Mechanisms of ice accumulation in a river bend – an experimental study

Wang, Jun; Sui, Jueyi; Zhang, Hong-ya; Chen, Pang-pang; Hirshfield, Faye

doi:10.1016/s1001-6279(13)60010-4

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…Also, according to Wang et al . (), ice builds up on the convex bank of river bends. On the Vistula, unlike in the case of the Yellow River, after ice cover progression, the occurrence of open‐water areas is not usually observed.…”

Section: Discussionsupporting

confidence: 68%

“…The greatest accumulation of ice in a cross sectionfrom 10% up to 50%occurred in the Yellow River in the end section of the river bend on the convex bank. Also, according to Wang et al (2012), ice builds up on the convex bank of river bends. On the Vistula, unlike in the case of the Yellow River, after ice cover progression, the occurrence of open-water areas is not usually observed.…”

Section: Discussionmentioning

confidence: 99%

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Internal structure and sources of selected ice jams on the lower Vistula River

Pawłowski

2016

Hydrol. Process.

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The lower stretch of the Vistula is the most ice‐jammed river section on the North European Plain. Since 1982, the structure of hanging dams has been studied by means of a mechanical non‐core sampler. In this article, a selected of field research results of the hanging dams' structure and the degree of filling of the cross section with ice obtained during surface ice‐jam events in the years 1995–2014 are presented, along with an explanation of their causes. Surface ice jams occurred during spring snowmelt surges and ice breakups and also during freeze‐up and ice‐covered periods. Their main cause was changes in the river flow and was also those affected by anthropogenic sources. A characteristic feature of the analysed cross sections was the considerable share of the underhanging ice dam's firm accumulation with ice floes, when the cross section would be filled with ice in excess of 70%. In most cases, due to low river discharge, there was no substantial flooding damage. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Discussionsupporting

confidence: 68%

Section: Discussionmentioning

confidence: 99%

Internal structure and sources of selected ice jams on the lower Vistula River

Pawłowski

2016

Hydrol. Process.

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“…The ice cover thickness in this section ranged from 0.9 to 1.0 m upstream and 0.8-0.9 m downstream, with a rapid increase in the water depth from 3.0 m upstream to 6.0 m downstream, resulting in a 50% decrease in the water velocity. Moreover, this stretch is located along a river bend, resulting in increased resistance to ice flow [20,21]. These factors can cause ice blocks to accumulate in the downstream portion of the reach because of increased flow resistance and decreased velocity [22][23][24].…”

Section: Selection Of Blasting Locationsmentioning

confidence: 99%

Effects of Water Depth and Ice Thickness on Ice Cover Blasting for Ice Jam Flood Prevention: A Case Study on the Heilong River, China

Wang

Guo

et al. 2018

Water

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Ice blasting with explosives is an important method for mitigating or preventing ice jam floods during the spring breakup of frozen rivers. Successful blasting relies on properly determining the relationships between parameters including blasting crater radius, explosive weight, ice cover thickness, and water depth, though variations in the final three factors have significant effects on the blasting crater radius. We conducted field experiments in an upper reach of the Heilong (Amur) River, which forms the border between China and Russia, in order to develop an empirical formula correlating these factors. The blasting crater radius determined by the proposed equation resulted in average errors of less than 8.5% when compared with the measured values. This formula was used for ice blasting along the upper Heilong River in spring 2016 and 2017, successfully preventing ice jam formation during river breakup and thus providing a scientific basis for the prevention of ice-related flooding in northern regions.

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“…For instance, Shen and Haas [6] conducted a 3D model experiment to calculate river age. Jun et al [7] studied the phenomenon and mechanism of ice accretion in a curved channel under different flow and ice flow conditions. Huang [8] carried out a physical simulation study on the height, propagation velocity, tidal current velocity, front gradient, and bore shape of a tidal bore in a rectangular flume.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network

Yan,

Du,

Zhang

et al. 2024

Water

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The hydrodynamic model, based on the strict conservation of momentum and continuity equations, can accurately simulate the distribution of a flow field. However, significant computing time and storage space requirements limit real-time prediction. Machine learning is well known for its fast computing speed and powerful learning ability, but its accuracy depends on an abundance of training data, hindering its wider use in locations without sufficient measurements. Application restrictions in data-deficient areas can be addressed through transfer learning, provided that two areas share common characteristics. In this study, a machine learning method based on a deep super-resolution convolutional neural network (DSRCNN) and transfer learning is proposed, validated, and applied to model two bend flows and one realistic test case. Firstly, the hydrodynamic model was established and validated against measured data. The validated model was considered to have the ability to generate real data and was used to generate a comprehensive data set for training and validating the machine learning model. Three different methods were compared and tested, with Realizable k-ε performing better than the others in predicting the outer bank flow distribution. DSRCNN was compared to a plain SRCNN (PSRCNN), as well as Bilinear, Nearest, and Bicubic methods, and the results showed that DSRCNN had the best performance. We compared Raw, RT, and TL methods, finding that the TL method performed the best overall. Therefore, the research results showed that the developed super-resolution convolutional neural network can provide more reliable predications and serve as an ideal tool for simulating flow field distribution in bends.

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Mechanisms of ice accumulation in a river bend – an experimental study

Cited by 12 publications

References 6 publications

Internal structure and sources of selected ice jams on the lower Vistula River

Internal structure and sources of selected ice jams on the lower Vistula River

Effects of Water Depth and Ice Thickness on Ice Cover Blasting for Ice Jam Flood Prevention: A Case Study on the Heilong River, China

Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network

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