Prediction of random packing density and flowability for non-spherical particles by deep convolutional neural networks and Discrete Element Method simulations-Reference-Cited by-同舟云学术

Prediction of random packing density and flowability for non-spherical particles by deep convolutional neural networks and Discrete Element Method simulations

Published:2021-11 Issue: Volume:393 Page:559-581
ISSN:0032-5910
Container-title:Powder Technology
language:en
Short-container-title:Powder Technology

Author:

Hesse Robert,Krull Fabian,Antonyuk Sergiy

Funder

Interreg Europe

Deutsche Forschungsgemeinschaft

Agence nationale de la recherche

Publisher

Elsevier BV

Subject

General Chemical Engineering

Reference142 articles.

1. Three dimensional discrete element models for simulating the filling and emptying of silos: analysis of numerical results;González-Montellano;Comput. Chem. Eng.,2012

2. Analysis of powder rheometry of FT4: Effect of air flow;Nan;Chem. Eng. Sci.,2017

3. On the numerical calibration of discrete element models for the simulation of bulk solids;Gröger;Comp. Aid. Chem. Eng.,2006

4. Granular flow during hopper discharge, Physical review. E, Statistical, nonlinear, and soft matter physics;Hilton,2011

5. Discrete element simulation of granular flow in 2D and 3D hoppers: dependence of discharge rate and wall stress on particle interactions;Langston;Chem. Eng. Sci.,1995

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Digital design and manufacturing of microstructural granular materials;Digital Engineering;2024-09

2. Effect of Particle Shape on the Flow of an Hourglass;Physical Review Letters;2024-07-31

3. On the performance of expert-augmented machine learning with limited experimental data collected from powder particle characteristics used in laser powder bed fusion;The International Journal of Advanced Manufacturing Technology;2024-07-09

4. A machine learning model of liquid bridge force and its application in discrete element method;Construction and Building Materials;2024-01

5. Deep learning-based prediction of the remaining time and future distribution of pebble flow from real-scene images;Chemical Engineering Science;2024-01