Research Papers: Gas Turbines: Turbomachinery

The Development and Mechanisms of the High Pressure Turbine Vane Wake Vortex

[+] Author and Article Information
Dun Lin

Key Laboratory for Thermal Science and
Power Engineering of Ministry of Education,
Tsinghua University,
Beijing 100084, China
e-mail: lindun91@gmail.com

Xinrong Su

Key Laboratory for Thermal Science and
Power Engineering of Ministry of Education,
Tsinghua University,
Beijing 100084, China
e-mail: suxr@mail.tsinghua.edu.cn

Xin Yuan

Key Laboratory for Thermal Science and
Power Engineering of Ministry of Education,
Tsinghua University,
Beijing 100084, China
e-mail: yuanxin@mail.tsinghua.edu.cn

1Corresponding author.

Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received April 25, 2017; final manuscript received February 26, 2018; published online May 24, 2018. Editor: David Wisler.

J. Eng. Gas Turbines Power 140(9), 092601 (May 24, 2018) (13 pages) Paper No: GTP-17-1150; doi: 10.1115/1.4039802 History: Received April 25, 2017; Revised February 26, 2018

The wake vortex is an important origin of unsteadiness and losses in turbines. In this paper, the development and underlying mechanisms of the shedding vortex of a high-pressure transonic turbine vane are studied and analyzed using the delayed detached eddy simulation (DDES) and proper orthogonal decomposition (POD). The goal is to understand the unsteadiness related to the wake vortex shedding and the wake evolution and mixing. Special attention is paid to the development of the wake vortex and the mechanisms behind the length characteristics. Interactions of the wake vortex with the shock wave and pressure waves are also discussed. First, the DDES simulation results are compared with published experimental data, Reynolds Averaged Navier-Stokes, and large eddy simulation (LES) simulations. Then, the development of the vane wake vortex, especially the different length characteristics from the cylinder vortex, is discussed. The reason of stronger pressure-side vortex shedding compared to suction-side vortex shedding is revealed. Wake-shock wave interaction and wake-pressure wave interaction are also investigated. The pressure waves are found to have a stronger effect than the shock wave on the spanwise motion and the dissipation of the wake vortex. An analysis of the losses through the turbine vane passage is carried out to evaluate the contributions of thermal and viscous irreversibilities. Losses analysis also confirms the strong interaction between the wake vortex and pressure waves. After that, POD study of the wake behavior was carried out. The results indicate that the shedding vortex is dominant in the unsteady flow. The phase relation between the pressure side wake vortex (PSVP) and the suction side wake vortex (SSVP) is confirmed.

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Fig. 1

Computational domain for LS89 and mesh details at the leading and trailing edges: (a) computational domains and (b) mesh details (every 2nd grid is shown)

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Fig. 10

Pressure and pressure gradient contours near TE (with pressure gradient vectors and iso-surfaces of Q–criterion colored by pressure gradient): (a) pressure and (b) pressure gradient

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Fig. 9

The development of the wake vortex (Pressure contours with Q-criterion iso-surfaces): (a) t1 = 0.48 Tvortex, (b) t2 = 0.60 Tvortex, (c) t3 = 0.74 Tvortex, (d) t4 = 0.88 Tvortex, (e) t5 = 1.02 Tvortex, and (f) t6 = 1.26 Tvortex

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Fig. 8

Spatial-temporal evolutions of two wake VPs: (a) Definition of downstream distance L/DTE and (b) positions of VPs in 2.5Tvortex

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Fig. 14

Instantaneous mass-averaged entropy generation rate in the wake area

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Fig. 7

Positions of a wake VP, VP1, of different time

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Fig. 6

Experimental Schlieren and smoke visualization of the wake vortex: (a) Schlieren visualization of a rotor blade, VKI LS59 [34] and (b) Smoke visualization of a VKI cascade [15]

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Fig. 5

Ma contours and Q–criterion iso-surfaces colored by Ma for Case MUR235: (a) 2D view and (b) 3D view

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Fig. 4

The flow field of Case MUR235 predicted by (U)RANS, DDES and LES [24]: (a) steady RANS result, (b) instantaneous result of URANS, (c) time-averaged result of DDES, (d) instantaneous result of DDES, (e) instantaneous result of unstructured LES, and (f) instantaneous result of structured LES

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Fig. 3

Isentropic Mach number distributions over the blade surface of Case MUR129

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Fig. 2

Turbulent velocity power spectral densities

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Fig. 13

Local losses contours of Case MUR235: (a) Viscous losses, S˙visc and (b) Thermal losses, S˙therm

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Fig. 12

Vorticity contour of Case MUR235

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Fig. 11

Iso-surfaces of the Q–criterion (contoured by Mach number) left: MUR235 with a shock wave right: MUR129 without a shock wave: (a) 2D View and (b) 3D view

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Fig. 15

Dimensionless axial velocity u contours and streamlines: (a) time-averaged DDES and (b) mean flow reconstructed from POD, a1n¯Φ1/Vinl

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Fig. 16

Relative and cumulative turbulent energy content of the first 50 POD modes (Mode 1 not included): (a) relative turbulent energy and (b) cumulative turbulent energy

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Fig. 17

Flow patterns of four important POD modes (axial velocity u contours and velocity vectors): (a) mode 2, Φ2, (b) mode 3, Φ3, (c) mode 4, Φ4, and (d) mode 5, Φ5

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Fig. 18

Coefficients of POD modes (a-n and a-t curves)



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