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physics.comp-ph2019
Deep learning for presumed probability density function models
Marc T. Henry de Frahan, Shashank Yellapantula, Ryan King +2
In this work, we use ML techniques to develop presumed PDF models for large eddy simulations of reacting flows. The joint sub-filter PDF of mixture fraction and progress variable i…
physics.comp-ph2018
An adaptive timestepping methodology for particle advance in coupled CFD-DEM simulations
Hariswaran Sitaraman, Ray Grout
An adpative integration technique for time advancement of particle motion in the context of coupled computational fluid dynamics (CFD) - discrete element method (DEM) simulations i…