Article: Capecelatro, J; Desjardins, O; Fox, RO; (2016) “Strongly Coupled Fluid-Particle Flows in Vertical Channels. I. Reynolds-Averaged Two-Phase Turbulence Statistics”, Physics of Fluids, 28 (3)
Abstract: Simulations of strongly coupled (i.e., high-mass-loading) fluid-particle flows in vertical channels are performed with the purpose of understanding the fundamental physics of wall-bounded multiphase turbulence. The exact Reynolds-averaged (RA) equations for high-mass-loading suspensions are presented, and the unclosed terms that are retained in the context of fully developed channel flow are evaluated in an Eulerian-Lagrangian (EL) framework for the first time. A key distinction between the RA formulation presented in the current work and previous derivations of multiphase turbulence models is the partitioning of the particle velocity fluctuations into spatially correlated and uncorrelated components, used to define the components of the particle-phase turbulent kinetic energy (TKE) and granular temperature, respectively. The adaptive spatial filtering technique developed in our previous work for homogeneous flows [J. Capecelatro, O. Desjardins, and R. O. Fox, “Numerical study of collisional particle dynamics in cluster-induced turbulence,” J. Fluid Mech. 747, R2 (2014)] is shown to accurately partition the particle velocity fluctuations at all distances from the wall. Strong segregation in the components of granular energy is observed, with the largest values of particle-phase TKE associated with clusters falling near the channel wall, while maximum granular temperature is observed at the center of the channel.
The anisotropy of the Reynolds stresses both near the wall and far away is found to be a crucial component for understanding the distribution of the particle-phase volume fraction. In Part II of this paper, results from the EL simulations are used to validate a multiphase Reynolds-stress turbulence model that correctly predicts the wall-normal distribution of the two-phase turbulence statistics. (C) 2016 AIP Publishing LLC.
Funding Acknowledgement: European Union [246556]; US National Science Foundation [CBET-1437865, CBET-1437903]; National Science Foundation [UCOR0015]
Funding Text: The research leading to the results reported in this work has received funding from the European Union Seventh Framework Programme (No. FP7/2007-2013) under Grant Agreement No. 246556, and from the US National Science Foundation under Grant Nos. CBET-1437865 and CBET-1437903. We would like to acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation (Grant No. UCOR0015).