five

A bedform tracking tool coupled with Fast Fourier Transform decomposition

收藏
DataCite Commons2022-03-08 更新2025-04-09 收录
下载链接:
https://hdl.handle.net/11299/218358
下载链接
链接失效反馈
官方服务:
资源简介:
Quantifying bedform characteristics is crucial because bedforms are omnipresent and play an important role in fluvial environments. Bedforms induce form drag against flows and can significantly alter water depth, flow velocity, and sediment transport rate (i.e. the hydraulic roughness of channels can be parameterized with bedforms). In addition, ship navigation can be constrained by the presence and distributions of bedform crests; and localized scour within bedform troughs can deteriorate performance of fluvial infrastructures (e.g. containment walls, embedded pipes, or groynes). Despite of its importance, characterizing bedforms has been challenges due to inherent multi-scale features observed in channel bathymetries in both natural rivers and laboratory flumes. To tackle such challenges, we developed a bedform tracking tool coupled with Fast Fourier Transform (FFT) decomposition. A key advantage of the presented bedform tracking method is that bedform characteristics (morphology and kinematics) can be quantified in a wider range of scales.
提供机构:
Data Repository for the University of Minnesota (DRUM)
创建时间:
2021-02-17
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作