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Research Papers

Automatic Design Optimization of Profiled Endwalls Including Real Geometrical Effects to Minimize Turbine Secondary Flows

[+] Author and Article Information
Shahrokh Shahpar

CFD Methods, DSE,
Rolls-Royce plc.,
Derby DE24 8BJ, UK
e-mail: shahrokh.shahpar@rolls-royce.com

Stefano Caloni, Laurens de Prieëlle

CFD Methods, DSE,
Rolls-Royce plc.,
Derby DE24 8BJ, UK

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received August 29, 2016; final manuscript received November 24, 2016; published online March 7, 2017. Editor: Kenneth Hall.

J. Turbomach 139(7), 071010 (Mar 07, 2017) (11 pages) Paper No: TURBO-16-1214; doi: 10.1115/1.4035510 History: Received August 29, 2016; Revised November 24, 2016

This paper presents a novel optimization methodology based on both adjoint sensitivity analysis and trust-based dynamic response surface modeling to improve the performance of a modern turbine of a large civil aero-engine in the presence of high-fidelity geometry configurations. The system has been applied to the nonaxisymmetric hub and tip endwall optimization of a high-pressure turbine stage making use of multirow 3D simulations, parametric modeling, and rapid meshing of real geometry features such as rim seals and modeling of film cooling flows. It has been shown in previous papers that improvements gained using simplified models of the stage are lost when applying the high-fidelity geometry configuration. New results presented in this paper indicate that controlling the purge flow that exits the disk space through the rim seal at the hub of the main annulus is more significant than the reduction of secondary flows in the main passage. For a given rim sealing mass flow rate and whirl velocity, the nonaxisymmetric endwalls are optimized such that the detrimental impact of the sealing flow on the turbine performance is reduced, and hence, the stage efficiency is significantly increased. The traditional optimization approaches based on evolutionary methods or even sequential modifications for defining the endwalls shape are computationally demanding. Since turbomachinery industry continuously strive to reduce the design cycle time, in particular when high-fidelity 3D computational fluid dynamics (CFD) is used, the main body of this paper outlines the novel methods developed to produce a practical design in a very aggressively short design cycle time.

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Figures

Grahic Jump Location
Fig. 1

Schematic flowchart of the SOPHY system

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Fig. 2

Schematic of zooming of trust regions in MAM

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Fig. 3

Full model view and HP + IP turbine rows

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Fig. 4

Computational model of the HP rotor

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Fig. 10

Convergence history of the MAM optimizer

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Fig. 5

PADRAM mesh, for NGV (top left), zoom on rotor shingling (top right), TE coolant slot (bottom left), and a large nonaxisymmetric endwall mesh perturbation

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Fig. 6

Axial stations set for the rotor endwall profiling

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Fig. 7

Turbine efficiency adjoint sensitivity map—red: raise the platform, green: no sensitivity, and blue: lower the platform

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Fig. 8

Turbine reaction adjoint sensitivity map—red: raise the platform, green: no sensitivity, and blue: lower the platform

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Fig. 9

Contour of change in radius from axisymmetric: (a) adjoint designed and (b) MAM optimized

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Fig. 11

(a) Typical convergence history of residuals and (b) oscillation of efficiency

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Fig. 12

Relative total pressure and relative whirl angle at rotor exit plane. Plotted every four iterations of one period.

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Fig. 13

Rotor lift plot at 10% height. Plotted every four iterations of one period.

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Fig. 14

Contribution of various bumps to the overall loss reductions

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Fig. 15

Static pressure above the rim seal and close to the endwall (solid lines), and circumferential variation of the average static pressure over the cavity rim (dashed lines) for one pitch

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Fig. 16

Cavity rim vortex colored by relative total temperature for the (a) datum geometry (red arrow: main annulus gas path) and (b) for the MAM optimum design (blue arrow: purge coolant flow)

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Fig. 17

Contour of relative total temperature in a circumferential plane just behind the FLMP bump: (a) axisymmetric case and (b) MAM optimized

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Fig. 18

Endwall streamlines colored by static pressure ((a) and (b)). Meridional view of “core coolant” and “recirculated” flow ((c) and (d)). Limiting streamlines on the hub endwall, colored by static pressure ((e) and (f)). Left: axisymmetric endwall and right: MAM optimized PEW.

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