## About

102

Publications

15,914

Reads

**How we measure 'reads'**

A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more

1,810

Citations

Citations since 2016

Introduction

Numerical simulations of compressible turbulence;
Subgrid-scale models for large eddy simulations of turbulence;
Numerical simulations of compressible Rayleigh-Taylor instability.

Additional affiliations

September 2016 - present

February 2016 - August 2016

August 2013 - January 2016

Education

September 2007 - July 2012

September 2003 - July 2007

## Publications

Publications (102)

A dynamic nonlinear algebraic model with scale-similarity dynamic procedure (DNAM-SSD) is proposed for subgrid-scale (SGS) stress in large-eddy simulation of turbulence. The model coefficients of the DNAM-SSD model are adaptively calculated through the scale-similarity relation, which greatly simplifies the conventional Germano-identity based dynam...

The effects of stratification parameter (Sr) and flow compressibility on mixing and energy transfer of three-dimensional compressible Rayleigh-Taylor turbulence are studied numerically for initial isothermal stratification at Sr. ranging from 0.5 to 3.0 and at Atwood number At=0.5. Flow compressibility plays an important role in the generation of l...

Enstrophy production and flow topology are numerically investigated for statistically stationary compressible isotropic turbulence in vibrational non-equilibrium with a large-scale thermal forcing. The net enstrophy production term is decomposed into solenoidal, dilatational and isotropic dilatational terms based on the Helmholtz decomposition. Fro...

Fourier neural operator (FNO) model is developed for large eddy simulation (LES) of three-dimensional (3D) turbulence. Velocity fields of isotropic turbulence generated by direct numerical simulation (DNS) are used for training the FNO model to predict the filtered velocity field at a given time. The input of the FNO model is the filtered velocity...

Modeling three-dimensional (3D) turbulence by neural networks is difficult because 3D turbulence is highly-nonlinear with high degrees of freedom and the corresponding simulation is memory-intensive. Recently, the attention mechanism has been shown as a promising approach to boost the performance of neural networks on turbulence simulation. However...

Fully connected neural networks (FCNNs) have been developed for the closure of subgrid-scale (SGS) stress and SGS heat flux in large-eddy simulations of compressible turbulent channel flow. The FCNN-based SGS model trained using data with Mach number Ma=3.0 and Reynolds number Re=3000 was applied to situations with different Mach numbers and Reynol...

We study the effect of sub-filter scale (SFS) dynamics on the accuracy of direct deconvolution models (DDM) in large eddy simulation of isotropic turbulence at different filter-to-grid ratios (FGR), by using several types of invertible filters including the Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly deca...

Constant-coefficient spatial gradient models (SGMs) are proposed for the sub-grid scale (SGS) closure in large-eddy simulation (LES) of turbulence. The model coefficients are determined either by expanding the neighboring first-order gradients using the local higher-order gradient or by directly discretizing the local higher-order gradients using f...

The effects of compressibility on the statistics and coherent structures of a temporally developing mixing layer are studied using numerical simulations at convective Mach numbers ranging from $M_c=0.2$ to $1.8$ and at Taylor Reynolds numbers up to 290. As the convective Mach number increases, the streamwise dissipation becomes more effective to su...

High-order gas-kinetic scheme (HGKS) with 5th-order non-compact reconstruction has been well implemented for implicit large eddy simulation (ILES) in nearly incompressible turbulent channel flows. In this study, the HGKS with higher-order non-compact reconstruction and compact reconstruction will be validated in turbulence simulation. For higher-or...

Based on the framework of deconvolutional artificial neural network (DANN) proposed by Yuan et al.[DOI:https://doi.org/10.1063/5.0027146], we extend the DANN approach to model subgrid-scale (SGS) terms in large eddy simulation (LES) of chemically reacting compressible turbulent flow with evident heat release. In constructing the DANN, the normalize...

There are different methods for setting the inlet boundary condition (IBC) in large eddy simulation (LES). However, different methods applied in the same simulation result in different results. In this study, the influence of setting the IBC in the LES with the Lund method, the divergence-free synthetic eddies method (DFSEM), and the digital filter...

