| Autor: |
Siddani, B., Balachandar, S., Moore, W. C., Yang, Y., Fang, R. |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Theoretical and Computational Fluid Dynamics 2021 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/s00162-021-00593-9 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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