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Research Papers: Techniques and Procedures

Effect of RANS Method on the Stall Onset Prediction by an Eigenvalue Approach

[+] Author and Article Information
Zhe Xie

Mem. ASME
National Key Laboratory of
Science and Technology on
Aero-Engine Aero-Thermodynamics,
School of Energy and Power Engineering,
Beihang University,
No. 37 Xueyuan Road, Haidian District,
Beijing 100191, China
e-mail: xiezhe_buaa@buaa.edu.cn

Yangwei Liu

National Key Laboratory of
Science and Technology on
Aero-Engine Aero-Thermodynamics,
School of Energy and Power Engineering,
Collaborative Innovation Center of
Advanced Aero-Engine,
Beihang University,
No. 37 Xueyuan Road, Haidian District,
Beijing 100191, China
e-mail: liuyangwei@126.com

Xiaohua Liu

School of Aeronautics and Astronautics,
Shanghai Jiao Tong University,
No. 800 Dongchuan Road,
Shanghai 200240, China
e-mail: Xiaohua-Liu@sjtu.edu.cn

Lipeng Lu

National Key Laboratory of
Science and Technology on
Aero-Engine Aero-Thermodynamics,
School of Energy and Power Engineering,
Collaborative Innovation Center of
Advanced Aero-Engine,
Beihang University,
No. 37 Xueyuan Road, Haidian District,
Beijing 100191, China
e-mail: lulp@buaa.edu.cn

Xiaofeng Sun

School of Energy and Power Engineering,
Collaborative Innovation Center of
Advanced Aero-Engine,
Beihang University,
No. 37 Xueyuan Road, Haidian District,
Beijing 100191, China
e-mail: Sunxf@buaa.edu.cn

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received April 25, 2018; final manuscript received August 25, 2018; published online October 5, 2018. Assoc. Editor: Sergio Pirozzoli.

J. Fluids Eng 141(3), 031401 (Oct 05, 2018) (12 pages) Paper No: FE-18-1290; doi: 10.1115/1.4041362 History: Received April 25, 2018; Revised August 25, 2018

The eigenvalue approach is a recently developed compressor stability model used to predict stall onset. In this model, the flow field from a Reynolds-averaged Navier–Stokes (RANS) simulation provides the basic flow. This paper presents the effect of the RANS methods (including the computational grid, the turbulence model, and the spatial discretization scheme) on the eigenvalue and investigates the most influencing flow structures to the eigenvalue. The test compressor was the transonic compressor of NASA Rotor 37. Three individual meshes with different grid densities were used to validate the grid independence, and the results indicated that RANS simulation and eigenvalue calculation obtain grid independence at the same grid density. Then, the effect of four turbulence models (including Spalart–Allmaras (SA) turbulence model, two different k–ε models with the extended wall function model (EWFKE), and the Yang–Shih model (YSKE), and k–ω shear stress transport (SST) model), and three spatial discretization schemes (the central scheme, the flux difference splitting (FDS) scheme, and the symmetric total variation diminishing (STVD)) was also studied. Further investigation showed that the SA turbulence model combined with the STVD scheme provided the best stall point prediction, with a relative error of 0.05%. Detailed exploration of the three-dimensional flow field revealed that there were two flow patterns near the blade tip necessary for precisely predicting stall onset: the flow blockage generated by the shockwave-tip leakage vortex (TLV) interaction, and the trailing edge separation and corresponding wake flow. The effect of the blockage was greater than the effect of the trailing edge flow.

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Figures

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Fig. 2

Case 2 mesh topology

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Fig. 1

Rotor 37 sketch and measurement locations

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Fig. 9

Normalized axial velocity comparison at 95% span, 92.5% mc: (a) Experimental result from [29], (b) Case 1, (c) Case 2, and (d) Case 3

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Fig. 3

Spanwise distributions of absolute flow angle at Stn 4, 92.5% mc. Experimental result from Ref. [27].

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Fig. 4

Compressor performance: total pressure ratio. Experimental result from Ref. [27].

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Fig. 5

Compressor performance: adiabatic efficiency. Experimental result from Ref. [27].

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Fig. 6

Eigenvalue: damping factor

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Fig. 7

Eigenvalue: relative speed

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Fig. 8

Circumferentially averaged flow field difference between case 1 versus case 2

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Fig. 10

Vortex identification: (a) Case 1, (b) Case 2, and (c) Case 3

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Fig. 11

Spanwise distributions of absolute flow angle at Stn 4, 92.5% mc, from different RANS methods. Experimental result from Ref. [27].

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Fig. 12

Circumferentially averaged flow field difference, 92.5% mc: (a) EWFKE–FDS vs. EWFKE–Central (Baseline), (b) EWFKE–STVD vs. Baseline, (c) YSKE–Central vs. Baseline, (d) YSKE–FDS vs. Baseline, (e) YSKE–STVD vs. Baseline, (f) SA–Central vs. Baseline, (g) SA–FDS vs. Baseline, (h) SA–STVD vs. Baseline, (i) SST–Central vs. Baseline, (j) SST–FDS vs. Baseline, and (k) SST-STVD vs. Baseline

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Fig. 13

Normalized axial velocity comparison at 95% span, 92.5% mc: (a) SA–STVD, (b) SST–FDS, and (c) SST–STVD

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Fig. 14

Vortex identification by SST-Central, SST-FDS, and SST-STVD, 92.5% mc: (a) SST–Central, (b) SST–FDS, and (c) SST–STVD

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