Effect of Local Turbulent Structures on the Hot Jet in Transitional Flow

Turbulent parts localized in flow direction may arise in a pipe with transitional regime of the stable laminar Poiseuille flow. A key condition for occurrence of such structures is a pipe with rather long length relative to its diameter. Our paper presents numerical modelling of the hot air jet flowing from the long pipe into the cold open volume at Re=2426. The modelling was performed in OpenFOAM software on the basis of the large eddy simulation (LES) method. The WALE (Wall-adapting local eddy-viscosity) model was used for closure of Navier-Stokes equations on subgrid scales. We demonstrated that local turbulent structures have a weak effect on the hot jet at flowing into the cold open volume.

Large-Eddy Simulation of ECN Spray A: Sensitivity Study on Modeling Assumptions

In this study, various mixing and evaporation modeling assumptions typically considered for large-eddy simulation (LES) of the well-established Engine Combustion Network (ECN) Spray A are explored. A coupling between LES and Lagrangian particle tracking (LPT) is employed to simulate liquid n-dodecane spray injection into hot inert gaseous environment, wherein Lagrangian droplets are introduced from a small cylindrical injection volume while larger length scales within the nozzle diameter are resolved. This LES/LPT approach involves various modeling assumptions concerning the unresolved near-nozzle region, droplet breakup, and LES subgrid scales (SGS) in which their impact on common spray metrics is usually left unexplored despite frequent utilization. Here, multi-parametric analysis is performed on the effects of (i) cylindrical injection volume dimensions, (ii) secondary breakup model, particularly Kelvin–Helmholtz Rayleigh–Taylor (KHRT) against a no-breakup model approach, and (iii) LES SGS models, particularly Smagorinsky and one-equation models against implicit LES. The analysis indicates the following findings: (i) global spray characteristics are sensitive to radial dimension of the cylindrical injection volume, (ii) the no-breakup model approach performs equally well, in terms of spray penetration and mixture formation, compared with KHRT, and (iii) the no-breakup model is generally insensitive to the chosen SGS model for the utilized grid resolution.

Fractal reconstruction of the subgrid scales in turbulence models in applications to cloud microphysics

<p>Modelling of small-scale turbulence in the atmosphere play a significant role in improving our understanding of cloud processes, thereby contributing to the development of better parameterization of climate models. One of the important problems is related to the transport of cloud particles, their activation and growth, which are influenced by small-scale turbulence motions. The idea presented in this work is to use fractal interpolation to reconstruct structures which are typically not resolved in the Large Eddy Simulations (LES) of clouds. Known filtered values of velocities on LES are basis of the reconstruction. The reconstructed small scales depend on the stretching parameter <em>d</em>, which is related to the fractal dimension of the signal. In many previous studies, the stretching parameter values were assumed to be constant in space and time. We modify this approach by treating the stretching parameter as a random variable with a prescribed probability density function (pdf). This function can be determined from <em>a priori</em> analysis of numerical or experimental data and within a certain range of wavenumbers it has a universal form, independent of the Reynolds number. We show, such modification leads to improvement in terms of reconstruction of two-point statistics of turbulent velocities. Preliminary results of simulations with Lagrangian particles (superdroplets) in the reconstructed field show the fractal model properly mimics the turbulent mixing processes at subgrid scales.</p>

Lagrangian stochastic microphysics at unresolved scales in turbulent cloud simulations

<p>Turbulent clouds are challenging to model and simulate due to uncertainties in microphysical processes occurring at unresolved subgrid scales (SGS). These processes include the transport of cloud particles, supersaturation fluctuations, turbulent mixing, and the resulting stochastic droplet activation and growth by condensation. In this work, we apply two different Lagrangian stochastic schemes to model SGS cloud microphysics. Collision and coalescence of droplets are not considered. Cloud droplets and unactivated cloud condensation nuclei (CCN) are described by Lagrangian particles (superdroplets). The first microphysical scheme directly models the supersaturation fluctuations experienced by each Lagrangian superdroplet as it moves with the air flow. Supersaturation fluctuations are driven by turbulent fluctuations of the droplet vertical velocity through the adiabatic cooling/warming effect. The second, more elaborate scheme uses both temperature and vapor mixing ratio as stochastic attributes attached to each superdroplet. It is based on the probability density function formalism that provides a consistent Eulerian-Lagrangian formulation of scalar transport in a turbulent flow. Both stochastic microphysical schemes are tested in a synthetic turbulent-like cloud flow that mimics a stratocumulus topped boundary layer. It is shown that SGS turbulence plays a key role in broadening the droplet-size distribution towards larger sizes. Also, the feedback on water vapor of stochastically activated droplets buffers the variations of the mean supersaturation driven the resolved transport. This extends the distance over which entrained CNN are activated inside the cloud layer and produces multimodal droplet-size distributions.</p>

A new data-driven subgrid 2d turbulence parameterization and comparison with conventional kinetic energy backscatter parameterizations in NEMO ocean model