Density-unweighted methods in large-eddy simulations (LES) of turbulence have received little attention, and the modeling of unclosed terms using density-unweighted methods even less. We investigate the density-unweighted subgrid-scale (SGS) closure problem for LES of decaying compressible isotropic turbulence at initial turbulent Mach numbers 0.4...

The compressibility effect when an oblique shock wave impinges on a turbulent boundary layer was analyzed with a direct numerical simulation using Helmholtz decomposition. The turbulent intensity near the impinging region is significantly enhanced by the interaction of the shock wave and boundary layer. In particular, the interaction behavior can e...

Previous works have shown that the small-scale information of incompressible homogeneous isotropic turbulence (HIT) is fully recoverable as long as sufficient large-scale structures are continuously enforced through temporally continuous data assimilation (TCDA). In the current work, we show that the assimilation time step can be relaxed to values...

The effects of the wall temperature and local compressibility on the small-scale structures, local flow topology, and enstrophy production in the hypersonic turbulent boundary layer are numerically investigated. The colder wall temperature significantly enhances the unstable topologies and non-focal topologies near the wall, mainly due to stronger...

The decompositions of the skin-friction and heat-transfer coefficients based on the twofold repeated integration in hypersonic transitional and turbulent boundary layers are analysed to give some major reasons of the overshoot phenomena of the wall skin friction and heat transfer. It is shown that the overshoot of the skin-friction coefficient is m...

The contribution of various flow topologies to the subgrid-scale (SGS) flux of kinetic energy in hypersonic turbulent boundary layer for different Mach numbers and wall temperature ratios is investigated by direct numerical simulation. In the far-wall region (approximately [Formula: see text], where y is the wall-normal location and [Formula: see t...

Flow topology and enstrophy production in chemically reacting compressible isotropic turbulence are studied by using numerical simulations with solenoidal forcing at initial turbulent Mach numbers ranging from 0.2 to 0.6 and at initial Taylor Reynolds numbers ranging from 32 to 160. A detailed chemical kinetic mechanism including nine species and 1...

Kinetic energy flux (KEF) is an important physical quantity that characterizes cascades of kinetic energy in turbulent flows. In large-eddy simulation (LES), it is crucial for the subgrid-scale (SGS) model to accurately predict the KEF in turbulence. In this paper, we propose a new eddy-viscosity SGS model constrained by the properly modelled KEF f...

In this paper, we implemented the Boltzmann-equation-based mesoscopic model, developed recently by Chen et al. [“Inverse design of mesoscopic models for compressible flow using the Chapman–Enskog analysis,” Adv. Aerodyn. 3, 5 (2021)], to simulate three-dimensional (3D) forced compressible isotropic turbulence. In this model, both the Prandtl number...

Deep neural network models have shown great potential in accelerating the simulation of fluid dynamic systems. Once trained, these models can make inferences within seconds, thus can be extremely efficient. However, it becomes more difficult for neural networks to make accurate predictions when the flow becomes more chaotic and turbulent at higher...

We establish a deconvolutional artificial-neural-network (D-ANN) approach in large-eddy simulation (LES) of compressible turbulent flow. Filtered variables in the neighboring locations are taken as the inputs of D-ANN to recover original (unfiltered) variables, including density, momentum and pressure. The scale-similarity form is adopted to recons...

A temporally sparse data assimilation (TSDA) strategy for direct numerical simulation (DNS) of incompressible isotropic turbulence (HIT) is proposed using large assimilation time steps. It is shown that the time step in the TSDA can be relaxed to a much coarser level (1 $\sim$ 2 orders larger) than the existing temporally continuous data assimilati...

The effect of wall temperature on the transfer of kinetic energy in a hypersonic turbulent boundary layer for different Mach numbers and wall temperature ratios is studied by direct numerical simulation. A cold wall temperature can enhance the compressibility effect in the near-wall region through increasing the temperature gradient and wall heat f...

