Neural-network-based filtered drag model for gas-particle flows
Authors:
- Yundi JiangYJYundi Jiang
- Jari KolehmainenJKJari Kolehmainen
- Yile GuYGYile Gu
- Yannis G. KevrekidisYKYannis G. Kevrekidis
- Ali OzelAOAli Ozel
- Sankaran SundaresanSSSankaran Sundaresan
- 1 March 2019
- journal article
- research article
- Published by Elsevier in Powder Technology
- Vol. 346, 403-413
Abstract
No abstract availableKeywords
This publication has 33 references indexed in Scilit:
- Filtered and heterogeneity‐based subgrid modifications for gas–solid drag and solid stresses in bubbling fluidized bedsAIChE Journal, 2013
- A functional subgrid drift velocity model for filtered drag prediction in dense fluidized bedAIChE Journal, 2011
- Filtered two‐fluid models for fluidized gas‐particle suspensionsAIChE Journal, 2008
- The effects of mesoscale structures on the macroscopic momentum equations for two-phase flowsInternational Journal of Multiphase Flow, 2002
- The role of meso-scale structures in rapid gas–solid flowsJournal of Fluid Mechanics, 2001
- Comparative analysis of CFD models of dense gas–solid systemsAIChE Journal, 2001
- Dense fluid transport for inelastic hard spheresPhysical Review E, 1999
- Kinetic theory for a monodisperse gas–solid suspensionPhysics of Fluids A: Fluid Dynamics, 1990
- Frictional–collisional constitutive relations for granular materials, with application to plane shearingJournal of Fluid Mechanics, 1987
- Kinetic theories for granular flow: inelastic particles in Couette flow and slightly inelastic particles in a general flowfieldJournal of Fluid Mechanics, 1984