<p>Kinetic energy backscatter (KEB) parameterizations of subgrid 2d turbulence have shown their efficiency in ocean models as they restore activity of mesoscale eddies. Modern KEBs utilize only two properties of badly resolved inverse energy cascade: KEB tendency should be larger than turbulent viscosity in spatial scale and amount of returning energy should compensate energy loss due to eddy viscosity. Typical operators for KEB tendency are Laplace operator with negative viscosity coefficient and stochastic process. Application of artificial neural networks (ANN) to approximate subgrid forces may give rise to new KEB models. The main challenge in this direction is to preprocess subgrid forces in such a way to reveal a part corresponding to returning of energy from subgrid scales. In this work, we propose to define subgrid forces as a term nudging a coarse-resolution model toward high-resolution model. This force is energy-generating and may be approximated with ANN. Conventional KEBs and ANN model are compared in Double-Gyre configuration of NEMO ocean model.</p>

Resolved and subgrid dynamics of Rayleigh–Bénard convection

In this work we present and demonstrate the reliability of a theoretical framework for the study of thermally driven turbulence. It consists of scale-by-scale budget equations for the second-order velocity and temperature structure functions and their limiting cases, represented by the turbulent kinetic energy and temperature variance budgets. This framework represents an extension of the classical Kolmogorov and Yaglom equations to inhomogeneous and anisotropic flows, and allows for a novel assessment of the turbulent processes occurring at different scales and locations in the fluid domain. Two relevant characteristic scales, $\ell _{c}^{u}$ for the velocity field and $\ell _{c}^{\unicode[STIX]{x1D703}}$ for the temperature field, are identified. These variables separate the space of scales into a quasi-homogeneous range, characterized by turbulent kinetic energy and temperature variance cascades towards dissipation, and an inhomogeneity-dominated range, where the production and the transport in physical space are important. This theoretical framework is then extended to the context of large-eddy simulation to quantify the effect of a low-pass filtering operation on both resolved and subgrid dynamics of turbulent Rayleigh–Bénard convection. It consists of single-point and scale-by-scale budget equations for the filtered velocity and temperature fields. To evaluate the effect of the filter length $\ell _{F}$ on the resolved and subgrid dynamics, the velocity and temperature fields obtained from a direct numerical simulation are split into filtered and residual components using a spectral cutoff filter. It is found that when $\ell _{F}$ is smaller than the minimum values of the cross-over scales given by $\ell _{c,min}^{\unicode[STIX]{x1D703}\ast }=\ell _{c,min}^{\unicode[STIX]{x1D703}}Nu/H=0.8$, the resolved processes correspond to the exact ones, except for a depletion of viscous and thermal dissipations, and the only role of the subgrid scales is to drain turbulent kinetic energy and temperature variance to dissipate them. On the other hand, the resolved dynamics is much poorer in the near-wall region and the effects of the subgrid scales are more complex for filter lengths of the order of $\ell _{F}\approx 3\ell _{c,min}^{\unicode[STIX]{x1D703}}$ or larger. This study suggests that classic eddy-viscosity/diffusivity models employed in large-eddy simulation may suffer from some limitations for large filter lengths, and that alternative closures should be considered to account for the inhomogeneous processes at subgrid level. Moreover, the theoretical framework based on the filtered Kolmogorov and Yaglom equations may represent a valuable tool for future assessments of the subgrid-scale models.

Subgrid Parameterizations of the Eddy–Eddy, Eddy–Mean Field, Eddy–Topographic, Mean Field–Mean Field, and Mean Field–Topographic Interactions in Atmospheric Models

Parameterizations are developed for each of the subgrid turbulence interaction classes in fully three-dimensional global atmospheric flows over topography, typical of January and July climate states. Stochastic and deterministic parameterizations are developed for the eddy–eddy interactions and deterministic parameterizations for eddy–mean field, eddy–topographic, mean field–mean field, and mean field–topographic interactions. All parameterizations are calculated from the statistics of higher-resolution reference direct numerical simulations (DNSs) truncated into resolved and subgrid scales and employed without tuning coefficients. This parameterization framework is validated by performing large-eddy simulations (LESs) that closely agree with the reference DNSs in terms of time-averaged kinetic energy spectra, zonal jet structure, and nonzonal streamfunction fields. Both the DNSs and LESs are formulated in such a way that the usual problem of a long artificial dissipation range does not occur. Successful LESs are produced with truncation wavenumbers 31 and 15, using, respectively, only 11.9% and 1.3% of the DNS computational effort at truncation wavenumber 63. The lower-resolution LESs show that the parameterizations are successful even when the energy injection due to baroclinic instability is not completely resolved. The contribution of each of the parameterized interaction classes to the quality of the LES is identified. The best agreement is achieved when all subgrid components are included. There is a very high level of agreement between the LESs and DNSs with typical streamfunction pattern correlations of r = 0.962 for the nonzonal components and r = 0.999 for the total fields when the complete sets of parameterizations are used.