Subgrid Scale Research Articles

LES and ILES Simulations of Free-Jets

A systematic numerical study was performed to investigate the influence of subgrid-scale (SGS) treatments on the simulation of turbulent free-jets. Large eddy simulations (LES) of such flows were performed to assess the accuracy of two SGS approaches: the detached eddy simulation and the dynamic Smagorinsky model (DSM). The Non-oscillatory Forward in Time-Multidimensional Positive Definite Advection Transport Algorithm (NFT-MPDATA) numerical scheme was employed to integrate the Navier–Stokes equations for incompressible flows. MPDATA due to its self-regularisation property is used to implicitly provide SGS effects. Two options of implicit LES (ILES) are investigated: ILES-NS, which solves the Navier–Stokes equations without an explicit SGS model, and ILES-EU that solves the Euler equations where viscous terms are absent. The performance of each approach was evaluated focusing on key global characteristics of jets, self-similar properties, and energy spectra. Quantitative and qualitative comparisons showed that all simulations were in good agreement with laboratory experiments, prior numerical studies, and each other, thus confirming the validity of the numerical approach and suitability of ILES for this class of flows. Additionally, energy spectra analysis revealed that ILES can reproduce the -5/3 and -7 gradients that signify the universal inertia subrange and dissipation range for turbulent free-jets.

Acoustic Response of a Vibrating Elongated Cylinder in a Hydrodynamic Turbulent Flow

The present paper contains the results of the numerical analysis of the interaction between a Newtonian incompressible turbulent flow and a linear elastic slender body, together with the influence of the fluid–structure interaction (FSI) on the noise generation and propagation. The purpose is to evaluate the differences in term of acoustic pressure between the case where the solid body is rigid (infinite stiffness) and the case where it is elastic (finite stiffness). A partitioned and implicit algorithm with the arbitrary Lagrangian–Eulerian method (ALE) is used for the interaction between the fluid and solid. For the evaluation of the turbulent fluid motion, we use a large eddy simulation (LES) with the Smagorinsky subgrid scale model. The equation for the solid is solved through the Lagrangian description of the momentum equation and the second Piola–Kirchoff stress tensor. In addition, the acoustic analogy of Lighthill is used to characterize the acoustic source (the slender body) by directly using the fluid dynamic fields. In particular, we use the Ffowcs Williams and Hawkings (FW-H) equation for the evaluation of the acoustic pressure in the fluid medium. As a first numerical experiment, we analyze a square cylinder immersed in a turbulent flow characterized by two different values of stiffness: one infinite (rigid case) and one finite (elastic case). In the latter case, the body stiffness and mean flow velocity are such that they induce the lock-in phenomenon. Finally, we evaluate the differences in terms of acoustic pressure between the two different cases.

Dynamic iterative approximate deconvolution model for large-eddy simulation of dense gas compressible turbulence

We study large-eddy simulation of compressible decaying isotropic turbulence of dense gas at initial turbulent Mach numbers of 0.4 and 0.8. The unclosed subgrid-scale (SGS) terms are approximated by the dynamic iterative approximate deconvolution (DIAD) model proposed by Yuan et al. [“Dynamic iterative approximate deconvolution models for large-eddy simulation of turbulence,” Phys. Fluids 33, 085125 (2021)], and compared with the dynamic Smagorinsky (DSM) model. In an a priori test, the correlation coefficients of the DIAD model for most SGS terms are larger than 0.98, and the relative errors are smaller than 0.2, except for the SGS internal energy flux. In an a posteriori test, the DIAD model can well predict the probability density functions (PDFs) of SGS terms involving thermodynamic variables. Moreover, the DIAD model shows greater advantages than the DSM model in predicting various statistics and structures of compressible turbulence of dense gas, including spectra of velocity and thermodynamic variables, PDFs of SGS kinetic energy flux, deviatoric SGS stress and normalized strain-rate tensor, and the instantaneous spatial structures of vorticity.

