scholarly journals Correction: Combination of Discrete Element Method and Artificial Neural Network for Predicting Porosity of Gravel-Bed River. Water 2019, 11(7), 1461

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 408
Author(s):  
Van Hieu Bui ◽  
Minh Duc Bui ◽  
Peter Rutschmann
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discrete Element Method ◽  
River Water ◽  
Discrete Element ◽  
Gravel Bed ◽  
Artificial Neural ◽  
Gravel Bed River ◽  
Element Method

We have found one error in Equation (2) (duplicate with Equation (1)) in our paper published in the Water Journal [1]: (1) m i d u → i dt   = m i g → + ∑ k f → i , k + f → i , f [...]

scholarly journals Combination of Discrete Element Method and Artificial Neural Network for Predicting Porosity of Gravel-Bed River

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1461 ◽  
Author(s):  
Van Hieu Bui ◽  
Minh Duc Bui ◽  
Peter Rutschmann
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Grain Size ◽  
Discrete Element Method ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Discrete Element ◽  
Fine Sediment ◽  
Gravel Bed ◽  
Artificial Neural

In gravel-bed rivers, monitoring porosity is vital for fluvial geomorphology assessment as well as in r ecosystem management. Conventional porosity prediction methods are restricting in terms of the number of considered factors and are also time-consuming. We present a framework, the combination of the Discrete Element Method (DEM) and Artificial Neural Network (ANN), to study the relationship between porosity and the grain size distribution. DEM was applied to simulate the 3D structure of the packing gravel-bed and fine sediment infiltration processes under various forces. The results of the DEM simulations were verified with the experimental data of porosity and fine sediment distribution. Further, an algorithm was developed for calculating high-resolution results of porosity and grain size distribution in vertical and horizontal directions from the DEM results, which were applied to develop a Feed Forward Neural Network (FNN) to predict bed porosity based on grain size distribution. The reliable results of DEM simulation and FNN prediction confirm that our framework is successful in predicting porosity change of gravel-bed.

Multi-physics Analysis of Multi-chip LED Light Source Based on Finite Element Method and Artificial Neural Network

2019 ◽  
Vol 40 (6) ◽  
pp. 795-802 ◽  
Author(s):  
刘宏伟 LIU Hong-wei ◽  
牛萍娟 YU Dan-dan ◽  
郭 凯 NIU Ping-juan ◽  
张建新 ZHANG Zan-yun ◽  
王 闯 GUO Kai ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Light Source ◽  
Led Light ◽  
Artificial Neural ◽  
Element Method
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