Large Eddy Simulation and CDNS Investigation of T106C Low-Pressure Turbine

Site Hu; Chao Zhou; Zhenhua Xia; Shiyi Chen

doi:10.1115/1.4037489

Journal of Fluids Engineering | Volume 140 | Issue 1 | research-article

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Research Papers: Flows in Complex Systems

Large Eddy Simulation and CDNS Investigation of T106C Low-Pressure Turbine

Site Hu, Chao Zhou, Zhenhua Xia and Shiyi Chen

[+] Author and Article Information

Site Hu

College of Engineering,
Peking University,
Beijing 100871, China
e-mail: husite@pku.edu.cn

Chao Zhou

State Key Laboratory for Turbulence and
Complex Systems,
College of Engineering,
BIC-EAST,
Peking University,
Beijing 100871, China;
Collaborative Innovation Center of
Advanced Aero-Engine,
Beijing 100191, China
e-mail: czhou@pku.edu.cn

Zhenhua Xia

School of Aeronautics and Astronautics,
Zhejiang University,
Hangzhou 310058, China
e-mail: xiazh1006@gmail.com

Shiyi Chen

College of Engineering,
Southern University of
Science and Technology,
Shenzhen 518055, China
e-mail: sycpku@163.com

¹Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received January 19, 2017; final manuscript received May 23, 2017; published online October 4, 2017. Assoc. Editor: Daniel Livescu.

J. Fluids Eng 140(1), 011108 (Oct 04, 2017) (12 pages) Paper No: FE-17-1044; doi: 10.1115/1.4037489 History: Received January 19, 2017; Revised May 23, 2017

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Abstract

This study investigates the aerodynamic performance of a low-pressure turbine, namely the T106C, by large eddy simulation (LES) and coarse grid direct numerical simulation (CDNS) at a Reynolds number of 100,000. Existing experimental data were used to validate the computational fluid dynamics (CFD) tool. The effects of subgrid scale (SGS) models, mesh densities, computational domains and boundary conditions on the CFD predictions are studied. On the blade suction surface, a separation zone starts at a location of about 55% along the suction surface. The prediction of flow separation on the turbine blade is always found to be difficult and is one of the focuses of this work. The ability of Smagorinsky and wall-adapting local eddy viscosity (WALE) model in predicting the flow separation is compared. WALE model produces better predictions than the Smagorinsky model. CDNS produces very similar predictions to WALE model. With a finer mesh, the difference due to SGS models becomes smaller. The size of the computational domain is also important. At blade midspan, three-dimensional (3D) features of the separated flow have an effect on the downstream flows, especially for the area near the reattachment. By further considering the effects of endwall secondary flows, a better prediction of the flow separation near the blade midspan can be achieved. The effect of the endwall secondary flow on the blade suction surface separation at the midspan is explained with the analytical method based on the Biot–Savart Law.

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Figures

Fig. 1

Global view of computational domain and the boundary conditions (one out of four points in both streamwise and pitchwise directions)

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Fig. 2

Grid spacing distribution along suction surface: (a) Δy⁺ distribution around blade suction surface and (b) Δx⁺ distribution around blade suction surface

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Fig. 3

Ma_is distribution along suction surface for different meshes and SGS models: (a) CM, (b) QM, and (c) FM

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Fig. 9

Ma_is distribution along suction surface for different Cs, CM, Smagorinsky

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Fig. 10

Difference in exit flow angles

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Fig. 11

Difference in mass-weighted kinetic energy losses

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Fig. 6

Instantaneous vortex structure based on Q criterion colored by spanwise vorticity, CM: (a) SMAG and (b) WALE

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Fig. 7

Instantaneous vortex structure based on Q criterion colored by spanwise vorticity: (a) QM-WALE and (b) FM-WALE

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Fig. 8

Contour of pressure coefficient and streamlines, CM, Smagorinsky: (a) C_s = 0.5 and (b) C_s = 0.05

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Fig. 12

Instantaneous vortex structure based on Q criterion colored by spanwise vorticity, FM, WALE: (a) EW and (b) SYM

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Fig. 13

Isentropic Mach number distribution on the midspan, FM

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Fig. 14

Contour of pressure coefficient and streamlines, FM, WALE: (a) EW and (b) SYM

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Fig. 15

Effects of endwall flows on exit flow angle and kinetic loss coefficient: (a) mean flow angles and (b) mass-weighted kinetic energy loss coefficients

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Fig. 16

The velocity and pressure distribution along the centerline of the passage, midspan, m/s and pascal

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Fig. 17

Spanwise vorticity distribution at the midspan, FM, WALE, EW

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Fig. 18

Streamwise vorticity distribution, FM, WALE, EW: (a) plane 1 and (b) plane 2

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Fig. 19

Wall normal velocity at the midspan, FM, WALE: (a) CFD results and (b) the difference of V_n between EW and SYM cases compared to the induced velocity computed by Eq. (9)

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Fig. 4

Contour of pressure coefficient and streamlines, CM: (a) SMAG and (b) WALE

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Fig. 5

Mass-averaged viscous ratio in boundary layer, CM

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Hu S, Zhou C, Xia Z, Chen S. Large Eddy Simulation and CDNS Investigation of T106C Low-Pressure Turbine. ASME. J. Fluids Eng. 2017;140(1):011108-011108-12. doi:10.1115/1.4037489.

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Large Eddy Simulation and CDNS Investigation of T106C Low-Pressure Turbine

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