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Generation of strongly non-Gaussian stochastic processes by iterative scheme upgrading phase and amplitude contents

机译：通过迭代方案升级阶段和幅度内容产生强烈的非高斯随机过程

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摘要

Random excitations, such as wind velocity, always exhibit non-Gaussian features. Sample realisations of stochastic processes satisfying given features should be generated, in order to perform the dynamical analysis of structures under stochastic loads based on the Monte Carlo simulation. In this paper, an efficient method is proposed to generate stationary non-Gaussian stochastic processes. It involves an iterative scheme that produces a class of sample processes satisfying the following conditions. (1) The marginal cumulative distribution function of each sample process is perfectly identical to the prescribed one. (2) The ensemble-averaged power spectral density function of these non-Gaussian sample processes is as close to the prescribed target as possible. In this iterative scheme, the underlying processes are generated by means of the spectral representation method that recombines the upgraded power spectral density function with the phase contents of the new non-Gaussian processes in the latest iteration. Numerical examples are provided to demonstrate the capabilities of the proposed approach for four typical non-Gaussian distributions, some of which deviate significantly from the Gaussian distribution. It is found that the estimated power spectral density functions of non-Gaussian processes are close to the target ones, even for the extremely non-Gaussian case. Furthermore, the capability of the proposed method is compared to two other methods. The results show that the proposed method performs well with convergence speed, accuracy, and random errors of power spectral density functions.
机译：随机激发，如风速，始终展示非高斯功能。应产生满足给定特征的随机过程的样本实现，以便在基于蒙特卡罗模拟的随机载荷下进行结构的动态分析。本文提出了一种有效的方法来产生静止的非高斯随机过程。它涉及一种迭代方案，其产生满足以下条件的一类样本过程。 （1）每个样品过程的边缘累积分布功能与规定的过程完全相同。 （2）这些非高斯样品过程的集合平均功率谱密度函数尽可能靠近规定的目标。在该迭代方案中，通过频谱表示方法生成底层过程，该方法将升级的功率谱密度函数重新结合到最新迭代中的新非高斯过程的相含量。提供了数值示例以证明所提出的四种典型非高斯分布方法的能力，其中一些方法从高斯分布偏离。结果发现，即使对于极其非高斯的情况，非高斯过程的估计功率谱密度函数均接近目标。此外，将所提出的方法的能力与另外两种方法进行比较。结果表明，该方法采用了收敛速度，精度和功率谱密度函数的随机误差良好。

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  • 来源
    《Applied Mathematical Modelling》 |2020年第11期|675-690|共16页
  • 作者

    Yongxin Wu; Houle Zhang; Yufeng Gao;

  • 作者单位

    Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering Hohai University No. 1 Xikang Road Nanjing 210098 China;

    Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering Hohai University No. 1 Xikang Road Nanjing 210098 China;

    Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering Hohai University No. 1 Xikang Road Nanjing 210098 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Non-Gaussian process; Translation process theory; Spectral representation method; Random error; Crossing rate;

    机译：非高斯过程;翻译过程理论;光谱表示方法;随机错误;交叉率;

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