Machine learning accelerated discrete element modeling of granular flows

Machine learning accelerated discrete element modeling of granular flows [electronic resource]

Granular flows are widely encountered in many industrial processes and natural phenomena. Discrete Element Modeling (DEM) is a useful tool for understanding and troubleshooting devices, which handle granular materials. However, its applicability is significantly limited by the huge computational cos...

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Bibliographic Details
Online Access: Full Text (via OSTI)
Corporate Authors: National Energy Technology Laboratory (U.S.) (Researcher), National Energy Technology Laboratory - In-house Research
Format: Government Document Electronic eBook
Language:English
Published: Washington, D.C. : Oak Ridge, Tenn. : United States. Office of the Assistant Secretary of Energy for Fossil Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Department of Energy, 2021.
Subjects:
37 inorganic, organic, physical, and analytical chemistry
Granular flow
Discrete element modeling
Machine learning
Convolutional neural network
Tensorflow
Inorganic, organic, physical, and analytical chemistry

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Call Number: E 1.99:1869680
E 1.99:1869680 Available

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