In the traditional hybrid RANS/LES approaches for the simulation of wall-bounded fluid turbulence, such as detached-eddy simulation (DES), the whole flow domain is divided into an inner layer and an outer layer. Typically the Reynolds-averaged Navier–Stokes (RANS) equations are used for the inner layer, while large-eddy simulation (LES) is used for the outer layer. The transition from the inner-layer solution to the outer-layer solution is often problematic due to the lack of small-scale dynamics in the RANS region. In this paper, we propose to simulate the whole flow domain by large-eddy simulation while enforcing a Reynolds-stress constraint on the subgrid-scale (SGS) stress model in the inner layer. Both the algebraic eddy-viscosity model and the one-equation Spalart–Allmaras (SA) model have been used to constrain the Reynolds stress in the inner layer. In this way, we improve the LES methodology by allowing the mean flow of the inner layer to satisfy the RANS solution while small-scale dynamics is included. We validate the Reynolds-stress-constrained large-eddy simulation (RSC-LES) model by simulating three-dimensional turbulent channel flow and flow past a circular cylinder. Our model is able to predict mean velocity, turbulent stress and skin-friction coefficients more accurately in turbulent channel flow and to estimate the pressure coefficient after separation more precisely in flow past a circular cylinder compared with the pure dynamic Smagorinsky model (DSM) and DES using the same grid resolution. Furthermore, the computational cost of the RSC-LES is almost the same as that of DES.

We investigate energy transfer across scales in three-dimensional compressible magnetohydrodynamic (MHD) turbulence, a model often used to study space and astrophysical plasmas. Analysis shows that kinetic and magnetic energies cascade conservatively from large to small scales in cases with varying degrees of compression. With more compression, energy fluxes due to pressure dilation and subscale mass flux are greater, but conversion between kinetic and magnetic energy by magnetic line stretching is less efficient. Energy transfer between the same fields is dominated by local contributions regardless of compressive effects. In contrast, the conversion between kinetic and internal energy by pressure dilation is dominated by the largest scale contributions. Energy conversion between the velocity and magnetic fields is weakly local.

A practical application of universal wall scalings is near-wall turbulence modeling. In this paper, we exploit the semilocal scaling [Patel, Boersma, and Pecnik, Phys. Rev. Fluids, 2, 084604 (2017)] and derive an eddy conductivity closure for wall-modeled large-eddy simulation of high-speed flows. We show that while the semilocal scaling does not collapse high-speed direct numerical simulation (DNS) data, the resulting eddy conductivity and the wall model work fairly well. The paper attempts to answer the following outstanding question: why the semilocal scaling fails but the resulting eddy conductivity works well. We conduct DNSs of Couette flows at Mach numbers from $M=1.4$ to 6. We add a source term in the energy equation to get a cold wall, a close-to-adiabatic wall, and a hot wall. Detailed analysis of the flows' energy budgets shows that aerodynamic heating is the answer to our question: Aerodynamic heating is not accounted for in Patel et al.'s semilocal scaling but is modeled in the equilibrium wall model. We incorporate aerodynamic heating in the semilocal scaling and show that the new scaling successfully collapses the high-speed DNS data. We also show that incorporating aerodynamic heating or not, the semilocal scaling gives rise to the exact same eddy conductivity, thereby answering the outstanding question.

Dynamic stall on airfoil is of great importance in engineering applications. In the present work, Fourier neural operator (FNO) is applied to predict flow fields during the dynamic stall process of the NACA0012 airfoil. Two cases with different angles of attack are simulated by Reynolds averaged numerical simulation with the Spalart–Allmaras (SA) model at Re=4×104. A prediction model is directly constructed between the flow fields at several previous time nodes and that at the future time node by FNO. The prediction of sequence flow fields based on the iterative prediction strategy is achieved for the dynamic stall. The results show that FNO can achieve a fast and accurate prediction of streamwise velocity, normal velocity, pressure, and vorticity for both cases. The dynamics of vortices around the airfoil is analyzed to demonstrate the prediction accuracy of FNO. In addition, FNOs with different configurations are tested to achieve a lower error and a shorter training time-consuming.

The reshocked turbulent Richtmyer–Meshkov (RM) mixing of two media is the most representative problem of more general and complex turbulent mixing induced by interfacial instabilities, broadly occurring in both nature and engineering applications. An accurate prediction of its evolving of spatial structure and mixing width (MW) is of fundamental importance. However, satisfactory prediction with the large-eddy simulation (LES) has not yet been achieved, even for the most important MW. In this paper, we innovatively solve this problem by combining the idea of the constrained large-eddy simulation (CLES), which succeeded previously only in classical single-medium turbulence, and our recently developed Reynolds averaged Navier–Stokes (RANS) model, which realized a satisfactory prediction of MW. Specifically, in our currently developed CLES model, with the aid of Reynolds decomposition, the unclosed subgrid scale (SGS) LES model is decomposed into two parts, i.e. the averaged and the fluctuating. The averaged part is dominated and modelled by the counterpart of our recently developed RANS model to accurately predict the MW, while the fluctuating part is modelled with the classical Smagorinsky model. Consequently, besides successfully capturing the three-dimensional large-scale structure of turbulence and the evolution of the (normalized) mixed mass, our newly proposed CLES also predicts a satisfactory MW with a very coarse grid. To the best of our knowledge, this is the first time that the LES can yield such a comparable result with experiment.

We apply a machine-learned subgrid-scale model to large-eddy simulations (LES) of heavy particles in isotropic turbulence with different Stokes numbers. The data-driven model, originally developed for high Reynolds number isotropic turbulent flows based on the gene expression programming (GEP) method, has explicit model equations and is for the first time tested in multiphase problems. The performance of the GEP model has been investigated in detail, focusing on the particle statistics including particle acceleration, velocity, and clustering. Compared with the commonly used dynamic Smagorinsky model, the GEP model provides significantly improved predictions on the particle statistics with Stokes numbers varying from 0.01 to 20, showing satisfactory agreement with the results from direct numerical simulations. The reasons for the enhanced predictions of the GEP model are further discussed. As the GEP model is less dissipative and it introduces high-order terms closely related to vorticity distribution, the fine-scale structures usually missing in LES simulations can be better recovered, which are believed to be closely related to the intermittency of particle motion and also particle clustering.