Prediction of hopper discharge rate using combined discrete element method and artificial neural network

2018 ◽  
Vol 29 (11) ◽  
pp. 2822-2834 ◽  
Author(s):  
Raj Kumar ◽  
Chetan M. Patel ◽  
Arun K. Jana ◽  
Srikanth R. Gopireddy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discrete Element Method ◽  
Discharge Rate ◽  
Discrete Element ◽  
Hopper Discharge ◽  
Artificial Neural ◽  
Element Method

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 [...]

Experimental and Numerical Investigation on the Discharge of Wood Pellets From a Hopper With the Discrete Element Method

2012 ◽  
Author(s):  
Dominik Höhner ◽  
Siegmar Wirtz ◽  
Viktor Scherer
Keyword(s):  
Discrete Element Method ◽  
Bulk Density ◽  
Particle Shape ◽  
Discharge Rate ◽  
Discrete Element ◽  
Wall Friction ◽  
Approximation Schemes ◽  
Wood Pellets ◽  
Hopper Discharge ◽  
Element Method

In this study hopper discharge experiments with wood pellets were conducted. The experimental bulk density, flow behavior and discharge rate were compared to corresponding 3-dimensional discrete element simulations with both multi-sphere and polyhedral approximations of the pellet geometry. Additionally a numerical sensitivity analysis for the particle-wall friction was made in order to evaluate the influence of this parameter on hopper discharge in the context of different particle geometries. In the past comparisons of experimentally and numerically obtained results demonstrated the adequacy of the discrete element method for predicting the general discharge behavior of a hopper. Nevertheless, in this study, comparing two different particle shape-approximations, significant differences in terms of bulk density, discharge rate, flow profile and dependency on the particle-wall friction coefficient between both investigated particle-shape approximation schemes could be observed. As a result, particle shape-representation must be considered a significant parameter in DEM-simulations.

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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