scholarly journals Numerical Simulation of Tidal Flow and Suspended Sediment Concentration Field in a Marina Project

Author(s):  
Ruijin Zhang ◽  
Jinmei Zhou ◽  
Xuezhi Huang ◽  
Yan Zhang
2021 ◽  
Vol 180 ◽  
pp. 108107
Author(s):  
Guillaume Fromant ◽  
Nicolas Le Dantec ◽  
Yannick Perrot ◽  
France Floc'h ◽  
Anne Lebourges-Dhaussy ◽  
...  

2012 ◽  
Vol 256-259 ◽  
pp. 2573-2576 ◽  
Author(s):  
Chao Feng Tong ◽  
Tao Yin ◽  
Jian Shi ◽  
Yu Yang Shao

For the construction of water conservancy project in the upstream of the Yangtze River and the human activities, the runoff and sediment discharge from the upstream to East China Sea have changed greatly. To explore the distribution characteristics of suspended sediment in Yangtze Estuary under the new upstream boundary condition, a 2-D flow-sediment numerical model including the Yangtze Estuary and the Hangzhou Bay was established. Four different runoffs, which are 4,620m³/s, 11,000m³/s, 75,900m³/s and 90,000m³/s respectively, and the correspond sediment discharges were considered. The result indicates that, with the increase of upstream runoff, the sediment intrusion from the top of the North Branch into the North Branch decreases and the suspended sediment concentration field in the South Branch changes is greater than that in the North Branch. In the same region, the sediment concentration decreases during rising tide while it increases at low tide. The change of the core position for suspended sediment field is insignificant.


2013 ◽  
Vol 11 (4) ◽  
pp. 457-466

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.


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