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Research Papers

Validation of a Numerical Model for Predicting Stalled Flows in a Low-Speed Fan—Part I: Modification of Spalart–Allmaras Turbulence Model

[+] Author and Article Information
Kuen-Bae Lee

Mechanical Engineering Department,
Imperial College London,
London SW7 2AZ, UK
e-mail: klee2@ic.ac.uk

Mark Wilson

Rolls-Royce plc,
Derby DE24 8BJ, UK
e-mail: mark.wilson@rolls-royce.com

Mehdi Vahdati

Mechanical Engineering Department,
Imperial College London,
London SW7 2AZ, UK
e-mail: m.vahdati@imperial.ac.uk

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received August 14, 2017; final manuscript received December 13, 2017; published online April 16, 2018. Assoc. Editor: Rakesh Srivastava.

J. Turbomach 140(5), 051008 (Apr 16, 2018) (11 pages) Paper No: TURBO-17-1114; doi: 10.1115/1.4039051 History: Received August 14, 2017; Revised December 13, 2017

The original Spalart–Allmaras (SA) model is known to predict premature stall when applied to fan or compressor, which is in line with the observation of other researchers who use the SA model. Therefore, to improve the prediction of the stall boundary, the original SA model was modified by scaling the source term based on the local pressure gradient and the velocity helicity of the flow. Furthermore, a generalized wall function valid for nonzero wall pressure gradient was implemented to improve the accuracy of boundary conditions at the solid wall. This work aims to produce a turbulence model which can be used to model flows near the stall boundary for the transonic fan rotors on relatively coarse grids of around 600k points per passage. Initially, two fan rotors with different design and operating speeds were used to optimize the new parameters in the modified turbulence model. The optimization was based on improving the correlation between measured and numerical radial profiles of the pressure ratio. Thereafter, steady computations were performed for two other fans (by using the same parameters), and the predictions were compared with the experimental data for all the four fan rotors. Numerical results showed a significant improvement over those obtained with the original SA model, when compared against the measured data. Finally, for completeness, it was decided to test the performance of the modified model by comparing the result with measured data for a simple canonical case.

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References

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Figures

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Fig. 1

Domain used for the steady computations

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Fig. 2

β function (see Eq. (5))

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Fig. 3

Components of β for fan A: (a) Mach contour with streamlines, (b, c) each component of β and (d) superposition of two components of β

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Fig. 4

general behavior of β for fan A: (a) Mach contour and streamlines and (b) β contour

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Fig. 5

Grid indenpendence study: (a) design working line and (b) stall boundary

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Fig. 6

Fan a characteristic map at 80% speed (a) and distribution of stagnation pressure at normalized mass flows of (b) 1.07 and (c) 0.95

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Fig. 7

Mach contours on the suction surface of fan A at mass flow of 1.02: (a) OSA, (b) OSA + GWF, (c) MSA and (d) MSA + GWF

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Fig. 8

Fan A characteristic map at 100% speed (a) and distribution of stagnation pressure at normalized mass flows of (b) 1.31 and (c) 1.17

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Fig. 9

Fan B characteristic map at 80% speed (a) and distribution of stagnation pressure at normalized mass flows of (b) 1.00 and (c) 0.85

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Fig. 10

Mach contours on the suction surface of fan B at mass flow of 1.00: (a) OSA and (b) MSA + GWF

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Fig. 11

Fan C characteristic map at 70% speed (a) and distribution of stagnation pressure at normalized mass flows of (b) 0.9 and (c) 0.81

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Fig. 12

NASA rotor 67 characteristic map at (a) 70% and (b) 100% speeds

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Fig. 13

Distribution of stagnation pressure and temperature at 100% speed for mass flow of (a) 0.99 and (b) 0.93

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Fig. 14

Geometry and flow features

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Fig. 15

Pressure distribution

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Fig. 16

Profiles of (a) streamwise velocity and (b) reynolds stress

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