In this study, the high-order gas kinetic scheme (GKS) is employed for explicit large eddy simulation (hereafter referred to simply as “LES”) and implicit large eddy simulation (iLES) of turbulent channel flows. The main objective is to compare the performance of iLES and LES in the high-order finite volume framework, and study which is most suitab...

We apply the gene-expression programing (GEP) method to develop subgrid-scale models for large-eddy simulations (LESs) of turbulence. The GEP model is trained based on Galilean invariants and tensor basis functions, and the training data are from direct numerical simulation (DNS) of incompressible isotropic turbulence. The model trained with GEP ha...

The effects of heat sources on the velocity and pressure spectra, Mach number scaling of one-point statistics, and small-scale structures of compressible homogeneous shear turbulence are numerically studied. The dilatational components of flow fields are significantly enhanced by a strong heat source at low turbulent Mach numbers Mt and are dominat...

A dynamic nonlinear algebraic model with scale-similarity dynamic procedure (DNAM-SSD) is proposed for subgrid-scale (SGS) stress in large-eddy simulation of turbulence. The model coefficients of the DNAM-SSD model are adaptively calculated through the scale-similarity relation, which greatly simplifies the conventional Germano-identity based dynam...

The decompositions of the skin-friction and heat transfer coefficients based on the two-fold repeated integration in hypersonic transitional and turbulent boundary layers are analyzed to explain the generations of the wall skin friction and heat transfer. The Reynolds analogy factor slightly increases as the wall temperature decreases, especially f...

This study numerically analyzes the two-dimensional (2D) compressible multi-mode Rayleigh–Taylor instability at different Atwood numbers (At) and stratification parameters (Sr), corresponding to the different levels of flow compressibility. It is found that the growth of bubble thickness is suppressed with the increase in Sr due to the density stra...

The small-scale statistics and local flow topology of compressible homogeneous isotropic turbulence of dense gas are numerically investigated with the turbulent Mach number and Taylor Reynolds number, respectively, nearly equaling 1.0 and 153.0. The initial state of the flow field is in the inversion zone, where the fundamental derivative of gas dy...

Deep neural network models have shown a great potential in accelerating the simulation of fluid dynamic systems. Once trained, these models can make inference within seconds, thus can be extremely efficient. However, they suffer from generalization problem when the flow becomes chaotic and turbulent. One of the most important reasons is that, exist...

A local artificial neural network (LANN) framework is developed for turbulence mod-eling. The Reynolds-averaged Navier-Stokes (RANS) unclosed terms are reconstructed by the artificial neural network based on the local coordinate system which is orthogonal to the curved walls. We verify the proposed model in the flows over periodic hills. The correl...

Dynamic iterative approximate deconvolution (DIAD) models with Galilean invariance are developed for subgrid-scale (SGS) stress in the large-eddy simulation (LES) of turbulence. The DIAD models recover the unfiltered variables using the filtered variables at neighboring points and iteratively update model coefficients without any a priori knowledge...

The transfer of internal energy fluctuation is numerically investigated for the stationary compressible isotropic turbulence in vibrational non-equilibrium with large-scale thermal forcing. We observe the spectra of velocity, solenoidal pressure component, density and temperatures all exhibiting the k −5/3 scaling in the inertial range. The Helmhol...

A dynamic spatial gradient model (DSGM) is proposed for the subgrid-scale (SGS) closure of large-eddy simulation (LES). The velocity gradients at neighboring LES grids are incorporated to improve the accuracy of the SGS stress. Compared to the previous machine-learning-based multi-point gradient models, the current model is free from the need of a...

Kinetic energy transfer in compressible homogeneous anisotropic turbulence is studied by numerical simulations of forced anisotropic turbulence (FAT) in a periodic box and homogeneous shear turbulence (HST) at different turbulent Mach numbers Mt and different Taylor Reynolds numbers Reλ. In both FAT and HST, the subgrid-scale (SGS) kinetic energy f...