Effect of heat source on kinetic energy transfer in compressible homogeneous shear turbulence

The effects of heat sources on kinetic energy transfer in compressible homogeneous shear turbulence are studied using numerical simulations at turbulent Mach numbers 0.1 and 0.4 for two levels of heat source. It is found that the strong heat source can significantly enhance both positive and negative components of subgrid-scale (SGS) kinetic energy flux and pressure–dilatation. After adding a strong heat source, compression motions enhance the positive SGS flux, and expansion motions enhance the negative SGS flux at a low turbulent Mach number. According to the Helmholtz decomposition, we found that the solenoidal and dilatational components of pressure–dilatation and SGS kinetic energy flux are increased greatly by a strong heat source at a low turbulent Mach number. The solenoidal mode plays a dominant role in the kinetic energy transfer process, but the contribution of the dilatational mode is not negligible. The dilatational component of the production term is increased by a strong heat source at a low turbulent Mach number, providing the main source of kinetic energy to the dilatational mode. The strong heat source also enhances the kinetic energy exchange between solenoidal mode and dilatational mode through nonlinear advection at a low turbulent Mach number. Moreover, the strong heat source enhances pressure anisotropy, redistribution of the kinetic energy of two transverse components, and energy transfer from internal energy to the kinetic energy through pressure–dilatation term. At a high turbulent Mach number, the strong heat source has little impact on the solenoidal and dilatational components of kinetic energy transfer terms.

Variations of Subgrid-Scale Turbulent Fluxes in the Dry Convective Boundary Layer at Gray Zone Resolutions

Abstract In the numerical gray zone of the convective boundary layer (CBL), the horizontal resolution is comparable to the size of organized convective circulation. As turbulence becomes partially resolved, gridscale variations of the subgrid-scale (SGS) turbulent fluxes become significant compared to the mean. Previously, such variations have often been ignored in scale-adaptive planetary boundary layer (PBL) schemes developed for the gray zone. This study investigates these variations with respect to height and resolution based on large-eddy simulations. It is found that SGS fluxes exhibit maximum variability at the center of the gray zone, where the resolved and the SGS mean fluxes are approximately equal. A simple analytical model is used to associate such characteristic variations to the nonlinear interactions of the dominant energy-containing mode of CBL turbulence. Examination of the horizontal distribution of the SGS fluxes reveals their preferential location over the updraft edges surrounding the core. A priori analysis further suggests the ability of a scale-similarity closure to reproduce the unique spatial patterns of the SGS fluxes at gray zone resolutions. Four scale-adaptive PBL schemes are evaluated focusing on their representations of the modeled SGS flux variability. Their shared shortcomings as a result of their gradient diffusion–based formulation are exposed. This study suggests that a mixed model consisting of a scale-adaptive PBL scheme to represent the mean, and a scale-similarity component to account for gridscale variability to be advantageous for the gray zone. Significance Statement As Gresho and Lee’s famous quote on numerical schemes goes, “Don’t suppress the wiggles. They’re telling you something!” In the numerical gray zone, where the grid spacing is comparable to the characteristic length scale of the flow, turbulence is partially resolved. The “wiggles” (or gridscale variability) reflecting the resolved heterogeneity of turbulent fluxes is an outstanding feature of the gray zone. However, they are often overlooked as error bars to the mean. This study uncovers the significance of these error bars, and characterizes and explains their variations with height and grid spacing. A suitable model that captures such variations is investigated to help build better planetary boundary layer schemes.

Deep Learning Based Cloud Cover Parameterization for ICON.

A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-resolving model (SRM) simulations. The ICOsahedral Non-hydrostatic (ICON) modeling framework permits simulations ranging from numerical weather prediction to climate projections, making it an ideal target to develop neural network (NN) based parameterizations for sub-grid scale processes. Within the ICON framework, we train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON SRM simulations. We set up three different types of NNs that differ in the degree of vertical locality they assume for diagnosing cloud cover from coarse-grained atmospheric state variables. The NNs accurately estimate sub-grid scale cloud cover from coarse-grained data that has similar geographical characteristics as their training data. Additionally, globally trained NNs can reproduce sub-grid scale cloud cover of the regional SRM simulation. Using the game-theory based interpretability library SHapley Additive exPlanations, we identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained SRM data. The interpretability tool also helps visualize similarities and differences in feature importance between regionally and globally trained column-based NNs, and reveals a local relationship between their cloud cover predictions and the thermodynamic environment. Our results show the potential of deep learning to derive accurate yet interpretable cloud cover parameterizations from global SRMs, and suggest that neighborhood-based models may be a good compromise between accuracy and generalizability.

