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Research Papers

Effects of Subgrid Scale Modeling on the Deterministic and Stochastic Turbulent Energetic Distribution in Large-Eddy Simulations of a High-Pressure Turbine Stage

[+] Author and Article Information
Dimitrios Papadogiannis

CFD Team,
CERFACS,
42 Avenue Gaspard Coriolis,
Toulouse 31057, France
e-mail: dimitrios.papadogiannis@safran.fr

Florent Duchaine, Laurent Gicquel

CFD Team,
CERFACS,
42 Avenue Gaspard Coriolis,
Toulouse 31057, France

Gaofeng Wang

School of Aeronautics and Astronautics,
Zhejiang University,
Hangzhou,
Zhejiang 310027, China

Stéphane Moreau

Département de Génie Mécanique,
University of Sherbrooke,
Sherbrooke QC J1K 2R1, Canada

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received January 7, 2015; final manuscript received November 26, 2015; published online April 12, 2016. Assoc. Editor: Graham Pullan.

J. Turbomach 138(9), 091005 (Apr 12, 2016) (10 pages) Paper No: TURBO-15-1003; doi: 10.1115/1.4032844 History: Received January 07, 2015; Revised November 26, 2015

This study focuses on the engine-representative MT1 transonic high-pressure turbine. Simulated by use of wall-modeled large-eddy simulations (LES) with three different subgrid scale (SGS) closures, mean pressure profiles across the blades as well as mean radial profiles at the rotor exit are found to be in good agreement with experimental data with only local differences between models. Unsteady flow features, inherently present in LES, are however affected by SGS modeling. This is evidenced by the relative energetic content of the deterministic to stochastic turbulent contributions evaluated, thanks to the triple decomposition analysis of the simulations. Origins of such differences are found to impact the entire radial distribution of the flow and activity, with deterministic and chaotic contributions distributed differently depending on the SGS model and reference frequency used to extract the deterministic signal. Such flow responses can be attributed to the different SGS capacities to satisfy basic turbulent flow features that translate in different dissipative and turbulent diffusive contributions of the three SGS models.

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References

Figures

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Fig. 8

Total pressure field at the rotor exit (near field plane) for case 1 (a), case 2 (b), and case 3 (c)

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Fig. 9

Instantaneous views of (||∇ρ||/ρ) at midspan (a)–(c) and FFT’s of the pressure signal for the identified probe (d)–(f) for case 1 (a) and (d), case 2 (b) and (e), and case 3 (c) and (f)

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Fig. 1

Mesh view of the stator (a), rotor (b), and rotor tip (c) mesh

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Fig. 11

Radial profiles of the unsteady activity obtained at the rotor/stator interface for all the three LES and based on a triple decomposition using (a) the rotor-BPF (9.5 kHz) and (b) the stator-BPF (4.75 kHz)

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Fig. 2

Q criterion of an instantaneous LES solution within the turbine stage obtained for case 1: (a) boundary layer separation, (b) stator wake, (c) horseshoe vortex, (d) corner vortex, (e) tip vortex, and (f) rotor wake

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Fig. 3

Q criterion of an instantaneous solution across the rotor obtained for case 1: C—horseshoe vortex; D—corner vortex; E—tip vortex; E1—induced vortex; E2—tip-separation vortex; and E3—induced vortex

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Fig. 4

Isentropic Mach number across the stator at 10% (a), 50% (b), and 90% (c) span

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Fig. 5

Static pressure across the rotor blade

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Fig. 6

Radial profiles for cases 1, 2, and 3 at the rotor exit: (a) near field and (b) far field

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Fig. 7

Vorticity of the mean velocity field at the rotor exit (near field plane) for case 1 (a), case 2 (b), and case 3 (c)

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Fig. 10

PSD of the temporal axial velocity signal of a probe in stator’s wake

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Fig. 12

Radial profiles of the unsteady activity obtained at the exit of the rotor (near field plane in Fig. 6) for all the three LES and based on a triple decomposition using (a) the rotor-BPF (9.5 kHz) and (b) the stator-BPF (4.75 kHz)

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