Identification of DEM simulation parameters by Artificial Neural Networks and bulk experiments

Luca Benvenuti; Christoph Kloss; Stefan Pirker

doi:10.1016/j.powtec.2016.01.003

Identification of DEM simulation parameters by Artificial Neural Networks and bulk experiments

Luca Benvenuti, Christoph Kloss, Stefan Pirker

Department of Particulate Flow Modelling

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	456-465
Number of pages	10
Journal	Powder Technology
Volume	291
DOIs	https://doi.org/10.1016/j.powtec.2016.01.003
Publication status	Published - 01 Apr 2016

Fields of science

103032 Fluid mechanics
203 Mechanical Engineering
203016 Measurement engineering
203024 Thermodynamics
211104 Metallurgy
204006 Mechanical process engineering
204007 Thermal process engineering
103043 Computational physics

JKU Focus areas

Computation in Informatics and Mathematics
Mechatronics and Information Processing
Engineering and Natural Sciences (in general)

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10.1016/j.powtec.2016.01.003

Cite this

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title = "Identification of DEM simulation parameters by Artificial Neural Networks and bulk experiments",

keywords = "Artificial neural networks, Discrete Element Method (DEM) simulations, Parameter identification",

author = "Luca Benvenuti and Christoph Kloss and Stefan Pirker",

note = "Publisher Copyright: {\textcopyright} 2016 Elsevier B.V.",

year = "2016",

month = apr,

day = "1",

doi = "10.1016/j.powtec.2016.01.003",

language = "English",

volume = "291",

pages = "456--465",

journal = "Powder Technology",

issn = "1873-328X",

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TY - JOUR

T1 - Identification of DEM simulation parameters by Artificial Neural Networks and bulk experiments

AU - Benvenuti, Luca

AU - Kloss, Christoph

AU - Pirker, Stefan

PY - 2016/4/1

Y1 - 2016/4/1

KW - Artificial neural networks

KW - Discrete Element Method (DEM) simulations

KW - Parameter identification

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U2 - 10.1016/j.powtec.2016.01.003

DO - 10.1016/j.powtec.2016.01.003

M3 - Article

SN - 1873-328X

VL - 291

SP - 456

EP - 465

JO - Powder Technology

JF - Powder Technology

ER -

Identification of DEM simulation parameters by Artificial Neural Networks and bulk experiments

Fields of science

JKU Focus areas

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