在菲律宾海测量663个流波的非线性里埃分类
Yu-Chen Lee1, Markus Brühl2, Dong-Jiing Doong3
1Delft Center for Systems and Control, Delft University of Technology, Delft, CD, The Netherlands.
PloS one
|May 14, 2024
概括
这项研究系统地将非线性里埃变换 (NFT) 应用于一大批流波的数据集,将它们分类为四种类型,并揭示了它们的形成机制和主要非线性模式的洞察力.
科学领域:
- 海洋学和流体动力学
- 非线性波浪现象 非线性波浪现象
- 数据分析和信号处理.
背景情况:
- 流波是极端的,不可预测的海洋表面波浪,超过显著波浪高度的两倍.
- 之前的流波分析主要集中在单个事件和线性叠加上,对非线性方法的应用有限.
- 非线性里埃变换 (NFT) 提供了一个强大的工具,可以发现波数据中隐藏的非线性组件.
研究的目的:
- 系统地将非线性弗里埃变换用于非线性施罗丁格方程 (NLSE-NFT) 应用到深水流波的大数据集上.
- 根据它们的主导的非线性福里埃模式对流波进行分类.
- 调查流波特征及其潜在的非线性光谱元件之间的关系.
主要方法:
- 利用了12年的海上浮标的现场测量数据.
- 在非线性施罗丁格方程 (NLSE-NFT) 中应用了非线性里埃变换来分析流波数据.
- 根据非线性模式特征,将流波分为四种类型 (稳定型,小呼吸型,大呼吸型,孤独型).
- 采用本杰明-费尔指数 (BFI) 来分析非线性光谱,并核实了考虑方向扩散和不考虑方向扩散的发现.
主要成果:
- 将测量的流波分为四种不同类型,基于主导的非线性里埃模式.
- 发现所有四种流波类型都可以在一个地点发生,特定条件有利于某些类型.
- 确定占主导地位的非线性里埃模式,特别是单子,可以显著地促进流波高度,在某些情况下高达50%.
- 证实NLSE-NFT分析甚至在计算定向扩散时也产生了类似的分类.
结论:
- 在NLSE-NFT提供了一个强大的框架来分类和理解在现实世界的数据中流波的非线性动态.
- 已识别的流波类型及其相关的非线性模式为流波生成机制提供了新的见解.
- 这种对大量数据集的系统分析有助于更好地理解流波预测和缓解策略.
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