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A model for tracking inertial particles in a lattice Boltzmann turbulent flow simulation
Affiliation:1. Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA 90089-2905, USA;2. Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843-3123, USA;1. Paul Scherrer Institut, Switzerland;2. Innovative, Technology Development GmbH, Switzerland;3. Nuclear Safety Institute of the Russian Academy of Sciences, Moscow 115191, Russian Federation;4. JSC “Afrikantov OKB Mechanical Engineering”, Nizhny Novgorod 603074, Russian Federation;5. CNL-2251 Speakman Drive, Mississauga, ON L5K 1B2, Canada;6. CEA, DEN, DM2S, STMF, F-91191 Gif-sur-Yvette Cedex, France;7. Forschungszentrum Juelich, 52425 Jülich, Germany;8. RWTH Aachen University, Germany;9. Institut de Radioprotection et de Sûreté Nucléaire, 92269 Fontenay-aux-Roses, France;10. Institut de Radioprotection et de Sûreté Nucléaire, 91192 Gif Sur Yvette, France;11. Karlsruher Institut für Technologie, 76131 Karlsruhe, Germany;12. U.S. Nuclear Regulatory Commission, Washington, DC 20555-0001, United States;13. Nuclear Research and Consultancy Group, 1755 Le Petten, Netherlands
Abstract:A computationally inexpensive model for tracking inertial particles through a turbulent flow is presented and applied to the turbulent flow through a square duct having a friction Reynolds number of Reτ = 300. Prior to introducing particles into the model, the flow is simulated using a lattice Boltzmann computation, which is allowed to evolve until a steady state turbulent flow is achieved. A snapshot of the flow is then stored, and the trajectories of particles are computed through the flow domain under the influence of this static probability field. Although the flow is not computationally evolving during the particle tracking simulation, the local velocity is obtained stochastically from the local probability function, thus allowing the dynamics of the turbulent flow to be resolved from the point of view of the suspended particles. Particle inertia is modeled by using a relaxation parameter based on the particle Stokes number that allows for a particle velocity history to be incorporated during each time step. Wall deposition rates and deposition patterns are obtained and exhibit a high level of agreement with previously obtained DNS computational results and experimental results for a wide range of particle inertia. These results suggest that accurate particle tracking through complex turbulent flows may be feasible given a suitable probability field, such as one obtained from a lattice Boltzmann simulation. This in turn presents a new paradigm for the rapid acquisition of particle transport statistics without the need for concurrent computations of fluid flow evolution.
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