率密度函数分布及动力可靠度验证了提出方法的适用性、可行性和优越性.     

非高斯激励下带附加质量的悬臂梁响应研究

大多数悬臂梁的研究都是基于确定性振动分析或将外部激励假定为高斯过程，鲜少涉及非高斯激励。针对非高斯激励下带附加质量的悬臂梁的响应特征进行研究。首先，通过Lagrange方程建立考虑曲率非线性的悬臂梁振动方程；然后，通过指数多项式闭合法求解结构响应的概率密度函数，并分析了平均到达速率、激励强度及附加质量对结构响应的影响；同时分析了非平稳非高斯激励下非线性结构参数对响应的影响。计算结果表明，指数多项式闭合法与模拟解吻合很好，平均到达速率越大，结构响应的概率密度函数曲线收缩越明显。     

多自由度非线性随机系统的响应与稳定性

响应与稳定性分析一直是随机动力学研究的热点, 发展预测随机响应及判定系统响应性态的方法具有重要的科学意义与广阔的应用前景. 本文综述了有关多自由度非线性随机系统的响应与稳定性的研究. 首先简介用于随机系统响应预测的Fokker-Planck-Kolmogorov方程法、随机平均法、等效线性化法、等效非线性系统法和Monte Carlo模拟法, 评述其优缺点, 进而讨论了多自由度非线性随机系统响应的精确平稳解、近似瞬态解的研究现状. 然后介绍了随机系统稳定性分析的两类方法, 即Lyapunov函数法及Lyapunov指数法,并综述了多自由度非线性随机系统稳定性分析的研究现状. 最后给出几点发展建议.     

DYNAMIC CHARACTERISTICS OF VIBRATION ISOLATION SYSTEM WITH CUBIC STIFFNESS AND HYSTERESIS 1)

包含立方刚度和Bouc-Wen 型滞回的隔振系统具有复杂的非线性动力学特性。系统无阻尼响应模型可基于无滞回恢复力建立,利用谐波平衡法和泰勒展开求得近似解析解。系统有阻尼响应模型可利用解析/数值联合方法求解,该方法基于谐波平衡法和Levenberg-Marquardt 迭代算法,对于滞回产生的多值非光滑函数项,先计算时域响应再通过快速傅里叶变换求解谐波项系数。上述方法在含水平绞制梁的非线性隔振系统分析中得到有效应用。分析表明,在Bouc-Wen 型滞回和立方刚度的综合影响下,隔振系统呈现渐软–渐硬特性,滞回阻尼和线性阻尼都可以有效抑制共振,但前者高频隔振效果优于后者。     

斜拉桥拉索在轴向窄带随机激励下的振动响应

导出了拉索在考虑垂度以及索张力沿索长变化时的参激随机微分方程,进一步给出了预测拉索在窄带随机激励下响应的近似理论解------用统计矩截断法求解矩方程,获得高斯闭合解和一阶非高斯闭合解. 以南京长江二桥约330米长的A20拉索为研究对象,对以上高斯闭合解和一阶非高斯闭合解进一步进行数值求解以获得拉索的响应,并采用Monte-Carlo数值方法对求解进行验证. 分析了拉索振动的一般特征,特别分析了激励中心频率和拉索频率比为1和2时的响应随激励带宽的变化特征,得到了一些新的结论.      

带位移偶次方项非线性随机振子在位移参数激励下的概率解

论文用指数多项式闭合或EPC(Exponential Polynomial Closure)法分析了具非零均值响应的带位移偶次方项非线性随机振子在参数激励下响应的概率密度函数解.给出了求解过程并通过算例分析验证了指数多项式闭合法在此情况下的有效性.数值结果显示,指数多项式闭合法得到的响应概率密度结果与蒙特卡洛模拟的结果符合较好,尤其是在对系统可靠性分析起主要作用的概率密度函数尾部区域符合很好.     

泊松白噪声激励下强非线性系统的半解析瞬态解

自然界与工程中都普遍存在着随机扰动, 且大多数呈现出固有的非高斯性质, 若采用高斯激励建模可能会导致巨大的误差. 泊松白噪声作为一种典型且重要的非高斯激励模型, 已引起了广泛的关注. 目前, 泊松白噪声激励下系统的动态特性分析主要集中于稳态响应的研究, 而针对瞬态响应的求解难度仍较大, 需进一步发展. 本文引入径向基神经网络, 提出了一种泊松白噪声激励下单自由度强非线性系统瞬态响应预测的高效半解析方法. 首先将广义Fokker-Plank-Kolmogorov (FPK) 方程的瞬态解表示为一组含时变待定权值系数的高斯径向基神经网络; 然后采用有限差分法离散时间导数项, 并结合随机取样技术构造含时间递推式的损失函数; 最后通过拉格朗日乘子法使得损失函数最小化获得时变最优权值系数. 作为算例, 探究了两个经典强非线性系统, 并采用蒙特卡罗模拟方法对解析结果加以验证. 结果表明: 本文方法所获得的瞬时概率密度函数与蒙特卡罗模拟数据吻合地较好, 并且算法具备较高的计算效率. 在系统响应的整个演化过程中, 本文所提方法能够非常有效地捕捉到系统响应在各个时刻下的复杂非线性特征. 此外, 本文方法所获得的高精度半解析瞬态解, 不仅可作为基准解检验其他非线性随机振动分析方法的精度, 对于结构的优化设计也存在巨大的潜在应用价值.      

