Experimental investigation of applicability limits of K-e turbulent model and Reynolds stresses transfer model in rotary-divergent flow under control via turning blades

Kabardin, I. K.; Yavorskii, N. I.; Meledin, V. G.; Pravdina, M. Kh.; Gordienko, M. R.; Ezendeeva, D. P.; Kakaulin, S. V.; Usov, E. V.; Kolotilov, V. A.; Krotov, S. V.; Bakakin, G. V.; Kabardin, A K

doi:10.1088/1742-6596/1677/1/012014

Cited by 3 publications

(1 citation statement)

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“…[ 21 ] Nevertheless, the computational cost of the RSM model is obviously larger than that of the SST k ‐ ω model because it involves solving six transport equations for stress transfer and one TKE dissipation rate equation. [ 22,23 ] The SST k‐ω model, known for its computational efficiency, is selected for turbulence closure in this study due to its proficiency in simulating rapidly strained flows and accurately capturing swirl characteristics within complex swirling flow systems. [ 24,25 ] Two critical variables in this turbulent model, including turbulence kinetic energy ( k ) and eddy dissipation rate ( ω ), can be formulated as follows [ 26 ] :

\frac{\partial}{\partial t} ((), ρk) goodbreak+ \frac{\partial k}{\partial x_{j}} ((), italicρk u_{j}) goodbreak= P_{k} goodbreak- ρ β_{*} italickω goodbreak+ \frac{\partial}{\partial x_{j}} ([], \frac{\partial}{\partial x_{j}} ((), k ((), (μ + \frac{μ_{t}}{σ_{k}}))))

\frac{\partial}{\partial t} ((), ρω) goodbreak+ \frac{\partial}{\partial x_{j}} ((), italicρω u_{j}) goodbreak= α P_{ω} goodbreak- {ρβω}^{2} goodbreak+ \frac{\partial}{\partial x_{j}} ([], \frac{\partial}{\partial x_{j}} ((), ω ((), μ goodbreak+ \frac{μ_{t}}{σ_{ω}}))) goodbreak+ 2 ρ ((), 1 goodbreak- F_{1}) σ_{ω 2} \frac{1}{ω} \frac{\partial k}{\partial x_{i}} \frac{\partial ω}{\partial x_{j}}

…”

Section: Methodsmentioning

confidence: 99%

Insights into the hydrodynamics in the annular pipe of industrial‐scale top‐blown smelting furnace

Wan,

Yang,

Tang

et al. 2024

Can J Chem Eng

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The coaxial jet consisting of swirl jet and axial jet plays a critical role in the industrial processes but is rarely investigated; therefore, it is numerically explored by means of the computational fluid dynamics (CFD) approach in the present work. The force on the swirler, axial and tangential velocity at the annular pipe outlet, and the spatial distribution of swirl number (SN) are selected to evaluate the effect of geometrical parameters on the swirling performance. The results clarify that the velocity and their azimuthal components in the transverse cross‐section can be categorized into six segments based on high/low velocity values. The coaxial jets form a weak swirl with high axial, certain tangential, and low radial velocities. x‐ and y‐vorticity vary within −200 to 200 s−1. Coaxial jet mixing decays the SN. Based on the multi‐objective matrix analysis, swirler height has the largest effect on swirl performance followed by diameter, angle, and number based on analysis. Optimal parameters are 130° vane angle, 240 mm diameter, 10 vanes, and 250 mm height of the swirler.

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