G306 Wind induced flow and sediment transport by natural wind

2012 ◽  
Vol 2012 (0) ◽  
pp. 487-488
Author(s):  
Masahiro HAYASHI ◽  
Shuya YOSHIOKA
Keyword(s):  
Sediment Transport ◽  
Flow And Sediment Transport ◽  
Induced Flow ◽  
Wind Induced Flow ◽  
Natural Wind

scholarly journals NUMERICAL SIMULATIONS OF SEDIMENT TRANSPORT INDUCED BY THE 2004 INDIAN OCEAN TSUNAMI NEAR KIRINDA PORT IN SRI LANKA

2011 ◽  
Vol 1 (32) ◽  
pp. 12 ◽  
Author(s):  
Naoto Kihara ◽  
Masafumi Matsuyama
Keyword(s):  
Sediment Transport ◽  
Sri Lanka ◽  
Indian Ocean ◽  
Numerical Simulations ◽  
Secondary Flow ◽  
Suspended Sediment ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami ◽  
Flow And Sediment Transport ◽  
Induced Flow

Numerical simulations of sediment transport induced by the 2004 Indian Ocean tsunami near Kirinda port in southeastern Sri Lanka is carried out and the relationships between the tsunami-induced flow and sediment transport are discussed. The results show two clear relationships. The first one is that the local scour occurs and a large amount of suspended sediment is generated around the head of breakwaters when the tsunami-induced flow passes through there. The second one is that the suspended sediment is deposited near vortex centers due to a secondary flow.

scholarly journals Application of a 3D Layer Integrated Numerical Model of Flow and Sediment Transport Processes to a Reservoir

Water ◽  
2015 ◽  
Vol 7 (10) ◽  
pp. 5239-5257 ◽  
Author(s):  
Shervin Faghihirad ◽  
Binliang Lin ◽  
Roger Falconer
Keyword(s):  
Sediment Transport ◽  
Numerical Model ◽  
Transport Processes ◽  
Flow And Sediment Transport

scholarly journals NUMERICAL MODEL INVESTIGATION OF SELECTED TIDAL INLET-BAY SYSTEM CHARACTERISTICS

1978 ◽  
Vol 1 (16) ◽  
pp. 76
Author(s):  
William N. Seelig ◽  
Robert M. Sorensen
Keyword(s):  
Sediment Transport ◽  
Numerical Model ◽  
Transport Model ◽  
Tidal Inlet ◽  
Inlet Channel ◽  
Astronomical Tide ◽  
Order Of Magnitude ◽  
Flow And Sediment Transport ◽  
Transport Factors ◽  
System Characteristics

A spatially integrated one-dimensional numerical model of inlet bay hydraulics has been combined with a simple sediment transport model to investigate selected tidal inlet-bay system characteristics. A parametric study has been performed using the models to determine the effect of various factors on the net direction and order of magnitude of inlet channel flow and sediment transport. Factors considered include astronomical tide type, storm surge height and duration, variation in bay surface area, time-dependent channel friction factor, and the addition of a second inlet connecting the bay and sea.

scholarly journals Sediment Transport Dynamism in the Confluence Area of Two Rivers Transporting Mainly Suspended Sediment Based on Sentinel-2 Satellite Images

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3132
Author(s):  
Ahmed Mohsen ◽  
Ferenc Kovács ◽  
Gábor Mezősi ◽  
Tímea Kiss
Keyword(s):  
Sediment Transport ◽  
Suspended Sediment ◽  
Temporal Dynamics ◽  
Pearson Correlation ◽  
Unsupervised Classification ◽  
Mixing Process ◽  
Hydrological Parameters ◽  
Flow And Sediment Transport ◽  
Spatio Temporal ◽  
Sentinel 2

Downstream of the confluence of rivers, complex hydrological and morphological processes control the flow and sediment transport. This study aimed to analyze the spatio-temporal dynamics of suspended sediment in the confluence area of the Tisza and its main tributary Maros River using Sentinel-2 images and to reveal the correlation between the hydrological parameters and the mixing process through a relatively long period (2015–2021). The surficial suspended sediment dynamism was analyzed by applying K-means unsupervised classification algorithm on 143 images. The percentages of the Tisza (TW) and Maros (MW) waters and their mixture (MIX) were calculated and compared with the hydrological parameters in both rivers. The main results revealed that the areal, lateral, and longitudinal extensions of TW and MIX have a better correlation with the hydrological parameters than the MW. The Pearson correlation matrix revealed that the discharge ratio between the rivers controls the mixing process significantly. Altogether, 11 mixing patterns were identified in the confluence area throughout the studied period. The TW usually dominates the confluence in November and January, MW in June and July, and MIX in August and September. Predictive equations for the areal distribution of the three classes were derived to support future water sampling in the confluence area.

Sign in / Sign up

Export Citation Format

Share Document