简支梁非线性响应的三维路径积分法分析

采用基于Gauss-Legendre积分公式的三维路径积分法,分析了在过滤高斯白噪声激励下的简支梁非线性随机振动响应的概率密度函数;联立一阶滤波方程与简支梁一阶模态的振动模型,得到在过滤高斯白噪声激励下的简支梁随机振动模型,基于Gauss-Legendre积分公式的积分法和短时高斯近似法求解响应的概率密度函数值。结果表明,三维路径积分法计算值与蒙特卡洛模拟值符合良好,即使在尾部区域也符合良好。三维路径积分法比等效线性化法的计算精度更高。     

Random vibration of hysteretic systems under Poisson white noise excitations

Hysteresis widely exists in civil structures, and dissipates the mechanical energy of systems. Research on the random vibration of hysteretic systems, however,is still insufficient, particularly when the excitation is non-Gaussian. In this paper, the radial basis function(RBF) neural network(RBF-NN) method is adopted as a numerical method to investigate the random vibration of the Bouc-Wen hysteretic system under the Poisson white noise excitations. The solution to the reduced generalized Fokker...     

EPC procedure for PDF solution of non-linear oscillators excited by Poisson white noise

The stationary probability density function (PDF) solution of the responses of non-linear stochastic oscillators subjected to Poisson pulses is analyzed. The PDF solutions are obtained by the exponential-polynomial closure (EPC) method. To assess the effectiveness of the solution procedure numerically, non-linear oscillators are analyzed with different impulse arrival rates, degree of oscillator non-linearity and excitation intensity. Numerical results show that the PDFs obtained with the EPC method yield good agreement with those obtained from Monte Carlo simulation when the polynomial order is 4 or 6. It is also observed that the EPC procedure is the same as the equivalent linearization procedure under Gaussian white noise in the case of the polynomial order being 2.     

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.     

单自由度超弹性SMA减振结构随机振动控制理论研究

利用形状记忆合金（Shape Memory Alloy,简称SMA）丝的超弹性,提出了一种具有复位功能的阻尼器。在SMA丝的Graesser本构模型基础上,建立了阻尼器恢复力的滑移双线性模型;假定滞回面积相等,提出了恢复力的滑移刚塑性模型以近似简化滑移双线性模型。采用等价线性化法建立了单自由度超弹性SMA减振结构在高斯白噪声激励下的平稳随机振动分析公式。通过一算例,考虑不同激励谱密度和结构阻尼比：比较了等价线性法和蒙特卡罗（Monte Carlo）模拟法计算的结构振动响应（位移标准差和速度标准差）,证明了SMA减振结构随机振动控制理论的有效性;比较了等价线性减振结构和无控结构的动力特性（刚度和阻尼比）和振动响应,说明了SMA阻尼器能提高结构的刚度和阻尼比,因而可有效抑制结构的振动。     

迟滞系统受随机激励的响应计算

首先为随机激励设计了具有较高精度的滤波器，把迟滞系统受有色噪声激励的随机振动问题转化为由迟滞系统与滤波器组成的扩阶系统受白噪声激励的随机振动问题，然后详细论述了等效线性化法对此问题的具体求解，着重推导了高斯随机变量联合矩的递推关系和李亚普诺夫方程解法的实现，最后根据数字模拟结果分析了等效线性化法的精度和误差来源，给出了迟滞减振系统参数设计的有关建议。     

The stationary solution of a random dynamical model

This paper studies the stationary probability density function (PDF) solution of a nonlinear business cycle model subjected to random shocks of Gaussian white-noise type. The PDF solution is controlled by a Fokker–Planck–Kolmogorov (FPK) equation, and we use exponential polynomial closure (EPC) method to derive an approximate solution for the FPK equation. Numerical results obtained from EPC method, better than those from Gaussian closure method, show good agreement with the probability distribution obtained with Monte Carlo simulation including the tail regions.     

A probabilistic linearization method for non-linear systems subjected to additive and multiplicative excitations

A general method to obtain approximate solutions for the random response of non-linear systems subjected to both additive and multiplicative Gaussian white noises is presented. Starting from the concept of linearization, the proposed method of “Probabilistic Linearization” (PL) is based on the replacement of the Fokker–Planck equation of the original non-linear system with an equivalent one relative to a linear system subjected to additive excitation only. By means of the general scheme of the weighted residuals, the unknown coefficients of the equivalent system are determined. Assuming a Gaussian probability density function of the response process and by choosing the weighting functions in a suitable way, the equivalence of the proposed method, called “Gaussian Probabilistic Linearization” (GPL), with the “Gaussian Stochastic Linearization” (GSL) applied to the coefficients of the Itô differential rule is evidenced. In addition, the generalization of the proposed method, called “Generalized Gaussian Probabilistic Linearization” (GGPL), is presented. Numerical applications show as, varying the choice of the weighting functions, it is possible to obtain different linearizations, with a variable degree of accuracy. For the two examples considered, different suitable combinations of the weighting functions lead to different equivalent linear systems, all characterized by the exact solution in terms of variance.