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Nonlinear random optical waves: Integrable turbulence, rogue waves and intermittency

机译:非线性随机光波:可积分湍流,流氓波和间歇性

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摘要

We examine the general question of statistical changes experienced by ensembles of nonlinear random waves propagating in systems ruled by integrable equations. In our study that enters within the framework of integrable turbulence, we specifically focus on optical fiber systems accurately described by the integrable one-dimensional nonlinear Schrodinger equation. We consider random complex fields having a Gaussian statistics and an infinite extension at initial stage. We use numerical simulations with periodic boundary conditions and optical fiber experiments to investigate spectral and statistical changes experienced by nonlinear waves in focusing and in defocusing propagation regimes. As a result of nonlinear propagation, the power spectrum of the random wave broadens and takes exponential wings both in focusing and in defocusing regimes. Heavy-tailed deviations from Gaussian statistics are observed in focusing regime while low-tailed deviations from Gaussian statistics are observed in defocusing regime. After some transient evolution, the wave system is found to exhibit a statistically stationary state in which neither the probability density function of the wave field nor the spectrum changes with the evolution variable. Separating fluctuations of small scale from fluctuations of large scale both in focusing and defocusing regimes, we reveal the phenomenon of intermittency; i.e., small scales are characterized by large heavy-tailed deviations from Gaussian statistics, while the large ones are almost Gaussian. (C) 2016 Elsevier B.V. All rights reserved.
机译:我们研究了由可积方程统治的系统中传播的非线性随机波的集合所经历的统计变化的一般问题。在进入可积湍流框架的研究中,我们特别关注可积一维非线性Schrodinger方程精确描述的光纤系统。我们考虑在初始阶段具有高斯统计和无限扩展的随机复数场。我们使用具有周期性边界条件的数值模拟和光纤实验来研究非线性波在聚焦和散焦传播方式中经历的光谱和统计变化。作为非线性传播的结果,随机波的功率谱在聚焦和散焦状态下均变宽并呈指数展翅。在聚焦状态下观察到高斯统计的重尾偏差,而在散焦状态下观察到与高斯统计量的低尾偏差。经过一些短暂的演化后,发现该波系统显示出统计上稳定的状态,其中波场的概率密度函数和频谱都不会随演化变量而变化。在聚焦和散焦状态下,将小范围的波动与大范围的波动分开,我们揭示了间歇现象。即,小尺度的特征是与高斯统计的重尾大偏差,而大尺度几乎是高斯的。 (C)2016 Elsevier B.V.保留所有权利。

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