Turbulent mixing in temporal compressible shear layers involving detailed diffusion processes

Mahle, Inga; Sesterhenn, Jörn; Friedrich, Rainer

doi:10.1080/14685240600806256

Cited by 47 publications

(7 citation statements)

References 18 publications

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“…17 (see later section). It is worth noticing that Re ω reaches values as large as 3 × 10 5 at the end of the simulation, which is one order of magnitude higher than similar DNS and LES computations reported in the literature [39,33,16]. Table 1 summarizes the detail of flow parameters for both LES and previous DNS data of the literature.…”

Section: Problem Setupmentioning

confidence: 74%

LES of temporally evolving mixing layers by an eighth‐order filter scheme

Hadjadj

Yee

Sjögreen

2012

Numerical Methods in Fluids

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An eighth-order filter method for a wide range of compressible flow speeds (H.C. Yee and B. Sjogreen, Proceedings of ICOSAHOM09, June 22-26, 2009, Trondheim, Norway) are employed for large eddy simulations (LES) of temporally evolving mixing layers (TML) for different convective Mach numbers (M c ) and Reynolds numbers. The high order filter method is designed for accurate and efficient simulations of shock-free compressible turbulence, turbulence with shocklets and turbulence with strong shocks with minimum tuning of scheme parameters. The value of M c considered is for the TML range from the quasi-incompressible regime to the highly compressible supersonic regime. The three main characteristics of compressible TML (the self similarity property, compressibility effects and the presence of large-scale structure with shocklets for high M c ) are considered for the LES study. The LES results using the same scheme parameters for all studied cases agree well with experimental results of , and published direct numerical simulations (DNS) work of Rogers & Moser (1994) and Pantano & Sarkar (2002). Key words: DNS, LES, Homogeneous turbulence, Mixing layer, SWBLI Motivation and ObjectiveFor the last decade there has been an increase in the use of computational fluid dynamics (CFD) in engineering science, not only for fundamental understanding of complex compressible turbulent physics, but also for the development and design of industrial devices. Owing to the recent progress in petascale computing, in tandem with advances in algorithm development for accurate direct numerical simulations (DNS) and large eddy simulations (LES) of shock free compressible turbulence and turbulence with strong shocks, this type of DNS and LES computation has gradually been able to tackle more complex flows physics. Advances in flow visualization tools have paved the way to extracting valuable information from the computed results containing hundreds of terabytes of data. Examples include * This work was performed while the first author was a visiting scholar at the Center for Turbulence Research, Stanford University.

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Section: Problem Setupmentioning

confidence: 74%

LES of temporally evolving mixing layers by an eighth‐order filter scheme

Hadjadj

Yee

Sjögreen

2012

Numerical Methods in Fluids

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“…Despite the elevated pressure inside the PSU rig, the conditions are far from being supercritical and these cross-diffusion terms are inherently small (less than 10% of the heat/ mass fluxes). While they could promote intermittency and flame extinction, resulting in a slight decrease of the flame temperature, even detailed direct numerical simulation studies of mixing layers have not reached a definitive conclusion on their importance [15,16]. The subgrid species diffusive flux sgs i;k g V i;k Y k Ṽ i;kỸk is usually neglected because it has been shown to have a small magnitude at high Reynolds numbers [17,18], such as the ones found in the main shear layers of the current injector flow.…”

Section: Formulationmentioning

confidence: 99%

Large-Eddy Simulation of Flame-Turbulence Interactions in a Shear Coaxial Injector

