Research Papers: Gas Turbines: Turbomachinery

Performance Prediction and Optimization of Low Pressure Steam Turbine Radial Diffuser at Design and Off-Design Conditions Using Streamline Curvature Method

[+] Author and Article Information
Luying Zhang

GE Power,
Rugby CV21 2NH, UK
e-mail: zly1984@sina.com

Francesco Congiu

GE Power,
Baden 5401, Switzerland
e-mail: francesco.congiu@ge.com

Xiaopeng Gan

GE Power,
Rugby CV21 2NH, UK
e-mail: xiao-peng.gan@ge.com

David Karunakara

Engineering Unit,
Infosys Limited,
Mysore 570027, India
e-mail: karunakara_david@infosys.com

1Corresponding author.

Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received June 25, 2016; final manuscript received November 22, 2016; published online February 14, 2017. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(7), 072601 (Feb 14, 2017) (8 pages) Paper No: GTP-16-1276; doi: 10.1115/1.4035527 History: Received June 25, 2016; Revised November 22, 2016

The performance of the radial diffuser of a low pressure (LP) steam turbine is important to the power output of the turbine. A reliable and robust prediction and optimization tool is desirable in industry for preliminary design and performance evaluation. This is particularly critical during the tendering phase of retrofit projects, which typically cover a wide range of original equipment manufacturer and other original equipment manufacturers designs. This work describes a fast and reliable numerical approach for the simulation of flow in the last stage and radial diffuser coupled with the exhaust hood. The numerical solver is based on a streamline curvature throughflow method and a geometry-modification treatment has been developed for off-design conditions, at which large-scale flow separation may occur in the diffuser domain causing convergence difficulty. To take into account the effect of tip leakage jet flow, a boundary layer solver is coupled with the throughflow calculation to predict flow separation on the diffuser lip. The performance of the downstream exhaust hood is modeled by a hood loss model (HLM) that accounts for various loss generations along the flow paths. Furthermore, the solver is implemented in an optimization process. Both the diffuser lip and hub profiles can be quickly optimized, together or separately, to improve the design in the early tender phase. 3D computational fluid dynamics (CFD) simulations are used to validate the solver and the optimization process. The results show that the current method predicts the diffuser/exhaust hood performance within good agreement with the CFD calculation and the optimized diffuser profile improves the diffuser recovery over the datum design. The tool provides General Electric the capability to rapidly optimize and customize retrofit diffusers for each customer considering different constraints.

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Fig. 5

Comparison on radial distribution (plane 1) between Haze and CFX

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Fig. 4

One-pitch domain of last stage and diffuser

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Fig. 3

Flow chart of the solver algorithm

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Fig. 9

Hood loss model algorithm

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Fig. 2

Rotor exit tip leakage model and Mach number distribution (plane 1)

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Fig. 11

Exhaust box for three different test cases

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Fig. 1

Solution domains, left: Haze solution domain (last stage + diffuser) and right: exhaust hood domain

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Fig. 7

Mach number contour plots from CFD overlaid by the haze calculation domain (solid line) from geometry modification

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Fig. 8

Illustration of hood loss model paths

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Fig. 10

Parameterization of the diffuser profile

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Fig. 6

Geometry modifications for diffuser lip (left) and hub (right)

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Fig. 14

The comparison of diffuser recovery (χdiff) between optimized design and datum design (case 1)

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Fig. 15

Mach number contour plots for datum design (left) and optimized design (right) (case 1)

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Fig. 16

Case 2 datum diffuser geometry (left); manually optimized geometry (right: solid line) and tool optimized geometry (right: dashed line) (green box: last control point searching range, see online version for color)

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Fig. 17

The comparison of diffuser recovery (χdiff) between tool optimized design, manual optimized design, and the datum design (case 2)

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Fig. 18

Mach number contour plots for tool optimized design (left) and manually optimized design (right) (case 2)

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Fig. 12

Comparison on performance prediction between current approach and CFD (solid line and squares: χdiff and dashed line and triangles: χcond)

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Fig. 13

Case 1 datum diffuser geometry (left and right: blue line) and optimized geometry (right: dashed line) (green box: last control point searching range, see online version for color)



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