Deep learning method for the super-resolution reconstruction of small-scale motions in large-eddy simulation

A super-resolution reconstruction model for the subgrid scale (SGS) turbulent flow field in large-eddy simulation (LES) is proposed, and it is called the meta-learning deep convolutional neural network (MLDCNN). Direct numerical simulation (DNS) data of isotropic turbulence are used as the dataset of the model. The MLDCNN is an unsupervised learning model, which only includes high-resolution DNS data without manually inputting preprocessed low-resolution data. In this model, the training process adopts the meta-learning method. First, in the a priori test, the SGS turbulent flow motions in the filtered DNS (FDNS) flow field are reconstructed, and the energy spectrum and probability density function of the velocity gradient of the DNS flow field are reconstructed with high accuracy. Then, in the a posteriori test, the super-resolution reconstruction of the LES flow field is carried out. The difficulty of LES flow field reconstruction is that it contains filtering loss and subgrid model errors relative to the DNS flow field. The super-resolution reconstruction of the LES flow field achieves good results through this unsupervised learning model. The proposed model makes a good prediction of small-scale motions in the LES flow field. This work improves the prediction accuracy of LES, which is crucial for the phenomena dominated by small-scale motions, such as relative motions of particles suspended in turbulent flows.

Evaluation of Model Summertime Boundary Layer Cloud Development over Complex Terrain in New York State

Abstract Weather forecasting over complex terrain with diverse land cover is challenging. Utilizing the high-resolution observations from New York State Mesonet (NYSM), we are able to evaluate the surface processes of the Weather Research Forecast (WRF) Model in a detailed, scale-dependent manner. In the study, possible impacts of land–atmosphere interaction on surface meteorology and boundary layer cloud development are investigated with different model resolutions, land surface models (LSMs), and planetary boundary layer (PBL) physical parameterizations. The High-Resolution Rapid Refresh, version 3 (HRRR), forecasting model is used as a reference for the sensitivity evaluation. Results show that over complex terrain, the high-resolution simulations (1 km × 60 vertical levels) generally perform better compared to low-resolution (3 km × 50 levels) in both surface meteorology and cloud fields. LSMs play a more important role in surface meteorology compared to PBL schemes. The NoahMP land surface model exhibits daytime warmer and drier biases compared to the Rapid Update Cycle (RUC) due to better prediction of the Bowen ratio in RUC. The PBL schemes would affect the convective strength in the boundary layer. The Shin–Hong (SH) scale-aware scheme tends to produce the strongest convective strength in the PBL, while the ACM2 PBL scheme rarely resolved convection even at 1-km resolution. By considering the radiation effect of subgrid-scale (SGS) clouds, the Mellor–Yamada–Nakanishi–Niino eddy diffusivity mass flux (MYNN-EDMF) predicted the highest cloud coverage and lowest surface solar radiation bias. The configuration of SGS clouds in MYNN-EDMF would not only significantly reduce shortwave radiation bias, but also affect the convection behaviors through land surface–cloud–radiation interaction.

Suitability of an Artificial Viscosity Model for Compressible Under-Resolved Turbulence Using a Flux Reconstruction Method

In the simulation of compressible turbulent flows via a high-order flux reconstruction framework, the artificial viscosity model plays an important role to ensure robustness in the strongly compressible region. However, the impact of the artificial viscosity model in under-resolved regions on dissipation features or resolving ability remains unclear. In this work, the performance of a dilation-based (DB) artificial viscosity model to simulate under-resolved turbulent flows in a high-order flux reconstruction (FR) framework is investigated. Comparison is conducted with results via several typical explicit subgrid scale (SGS) models as well as implicit large eddy simulation (iLES) and their impact on important diagnostic quantities including turbulent kinetic energy, total dissipation rate of kinetic energy, and energy spectra are discussed. The dissipation rate of kinetic energy is decomposed into several components including those resulting from explicit SGS models or Laplacian artificial viscosity model; thus, an explicit evaluation of the dissipation rate led by those modeling terms is presented. The test cases consist of the Taylor-Green vortex (TGV) problem at Re=1600, the freely decaying homogeneous isotropic turbulence (HIT) at Mat0=0.5 (the initial turbulent Mach number ), the compressible TGV at Mach number 1.25 and the compressible channel flow at Reb= 15,334 (the bulk Reynolds number based on bulk density, bulk velocity and half-height of the channel), Mach number 1.5. The first two cases show that the DB model behaves similarly to the SGS models in terms of dissipation and has the potential to improve the insufficient dissipation of iLES with the fourth-order-accurate FR method. The last two cases further demonstrate the ability of the DB method on compresssible under-resolved turbulence and/or wall-bounded turbulence. The results of this work suggest the general suitability of the DB model to simulate under-resolved compressible turbulence in the high order flux reconstruction framework and also suggest some future work on controlling the potential excessive dissipation caused by the dilation term.

