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Moment equations and cumulant-neglect closure techniques for non-linear dynamic systems under renewal impulse process excitations

机译:续期脉冲过程激发下的非线性动态系统的时刻方程和累积忽略闭合技术

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The moment equations technique together with modified cumulant-neglect closure techniques is developed for a non-linear dynamic system subjected to a random train of impulses driven by an Erlang renewal counting process. The original non-Markov problem is converted into a Markov one by recasting the excitation process with the aid of an auxiliary, pure-jump stochastic process characterized by a Markov chain. Hence the conversion is carried out at the expense of augmentation of the state space of the dynamic system by Markov states of the auxiliary jump process. The problem is characterized by the set of joint probability density - discrete distribution function, which are the joint probabilities of the original state vector and of the Markov chain being in the particular jth state. Accordingly the statistical moments of the state variables are defined as integrals with respect to the mixed-type, probability density - discrete distribution function. The differential equations for moments are obtained with the aid of the forward integro-differential Chapman-Kolmogorov operator (Iwankiewicz, 2014). Two modified closure techniques are developed. The first one is the result of application of cumulant-neglect closure directly to unconditional moments. The second modified closure approximation technique is based on the representation of the joint probability density - discrete distribution function by conditioning it on two mutually exclusive and exhaustive events: that the Markov chain is in the jth state while the system is at rest (it is in the jth state for the first time) and that the Markov chain is in the jth state while the system is not at rest (it is in the jth state for any subsequent time). Thus the joint probability density function consists of a Dirac-delta spike and of the continuous part (cf Iwankiewicz a al. (1990)). The cumulant-neglect closure approximations are first formulated for the conditional moments resulting from the continuous part of the probability density function and next for the unconditional moments. As an example of a non-linear system the oscillator with cubic restoring force term is considered. The equations for moments up to the fourth-order are derived. Hence the modified cumulant-neglect closure approximations are derived for redundant fifth- and sixth order moments, both centralized and ordinary. The developed moment equations with modified cumulant-neglect closure techniques are verified against Monte Carlo simulations.
机译:对于由Erlang更新计数过程驱动的随机脉冲的非线性动态系统,开发了动态方程技术与改进的累积封闭技术一起开发了一种非线性动态系统。通过借助于由Markov链特征的辅助,纯跳跃随机过程重用激发过程,将原始的非马尔可夫问题转换为马尔可夫。因此,通过Markov跳跃过程的Markov状态,以牺牲动态系统的状态空间的增量来执行转换。该问题的特征在于,一组联合概率密度 - 离散分布函数,其是原始状态向量的关节概率和马尔可夫链处于特定的第j态。因此,状态变量的统计瞬间被定义为相对于混合型,概率密度 - 离散分布函数的积分。借助前方积分差分查普曼-Kolmogorov运算符(IWankiewicz,2014)获得了矩的微分方程。开发了两个改进的闭合技术。第一个是将累积疏忽闭合到无条件时刻的施用结果。第二修改的闭合近似技术基于联合概率密度 - 离散分布函数的表示,通过将其调节到两个互斥和详尽的事件中:马尔可夫链在系统静止时处于第三状态(它在第一次第一次),而且马尔可夫链是在第j状态的时候,而系统在休息时(它在任何后续时间的jth状态)。因此,联合概率密度函数由Dirac-Delta Spike和连续部分(CF IwankiewiCz A Al。(1990))组成。首先配制累积疏忽闭合近似,用于由概率密度函数的连续部分和接下来的瞬间产生的条件矩。作为非线性系统的示例,考虑具有立方恢复力术语的振荡器。导出了最多四阶的时刻的方程。因此,用于集中式和普通的冗余五分之象,导出修改的累积次数忽略近似。具有修改的累积封闭技术的发达的时刻方程是针对蒙特卡罗模拟的验证。

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