4.5 Article

Structural subgrid scale model based on wavelet filter for large eddy simulation of particle-laden turbulence

期刊

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijmultiphaseflow.2021.103730

关键词

Particle-laden flow; Subgrid scale; Coherent vortex extraction; Wavelet filter; Differential filter model

资金

  1. National Natural Science Foundation of China [51876076, 51876075]
  2. Foundation of the State Key Laboratory of Coal Combustion [FSKLCCB1702]
  3. Young Talents Program of China National Nuclear Corporation (CNNC)

向作者/读者索取更多资源

This study introduces a new large eddy simulation strategy that combines wavelet filtered large eddy simulation with a differential filter SGS model, showing superior performance in recovering coherent vortex structures and achieving better agreement with DNS results.
Coherent vortex structures in subgrid scale (SGS) motions are important for preferential concentration and collision of particles with small and intermediate Stokes number (Xiong et al., 2019). In this study, a new large eddy simulation (LES) strategy that combines wavelet filtered large eddy simulation with a differential filter SGS model is proposed. First, a wavelet filtered direct numerical simulation (WFDNS) enables good preservation of SGS structures due to the high compressibility and local fidelity of the wavelet filter. Second, a differential filter (DF) model, containing only a parameter related to the nominal filter width, is used to dynamically reconstruct unresolved eddies. The SGS model presented here is verified using direct numerical simulation (DNS) data for particle-laden homogenous isotropic turbulence. Compared to the classical spectral-filtered DNS (FDNS) model, the new SGS model enables to achieve better agreement with DNS results related to the dispersed-phase statistics, such as particle acceleration, particle-seen fluid kinetic energy, particle-seen Lagrangian integral time etc. Furthermore, obtained results also exhibit the advantages of a proposed model over the stochastic Langevin model in the prediction of particle dynamics, especially collision-related statistics such as radial distribution function, radial relative velocities, and particle collision rates. Moreover, the model also demonstrates good performance in application to particle-pair related statistics at different Reynolds numbers and different filter depths. This is attributed to the ability of this model to multispectrally enhance coherent vortex structures in SGS, which is promising to apply for recovering SGS effects in the LES of turbulent particle-laden flows.

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