Masquelet

Menon

2010

Journal of Propulsion and Power

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The turbulent combustion inside the main chamber of a liquid rocket engine is difficult to numerically model because of the wide range of scales involved, the complex thermodynamics at elevated pressure, and the influence of heat transfer. Here, a single-element gaseous-hydrogen-gaseous-oxygen shear-coaxial injector is studied. This case involves complex physical processes that are simulated using a large-eddy simulation approach. In particular, modeling of the nonpremixed turbulent combustion and its impact on the wall heat flux is addressed. This large-eddy simulation of the compressible multispecies Navier-Stokes equations is solved using a hybrid central-upwind scheme with a thermally perfect formulation. To keep the computational cost reasonable, priority was given to the near-field resolution over the near-wall resolution. The modeled flame anchoring and dynamics provide a satisfactory estimate of the wall heat flux. The unsteady and three-dimensional features of the flow are discussed in length, and the implications of the unique features of this shear-coaxial GH 2 -GO 2 injector for turbulent combustion modeling are analyzed.Nomenclature C , C = model coefficients for the subgrid closure D = mass diffusivity, m 2 s 1 e T = total energy (internal kinetic) per unit mass, J kg 1 H sgs = subgrid enthalpy flux, J m 2 h k = partial massic enthalpy of species k, J kg 1 J k = mass diffusion flux, kg m 2 k sgs = turbulent kinetic energy, m 2 s 2 N S = number of species considered P sgs = production of turbulent kinetic energy, kg m 1 s 3 p = pressure, pa Q IK = heat flux in the Irving-Kirkwood form, J m 2 Q sgs k = subgrid enthalpy flux due to diffusion of species k, J m 2 R = gas constant, J kg 1 K 1 T = temperature, k t = time, s u = velocity, m s 1 V k = diffusion velocity of species k, m s 1 x i = Cartesian coordinate, m Y k = mass fraction of species k = grid filter size, m sgs = subgrid dissipation of turbulent kinetic energy, kg m 1 s 3 sgs k = subgrid species diffusive flux, kg m 2 = thermal conductivity, W m 1 K 1 = dynamic viscosity, Pa s = density, kg m 3 sgs = subgrid heat flux, J m 2 ij = stress tensor Subscripts i, j = Cartesian direction k = species k property m = mixture property Superscripts x = spatially filtered quantitỹ x = Favre-filtered quantity x sgs = subgrid quantity

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“…Numerous results have been reported, and our understanding of the processes involved in transitional flows has been improved. [1][2][3][4][5][6][7][8][9][10][11][12][13] In addition to the experimental and theoretical approaches, numerical simulation methods are becoming prevalent tools to investigate transition problems, for both spatially developing and temporally evolving problems. Because of the rapid development of computing capability over the last two decades, the direct numerical simulation (DNS) of laminar-turbulent transition has been successfully performed in canonical geometries (i.e., a boundary layer or a channel), and results related to different aspects of this problem have been reported.…”

Section: Introductionmentioning

confidence: 99%

Constrained large-eddy simulation of laminar-turbulent transition in channel flow

et al. 2014

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A constrained large-eddy simulation (CLES) of a laminar-turbulent transition in a temporally developing channel flow is performed. First, we confirm the capability of CLES to simulate this transition problem using the a priori Reynolds stress estimated from a direct numerical simulation. Based on the analysis of the Reynolds stress during the transition process, an intermittency factor is introduced in the Reynolds-averaged Navier–Stokes equation (RANS) model to account for the transition property. Two simple approaches are used to construct the intermittency factor. One is based on the shape factor, and the other is based on the coefficients of Smagorinsky models. The CLES results using the intermittency modified RANS model can accurately predict the onset of the transition and the basic transition process, in a manner similar to a large eddy simulation with dynamics Smagorinsky model (LES-DSM). Meanwhile, CLES preserves its advantage over LES-DSM in the turbulent state. The present work illustrates that CLES can be used to simulate transitional flows.

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Turbulent mixing in temporal compressible shear layers involving detailed diffusion processes

Cited by 47 publications

References 18 publications

LES of temporally evolving mixing layers by an eighth‐order filter scheme

LES of temporally evolving mixing layers by an eighth‐order filter scheme

Large-Eddy Simulation of Flame-Turbulence Interactions in a Shear Coaxial Injector

Constrained large-eddy simulation of laminar-turbulent transition in channel flow

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