For large eddy simulation, it is critical to choose the suitable turbulent inlet boundary condition as it significantly affects the calculated flow field. In this paper, the effect of different inlet boundary conditions, including random method (RAND), Lund method, and divergence-free synthetic eddies method (DFSEM), on the flow in a channel with a...

The compressibility effect in isothermal hypersonic boundary layer is studied with direct numerical simulation (DNS) using Helmholtz decomposition. The dilatational components of the diagonal Reynolds stress are enhanced by the cold wall condition in the near-wall region. The outward (Q1) and ejection (Q2) events are mainly located in the expansion...

The subgrid-scale stress (SGS) of large-eddy simulation (LES) is modeled by artificial neural network-based spatial gradient models (ANNSGMs). The velocity gradients at neighboring stencil locations are incorporated to improve the accuracy of the SGS stress. The consideration of the gradient terms in the stencil locations is in a semi-explicit form...

Interscale kinetic energy transfer in chemically reacting compressible isotropic turbulence
is studied using numerical simulations at turbulent Mach numbers 0.2 and 0.8 for
isothermal and exothermic reactions. At low turbulent Mach number Mt = 0.2 for
exothermic reaction, heat release greatly enhances expansion and compression motions,
and induces...

Recent research indicates that the structure of the wall-bounded turbulence depends on the mean shear created by the wall. To examine the response of the wall turbulence to the rough-wall-like mean shear, we performed numerical tests of the channel flows in the framework of the constrained large-eddy simulation without resolving the roughness eleme...

In this work we extend the method of the constrained large-eddy simulation (CLES) to simulate the turbulent flow over inhomogeneous rough walls. In the original concept of CLES, the subgrid-scale (SGS) stress is constrained so that the mean part and the fluctuation part of the SGS stress can be modelled separately to improve the accuracy of the sim...

A local artificial neural network (LANN) framework is developed for turbulence modeling. The Reynolds-averaged Navier-Stokes (RANS) unclosed terms are reconstructed by artificial neural network (ANN) based on the local coordinate system which is orthogonal to the curved walls. We verify the proposed model for the flows over periodic hills. The corr...

Large eddy simulation (LES) is an important method to investigate different types of complex turbulent flows, which has been widely applied to the turbulent flows in aerospace, combustion, acoustics, atmospheric boundary layer, etc. Large eddy simulation effectively solves the large-scale motions of turbulence and models the effects of small-scale...

Direct numerical simulations of forced homogeneous rotating stratified turbulence are carried out with the same Rossby number and different Froude numbers. We investigated the effects of different stratification on energy transfers across scales and kinetic-potential
energy exchange in Fourier space in the inverse energy cascade range. When the str...

In this paper, a new mesoscopic approach with both the adjustable Prandtl number and the ratio of bulk to shear viscosity has been developed to simulate three-dimensional compressible decaying homogeneous isotropic turbulence under the framework of discrete unified gas kinetic scheme (DUGKS). In the new approach, two reduced model Boltzmann equatio...

Deconvolutional artificial neural network (DANN) models are developed for subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence. The filtered velocities at different spatial points are used as input features of the DANN models to reconstruct the unfiltered velocity. The grid width of the DANN models is chosen to be smaller than th...

In this work, artificial neural network-based nonlinear algebraic models (ANN-NAMs) are developed for the subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence at the Taylor Reynolds number Re λ ranging from 180 to 250. An ANN architecture is applied to construct the coefficients of the general NAM for the SGS anisotropy stress. I...

As an underlying mechanism, cross-chirality transfer of kinetic energy and helicity plays an essential role in the turbulent dynamics, which is as important as cross-scale transfer especially in broken mirror-symmetry turbulence. The effects of helicity on the properties of turbulent flows in previous studies highlight the role of cross chirality,...

Numerical simulations are carried out to study the spectra and statistics in chemically reacting compressible homogeneous isotropic turbulence at turbulent Mach number Mt from 0.1 to 1.0 and at Taylor Reynolds number Reλ from 54 to 103 with solenoidal forcing. A single-step irreversible Arrhenius-type chemical reaction is implemented to evaluate th...

