Machine learning for physics-informed generation of dispersed multiphase flow using generative adversarial networks View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-10-28

AUTHORS

B. Siddani, S. Balachandar, W. C. Moore, Y. Yang, R. Fang

ABSTRACT

Fluid flow around a random distribution of stationary spherical particles is a problem of substantial importance in the study of dispersed multiphase flows. In this paper, we present a machine learning methodology using generative adversarial network framework and convolutional neural network architecture to recreate particle-resolved fluid flow around a random distribution of monodispersed particles. The model was applied to various Reynolds number and particle volume fraction combinations spanning over a range of [2.69, 172.96] and [0.11, 0.45], respectively. Test performance of the model for the studied cases is very promising. More... »

PAGES

807-830

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00162-021-00593-9

DOI

http://dx.doi.org/10.1007/s00162-021-00593-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1142245689


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