Prediction of Lagrangian dispersion of fluid particles in isotropic turbulent flows using large-eddy simulation method

• Published in: Acta mechanica Vol. 228; no. 9; pp. 3203 - 3222
• Main Authors: Zhou, Zhideng, Chen, Jincai, Jin, Guodong
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.09.2017; Springer Nature B.V
• Subjects: Atoms & subatomic particles; Classical and Continuum Physics; Computational fluid dynamics; Control; Direct numerical simulation; Dispersions; Dynamical Systems; Eddy viscosity; Engineering; Engineering Thermodynamics; Filtration; Fluid dynamics; Fluid flow; Heat and Mass Transfer; Large eddy simulation; Mathematical models; Original Paper; Reynolds number; Similarity theory; Simulation; Solid Mechanics; Theoretical and Applied Mechanics; Turbulence; Velocity; Vibration; Vortices
• ISSN: 0001-5970; 1619-6937
• DOI: 10.1007/s00707-017-1877-5

The single-, two- and multi-particle dispersions in isotropic turbulent flows are investigated using the direct numerical simulation (DNS), filtered DNS (FDNS) and large-eddy simulation (LES) with a spectral eddy viscosity subgrid scale (SGS) model. The contributions of filtering operation and SGS model to the dispersions are separately studied by comparing the statistics obtained from the three methods. The missing SGS motions in LES can be observed to significantly hinder two-particle and four-particle dispersions if the initial separations are less than or comparable to the filter width. A theoretical analysis of the non-monotonic behavior at short time of the one-time, two-point Lagrangian velocity correlation functions with large initial separations based on the Taylor expansion and the Kolmogorov similarity theory is derived, and the Reynolds number effect on the performance of the spectral eddy viscosity SGS model is also investigated. The results show that the SGS model used performs better with increasing Reynolds numbers. It is concluded that the particle SGS model is needed to be developed to correctly capture the Lagrangian two- and multi-point dispersion statistics of fluid particles.