Time dependent subgrid multiscale stabilized finite element analysis of fully coupled transient Navier–Stokes‐transport model

AbstractIn this paper, a fully coupled system of transient Navier–Stokes fluid flow model and unsteady variable coefficient advection–diffusion–reaction transport model has been studied through subgrid multiscale stabilized finite element method. In particular algebraic approach of approximating the subscales has been considered to arrive at the stabilized variational formulation of the coupled system and standard expressions for the stabilization parameters have been proposed. The unknown subgrid scales are considered to be time dependent. The consideration of the fluid viscosity coefficient depending upon the concentration of the solute mass makes this coupling strong. Fully implicit backward Euler scheme has been employed for time discretization. Stability analysis of the stabilized formulation has been conducted. Furthermore detailed derivations of both and error estimates for the stabilized finite element scheme have been carried out. The performance of the proposed scheme is validated for benchmark problems as well as the credibility of the stabilized method is also established well through various numerical experiments.

Frame invariant neural network closures for Kraichnan turbulence

Numerical simulations of geophysical and atmospheric flows have to rely on parameterizations of subgrid scale processes due to their limited spatial resolution. Despite substantial progress in developing parameterization (or closure) models for subgrid scale (SGS) processes using physical insights and mathematical approximations, they remain imperfect and can lead to inaccurate predictions. In recent years, machine learning has been successful in extracting complex patterns from high-resolution spatio-temporal data, leading to improved parameterization models, and ultimately better coarse grid prediction. However, the inability to satisfy known physics and poor generalization hinders the application of these models for real-world problems. In this work, we put forth a frame invariant closure approach to improve the accuracy and generalizability of deep learning-based subgrid scale closure models by embedding physical symmetries directly into the structure of the neural network. Specifically, we utilized specialized layers within the convolutional neural network in such a way that desired constraints are theoretically guaranteed without the need for any regularization terms. We demonstrate our framework for a two-dimensional decaying turbulence test case mostly characterized by the forward enstrophy cascade. We show that our frame invariant SGS model (i) accurately predicts the subgrid scale source term, (ii) respects the physical symmetries such as translation, Galilean, and rotation invariance, and (iii) is numerically stable when implemented in coarse-grid simulation with generalization to different initial conditions and Reynolds number. This work opens up a possibility of connecting physics-based theories and data-driven modeling paradigms, and thus represents a promising step towards the development of physically consistent data-driven turbulence closure models.

PeleLM-FDF large eddy simulator of turbulent reacting flows

A new computational methodology, termed ‘PeleLM-FDF’ is developed and utilised for high fidelity large eddy simulation (LES) of complex turbulent combustion systems. This methodology is constructed via a hybrid scheme combining the Eulerian PeleLM base flow solver with the Lagrangian Monte Carlo simulator of the filtered density function (FDF) for the subgrid scale reactive scalars. The resulting methodology is capable of simulating some of the most intricate physics of complex turbulence-combustion interactions. This is demonstrated by LES of a non-premixed CO/H temporally evolving jet flame. The chemistry is modelled via a skeletal kinetics model, and the results are appraised via a posteriori comparisons against direct numerical simulation (DNS) data of the same flame. Excellent agreements are observed for the time evolution of various statistics of the thermo-chemical quantities, including the manifolds of the multi-scalar mixing. The new methodology is capable of capturing the complex phenomena of flame-extinction and re-ignition at a 1/512 of the computational cost of the DNS. The high fidelity and the computational affordability of the new PeleLM-FDF solver warrants its consideration for LES of practical turbulent combustion systems.

