Turbulence simulation

The research on the flow regimes in packed tubes suggests that laminar flow CFD simulations should be reasonable for Re <100 approximately, and turbulent simulations for Re >600, also approximately. Just as RANS models provide steady solutions that are regarded as time averages of the real time-dependent turbulent flow, it may be suggested that CFD simulations in the unsteady laminar inertial range 100 time-averaged picture of the flow field. As with wall functions, comparisons with experimental data and an improved assessment of what information is really needed from the simulations will inform us as to how to proceed in these areas. 

Echekki, T., A. R. Kerstein, T. D. Dreeben, and J.-Y. Chen (2001). One-dimensional turbulence simulation of turbulent jet diffusion flames Model formulation and illustrative applications. Combustion and Flame 125, 1083-1105. 

Direct numerical simulation, as the name implies, attempts to simulate all the dynamically important scales of turbulent flows, directly. It is based on the hypothesis that direct simulations may be carried out by artificially decreasing the Reynolds number to the point where important scales can be simulated accurately on existing computers. This is probably the most exact approach to turbulence simulation without requiring any additional modeling beyond accepting the Navier-Stokes equations to describe the turbulent flow processes. The result is equivalent to a single realization of a flow or a short duration laboratory experiment. It is also the simplest approach conceptually. In DNS, all the motions contained in the flow are resolved. 

Currently there exist computers with sufficient storage capacity and speed to allow computation of these time-dependent motions for rather simple flows with finite difference meshes sufficiently fine to resolve the larger eddies of the motion. Even with such computations, however, it is necessary to model the effects of the eddies that are too small to be resolved. It is believed that since the transport of properties is governed by the larger eddies, the modeling process is less critical in these computations than where the entire turbulence is modeled. These turbulence simulations are still too costly for routine engineering computations and are used primarily to study the physics of particular turbulent flows. In fact, the results provide much more information than an engineer may ever want or need. 

This parameter / is quite important. Such factors as boundary-layer transition, separation, and heat- and mass-transfer coefficients depend upon the intensity of turbulence. Simulation of turbulent flows in testing of models requires that the Reynolds number and the intensity of turbulence be the same. One method used to measure intensity of turbulence is to utilize a hot-wire anemometer. 

Abstract. A tutorial is provided of quantum computing (QC) and the way it has made significant speed-up in various simulations. A review will also be provided of the large eddy simulation (LES) of tmrbulent flows via the stochastic filtered density function (PDF) methodology. The potentials of the quantum speed-up in FDF simulation via QC appear to be significant. This can results to a revolutionary means by which turbulence simulations can be conducted in future. 

The first term on the right-hand side of eq. (5-19) represents heat transfer due to conduction, or the diffusion of heat, where the effective conductivity, keff, contains a correction for turbulent simulations. The second term represents heat transfer due to the diffusion of species, where Jj, i is the diffusion flux defined in Section 5-2.1.4. The third term involves the stress tensor, (tij)eff, a collection of velocity gradients, and represents heat loss through viscous dissipation. The... 

Velocity vectors on a plane during a turbulent simulation modeled using large eddy simulation... 

On the right-hand side of the constitutive equation, Eq. (1.3), a diffusion term has been added, as proposed by Sureshkumar and Beris [81], so that in turbulent simulations the high wavenumber contributions of the conformation tensor do not diverge during the numerical integration of this equation in time. This parallels the introduction of a numerical diffusion term in any scalar advection equation (e.g., a concentration equation with negligible molecular diffusion) that is solved along with the flow equations under turbulent conditions [82]. In Eq. (1.3), Dq is the dimensionless numerical diffusivity [54-56]. The issue of the numerical diffusivity is further discussed in Sections 1.3.2 and 1.4.3. 

Orszag SA, Yakhot V, Flannery WS, Boysan F, Choudhury D, Maruzewski J, Patel B (1993) Renormalization group modeling and turbulence simulations. In So RMC, Speziale CG, Launder BE (eds) Near-waU turbulent flows. Elsevier, Amsterdam, New York, pp 1031-1046... 

Derksen also shows a series of comparisons of the gas velocity fields in his simulations and in measurements performed with LDA (see Sect. 10.1) by Hoekstra (2000). Some are redrawn in Fig. 7.3.2. We note that the agreement is quite impressive, even in case of the fluctuating velocity components, adding to the credibility of the LES turbulence simulations. 

Wang Q, Squires KD, Chen M, McLaughlin JB On the role of the lift force in turbulence simulations ofparticle deposition, IntJ Multiphase Flow 23 749—763,1997. http //dx.doi. org/10.1016/S0301-9322(97)00014-l. 

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