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Enhanced stochastic averaging of non-integrable nonlinear systems subjected to stochastic excitations

机译:受到随机激励的非积分非线性系统的增强随机平均

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Stochastic averaging method based on energy envelope for a non-integrable system reduces the dimension of dynamic systems, while preserving the nonlinearity in system behavior and its stochasticity arising from input excitations. However, this approach for a general system neglects effects of off-diagonal damping components, considers a lumped effect of diagonal damping entries, and assumes independent stochastic excitations for involved degrees of freedom. These constraints may limit the application of stochastic averaging as nonlinear systems often do not satisfy these conditions. This paper addresses these shortcomings and proposes a new strategy to derive an equivalent nonlinear stochastic system. For this purpose, an equivalent excitation intensity and modified damping parameters are derived by equating drift and diffusion components of the modified and the original system through the method of weighted residuals using high order moments of system velocities. The proposed approach, called enhanced stochastic averaging, is demonstrated for a one-story building on a raft foundation in loose sand subjected to lateral stochastic excitations. Results, such as probability density function at different intensity levels, indicate that the enhanced stochastic averaging method significantly improves the analytical predictions of the probabilistic properties of system responses compared to the conventional stochastic averaging method.
机译:基于能量包络的不可积分系统随机平均方法减小了动态系统的尺寸,同时保留了系统行为的非线性及其输入激励引起的随机性。但是,这种用于一般系统的方法忽略了对角阻尼分量的影响,考虑了对角阻尼项的集总效应,并针对所涉及的自由度假设了独立的随机激励。这些约束可能会限制随机平均的应用,因为非线性系统通常不满足这些条件。本文解决了这些缺点,并提出了一种新的策略来推导等效非线性随机系统。为此,通过使用系统速度的高阶矩通过加权残差的方法,使修改后的系统和原始系统的漂移和扩散分量相等,从而得出等效的激励强度和修改后的阻尼参数。提出的方法称为增强随机平均,该方法在筏板基础上的一幢多层建筑物中,在松散的沙子中受到横向随机激励的情况下得到了证明。结果(例如在不同强度级别的概率密度函数)表明,与常规随机平均方法相比,增强型随机平均方法显着改善了系统响应概率特性的分析预测。

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