Implicit large-eddy simulation of turbulent plane jet at Re = 104

In this study, implicit large-eddy simulations (ILESs) are performed for a turbulent plane jet at a Reynolds number 104 to assess the capability of ILESs in simulating turbulent transport in free shear flows. To investigate the effect of resolved turbulent scales on the self-similar behavior of the plane jet, ILESs are conducted using fifth- (ILES+P5), seventh- (ILES+P7), and ninth-order polynomial (ILES+P9) interpolation methods, as well as the third-order MUSCL method (ILES+M3). ILES+P5, ILES+P7, and ILES+P9 reproduced the results of the direct numerical simulation (DNS) for a plane jet at Re = 104. The resolution of the power spectral density of ILESs with high-order interpolation schemes, which agrees well with the DNS, increases as the order of spatial accuracy increases and resolves more than 80% of the total energy. Hence, we conclude that the spatial resolution to resolve up to the frequency region that contains approximately 80% of the turbulent energy is required to obtain good agreement with the results of DNS, which is widely accepted in LES with the subgrid-scale (SGS) model and is also valid for ILES.

Near-autonomous large eddy simulations of turbulence based on interscale energy transfer among resolved scales

Numerical simulations of turbulent flows at high Reynolds numbers rely on postulated turbulence models. We show that a model can be obtained by computing subgrid scale (SGS) energy transfer from the actual, resolved velocity fields in the course of simulations. This information, supplemented by asymptotic properties of the energy flux in the inertial range, allows self-contained simulations without postulating extraneous SGS models. For high Reynolds numbers such autonomous simulations lead to the inertial range ${k}^{\ensuremath{-}5/3}$ spectrum with the correct value of the Kolmogorov constant.

A modified wall-adapting local eddy-viscosity model for large-eddy simulation of compressible wall-bounded flow

The wall-adapting local eddy-viscosity (WALE) model in large-eddy simulation can well predict wall-bounded flows but it is also well known for excessive dissipation. In this study, we apply the minimum-dissipation model to constrain the WALE model in compressible flows and obtain the coefficient of the WALE model. Through this process, the dissipation of WALE model can be lower while it still maintains strong stability. In the modified WALE model, the isotropic part of the subgrid-scale (SGS) stress is also reconstructed. In the filtered total energy equation, all of the extra SGS unclosed terms (besides SGS stress and SGS heat flux) are modeled instead of neglecting some SGS terms, such as the SGS viscous diffusion. The modified WALE model is tested in a compressible turbulent channel flow and a supersonic turbulent boundary layer over a compression corner. The new model can well predict the mean velocity, the mean temperature, the Reynolds stress, and the separation bubble.

Impacts of Cumulus Convection and Turbulence Parameterizations on the Convection-Permitting Simulation of Typhoon Precipitation

Abstract Convection-permitting resolutions, which refer to kilometer-scale horizontal grid spacings, have become increasingly popular in regional numerical weather prediction and climate studies. However, this resolution range is in the gray zone for the simulation of convection, where conventional cumulus convection and subgrid-scale (SGS) turbulence parameterizations are inadequate for such grid spacings due to invalid assumptions and simplifications. Recent studies demonstrated that the magnitudes of SGS fluxes of momentum and scalars are comparable to those of resolved fluxes at convection-permitting resolutions and that horizontal SGS components are as important as the vertical SGS component. Thus, it appears necessary to adapt available schemes to model the SGS effects of convective motions for the gray zone. Here, we investigated the efficacy of separately parameterizing the vertical and horizontal SGS effects in improving the convection-permitting simulation of Typhoon Vicente (2012). To represent the vertical SGS turbulence effect, we evaluated the Grell-3, Tiedtke, and multiscale Kain–Fritsch (MSKF) schemes in the Weather Research and Forecasting (WRF) Model; the MSKF scheme is scale adaptive, whereas the other two are conventional cumulus schemes. For horizontal SGS turbulence, we evaluated the effects of the traditional Smagorinsky scheme and our newly developed reconstruction and nonlinear anisotropy (RNA) model, which models not only downgradient diffusion but also backscatter. We found that the simulation combining the MSKF and RNA schemes exhibits the best skill in predicting precipitation, especially rainfall extremes. The advantages are rooted in the MSKF scheme’s scale-awareness and parameterized cloud–radiation feedback and in the backscatter-enabling capability of the RNA model. Significance Statement Operational numerical weather prediction and some climate simulations have approached kilometer-scale horizontal resolutions, called convection-permitting resolutions. However, details of convective storms are not well represented at these resolutions, and small-scale fluid motions can potentially impact the overall simulation performance. In practice, the effects of such unresolved turbulent eddies were once neglected. We suggest representing these effects in the vertical and horizontal directions with an adaptive cumulus convection parameterization and an advanced turbulence model, respectively, which significantly improve the simulation of tropical cyclones. This framework allows us to adapt convection schemes developed by the mesoscale modeling community and turbulence schemes studied by large-eddy simulation groups for representing three-dimensional turbulence in the convection-permitting regime.

Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow

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 Reynolds numbers. The input variables of the neural network model were the filtered velocity gradients and temperature gradients at a single spatial grid point. The a priori test showed that the FCNN model had a correlation coefficient larger than 0.91 and a relative error smaller than 0.43, with much better reconstructions of SGS unclosed terms than the dynamic Smagorinsky model (DSM). In a posteriori test, the behavior of the FCNN model was marginally better than that of the DSM in predicting the mean velocity profiles, mean temperature profiles, turbulent intensities, total Reynolds stress, total Reynolds heat flux, and mean SGS flux of kinetic energy, and outperformed the Smagorinsky model.

Mixing and energy transfer in compressible Rayleigh-Taylor turbulence for initial isothermal stratification

In compressible Rayleigh--Taylor instability, flow compressibility plays an important role in the generation of large-scale kinetic energy, which mainly comes from the conversion of potential energy for small stratification parameter (Sr) and conversion of internal energy through pressure-dilatation work for large Sr. The latter leads to bubble heights increasing rapidly and bubbles that are bigger at large Sr. The overall statistics of normalized subgrid-scale (SGS) flux of kinetic energy is nearly independent of Sr, but the reverse SGS flux increases significantly with increase of Sr. The compression motions enhance direct SGS flux and the expansion motions strengthen the reverse SGS flux.

Investigation on multiscale features of cavitating flow in convergent-divergent test section using Eulerian–Lagrangian method

Cavitating flows in fluid machineries always present complicated phase morphologies including a wide range of interface length scales. In the present work, an Eulerian–Lagrangian cavitation model is proposed for simulating the unsteady cavitating flow and investigating the multiscale cavitating flow features in a convergent-divergent test section. Most importantly, a two-way transition algorithm is newly developed mainly based on the shape of resolved cavities and the relative size of microscale bubbles compared to the cells, for taking the transition between microscale discrete bubbles and macroscale vapor cavities into consideration. The transient flow field of the two-phase system is described by the filtered Navier–Stokes equation and the sub-grid scale model. The volume of fluid (VOF) method and a discrete bubble model (DBM) are combined for simulating resolvable water-vapor interfaces and unresolvable discrete bubbles, respectively. Applying the proposed multiscale modeling framework, the dissipated bubbles caused by the collapse of shedding cavities that are unable to be represented by the VOF method are well predicted. The results show that the VOF-DBM model is less sensitive to the mesh resolution compared to the VOF method and it is found the dissipated bubbles are transporting around the vortex core region. Detailed bubble dynamics including DBM to VOF and VOF to DBM transitions and the interaction between macroscale cavities and microscale bubbles are well presented, indicating that the proposed VOF-DBM model is a promising approach to reveal the multiscale cavitation characteristics within limited computing resources.

Evaluation of Enhanced Gray Area Mitigation Approaches Based on Jet Aeroacoustics

An investigation of various eddy viscosity models for accurate scale-resolving simulation of aerodynamics and aeroacoustics of a turbulent jet is presented. The models are considered from the perspective of solving the so-called gray area problem. This problem is related to nonphysical delay in the transition from Reynolds-averaged Navier–Stokes (RANS) to large-eddy simulation (LES) in hybrid RANS–LES approaches such as detached eddy simulation (DES) when applied to shear-layer flows. The performance of recently developed dynamic adapting subgrid length scales (, , and ) and subgrid LES models ( and S3QR) separately and together is demonstrated. The object of the investigation is an immersed subsonic turbulent jet. The simulations are carried out on a set of refining meshes using two different scale-resolving numerical algorithms with basic and higher-accuracy schemes on unstructured meshes. The evaluation of various eddy-viscosity models within the DES approach is mostly focused on the analysis of far-field noise. The study has clearly demonstrated the importance of both a numerical scheme and a subgrid turbulence model. The results show that some of the considered techniques provide the necessary accuracy to predict the noise generated by a turbulent jet. The features of the considered approaches are identified and discussed. Recommendations are formulated for the choice of the LES model and subgrid length scale for this kind of problem.