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

Zhou, Zhideng; Chen, Jin-Cai; Jin, Guodong

doi:10.1007/s00707-017-1877-5

Cited by 6 publications

(2 citation statements)

References 71 publications

(92 reference statements)

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“…The results by the spectral eddy viscosity model coincide with the dynamic Smagorinsky model, which are not shown in the figure (see Ref. [48] ). The Lagrangian statistics obtained from the improved ANN model almost approach those from FDNS, better than the results from the Smagorinsky model and dynamic Smagorinsky model.…”

Section: A Posteriori Validation Of the Ann Modelmentioning

confidence: 61%

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Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network

et al. 2019

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An artificial neural network (ANN) is used to establish the relation between the resolved-scale flow field and the subgrid-scale (SGS) stress tensor, to develop a new SGS model for large-eddy simulation (LES) of isotropic turbulent flows. The data required for training and testing of the ANN are provided by performing filtering operations on the flow fields from direct numerical simulations (DNSs) of isotropic turbulent flows. We use the velocity gradient tensor together with filter width as input features and the SGS stress tensor as the output labels for training the ANN. In the a priori test of the trained ANN model, the SGS stress tensors obtained from the ANN model and the DNS data are compared by computing the correlation coefficient and the relative error of the energy transfer rate. The correlation coefficients are mostly larger than 0.9, and the ANN model can accurately predict the energy transfer rate at different Reynolds numbers and filter widths, showing significant improvement over the conventional models, for example the gradient model, the Smagorinsky model and its dynamic version. A real LES using the trained ANN model is performed as the a posteriori validation. The energy spectrum computed by the improved ANN model is compared with several SGS models. The Lagrangian statistics of fluid particle pairs obtained from the improved ANN model almost approach those from the filtered DNS, better than the results from the Smagorinsky model and dynamic Smagorinsky model.

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Section: A Posteriori Validation Of the Ann Modelmentioning

confidence: 61%

“…To obtain the data for ANN training and testing, we performed filtering operations on the flow fields from DNS of isotropic turbulent flows [47][48][49] . The filtering operations using various filter functions and filter widths are carried out in this section.…”

Section: Data Preparationmentioning

confidence: 99%

Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network

et al. 2019

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Influence of coherent vortex structures in subgrid scale motions on particle statistics in homogeneous isotropic turbulence

Xiong

Fei

et al. 2019

International Journal of Multiphase Flow

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Acceleration statistics of tracer particles in filtered turbulent fields

Lalescu

Wilczek

2018

J. Fluid Mech.

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We present results from direct numerical simulations of tracer particles advected in filtered velocity fields to quantify the impact of the scales of turbulence on Lagrangian acceleration statistics. Systematically removing spatial scales reduces the frequency of extreme acceleration events, consistent with the notion that they are rooted in the small-scale structure of turbulence. We also find that acceleration variance and flatness as a function of filter scale closely resemble experimental results of neutrally buoyant, finite-sized particles, corroborating the picture that particle size determines the scale on which turbulent fluctuations are sampled.

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

Cited by 6 publications

References 71 publications

Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network

Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network

Influence of coherent vortex structures in subgrid scale motions on particle statistics in homogeneous isotropic turbulence

Acceleration statistics of tracer particles in filtered turbulent fields

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