2019/04/19 by Guiquan Wang, Kee Onn Fong, Wang, Guiquan +5 · 1 citation
Earth and Planetary Sciences · Engineering · Mathematics · Physics and Astronomy · #Aeolian processes and effects #Classical mechanics #Computer simulation #Direct numerical simulation #FOS: Physical sciences #Flow (mathematics) #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Turbulent Flows #Geology #Inertial frame of reference #Mathematical analysis #Mathematics #Mechanics #Numerical analysis #Open-channel flow #Particle (ecology) #Particle Dynamics in Fluid Flows #Physics #Reynolds number #Statistical physics #Stokes number #Turbulence #physics.flu-dyn
paper · pdf · doi:10.48550/arxiv.1904.09042
published in arXiv (Cornell University) (Cornell University)
arxiv created 2019/04/19 · openalex publication_date 2019/04/19 · arxiv updated 2019/04/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This study is concerned with the statistics of vertical turbulent channel flow laden with inertial particles for two different volume concentrations (ΦV = 3 × 10-6 and ΦV = 5 × 10-5) at a Stokes number of St+ = 58.6 based on viscous units. Two independent direct numerical simulation models utilizing the point-particle approach are compared to recent experimental measurements, where all relevant nondimensional parameters are directly matched. While both numerical models are built on the same general approach, details of the implementations are different, particularly regarding how two-way coupling is represented. At low volume loading, both numerical models are in general agreement with the experimental measurements, with certain exceptions near the walls for the wall-normal particle velocity fluctuations. At high loading, these discrepancies are increased, and it is found that particle clustering is overpredicted in the simulations as compared to the experimental observations. Potential reasons for the discrepancies are discussed. As this study is among the first to perform one-to-one comparisons of particle-laden flow statistics between numerical models and experiments, it suggests that continued efforts are required to reconcile differences between the observed behavior and numerical predictions.