Deconvolutional artificial neural network (DANN) models are developed for subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence. The filtered velocities at different spatial points are used as input features of the DANN models to reconstruct the unfiltered velocity. The grid width of the DANN models is chosen to be smaller than th...

Dual channels of helicity cascade in turbulent flows - Volume 894 - Zheng Yan, Xinliang Li, Changping Yu, Jianchun Wang, Shiyi Chen

Spatial artificial neural network (ANN) models are developed for subgrid-scale (SGS) forces in the large eddy simulation (LES) of turbulence. The input features are based on the first-order derivatives of the filtered velocity field at different spatial locations. The correlation coefficients of SGS forces predicted by the spatial artifical neural...

As pioneered by Donzis and Maqui [J. Fluid Mech. 797, 181 (2016)] and Khurshid and Donzis [Phys. Fluids 31, 015103 (2019)], the compressible isotropic turbulence in thermal nonequilibrium is drawing attention in the fluid dynamics community. In the present study, the vibrational rate and the dissipation or production of vibrational energy fluctuati...

In this paper, the detailed dynamic characteristics of the subgrid scale (SGS) stress tensor and heat flux are investigated through Taylor series expansion in numerical simulations of compressible isotropic turbulence. A new approximate second-order closure (ASOC) model is introduced based on the transport equations of the first-order Taylor series...

The local flow topology based on the invariants of the velocity gradient tensor in stationary compressible homogeneous shear turbulence (HST) is studied by numerical simulations. In the compressible homogeneous shear turbulence, local compressibility decreases the flow volume fraction occupied by the focal, eddy, and shear flow structures both in c...

The effects of flow topology on the subgrid-scale (SGS) kinetic energy flux in compressible isotropic turbulence is studied. The eight flow topological types based on the three invariants of the filtered velocity gradient tensor are analysed at different scales, along with their roles in the magnitude and direction of kinetic energy transfer. The u...

In order to study the effect of compressibility on Rayleigh-Taylor (RT) instability, we numerically simulated the late-time evolution of two-dimensional single-mode RT instability for isothermal background stratification with different isothermal Mach numbers and Atwood numbers (At) using a high-order central compact finite difference scheme. It is...

In this work, subgrid-scale (SGS) stress and SGS heat flux of compressible isotropic turbulence are reconstructed by a spatially multi-scale artificial neural network (SMSANN). The input features of the SMSANN model are based on the first order derivatives of the primary and secondary filtered variables at different spatial locations. The SMSANN mo...

We investigate the long-time evolution of flow structures and the kinetic-potential energy exchange in rotating stratified turbulence, which is of great significance in geophysical flows. Numerical simulations of forced three-dimensional homogeneous rotating stratified turbulence in transient state with different Froude numbers are performed. Numer...

The subgrid-scale (SGS) stress and SGS heat flux are modeled by using an artificial neural network (ANN) for large eddy simulation (LES) of compressible turbulence. The input features of ANN model are based on the first-order and second-order derivatives of filtered velocity and temperature at different spatial locations. The proposed spatial artif...

In this paper, the subgrid-scale (SGS) force and the divergence of SGS heat flux of compressible isotropic turbulence are modeled directly by an artificial neural network (ANN), which serves as a data-driven SGS modeling tool for large-eddy simulations (LESs). The unclosed SGS force and divergence of SGS heat flux are modeled based on the local ste...

The bulk viscosity appearing in the Navier-Stokes equations is generally assumed to be zero for dilute monatomic gases or incompressible fluids. With the growing interest in compressible flows, it is necessary to have a more clear understanding of the role of bulk viscosity and its effects on the properties of flow fields. In the present study, the...

In this work, the subgrid-scale (SGS) stress and the SGS heat flux of compressible isotropic turbulence are modeled by an artificial neural network (ANN) mixed model (ANNMM), which maintains both functional and structural performances. The functional form of the mixed model combining the gradient model and the Smagorinsky’s eddy viscosity model is...