Three-Dimensional Discrete Element Simulations of Round Hopper in Thermal Power Plant

2012 ◽  
Vol 157-158 ◽  
pp. 45-48
Author(s):  
Qing He ◽  
Dong Mei Du ◽  
Ye Wang ◽  
Zi Rui He

Three-dimensional discrete element process was adopted to simulate the discharge of hopper and the change of flow pattern of particles and dynamic lateral pressure of walls were analyzed in process of discharge. The results showed that three-dimensional discrete element simulation can obtain much more information. Discrete element method can simulate dynamic discharge problems. The dynamic force of vertical direction is much greater than dynamic force of horizontal direction in the process of discharge in the cone section of hopper. When the stored materials in hopper generate arches in the course of discharge, the inertia of floating stored materials will make the pressure of particles above arches hoist, so the walls of hopper may crack.

2021 ◽  
Vol 249 ◽  
pp. 15001
Author(s):  
Daniel N. Wilke ◽  
Paul W. Cleary ◽  
Nicolin Govender

Industrial-scale discrete element simulations typically generate Gigabytes of data per time step, which implies that even opening a single file may require 5 - 15 minutes on conventional magnetic storage devices. Data science’s inherent multi-disciplinary nature makes the extraction of useful information challenging, often leading to undiscovered details or new insights. This study explores the potential of statistical learning to identify potential regions of interest for large scale discrete element simulations. We demonstrate that our in-house knowledge discovery and data mining system (KDS) can decompose large datasets into i) regions of potential interest to the analyst, ii) multiple decompositions that highlight different aspects of the data, iii) simplify interpretation of DEM generated data by focusing attention on the interpretation of automatically decomposed regions, and iv) streamline the analysis of raw DEM data by letting the analyst control the number of decomposition and the way the decompositions are performed. Multiple decompositions can be automated in parallel and compressed, enabling agile engagement with the analyst’s processed data. This study focuses on spatial and not temporal inferences.


2006 ◽  
Vol 23 (1) ◽  
pp. 4-15 ◽  
Author(s):  
Shu‐chun Zuo ◽  
Yong Xu ◽  
Quan‐wen Yang ◽  
Y.T. Feng

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