Research Papers

Heterogeneous Optimization Strategies for Carved and Squealer-Like Turbine Blade Tips

[+] Author and Article Information
C. De Maesschalck

von Karman Institute for Fluid Dynamics,
Rhode Saint Genèse,
Brussels BE-1640, Belgium
e-mail: cis.demaesschalck@gmail.com

S. Lavagnoli

von Karman Institute for Fluid Dynamics,
Rhode Saint Genèse,
Brussels BE-1640, Belgium
e-mail: lavagnoli@vki.ac.be

G. Paniagua

von Karman Institute for Fluid Dynamics,
Rhode Saint Genèse,
Brussels BE-1640, Belgium
e-mail: gpaniagua@me.com

T. Verstraete

von Karman Institute for Fluid Dynamics,
Rhode Saint Genèse,
Brussels BE-1640, Belgium
e-mail: tom.verstraete@vki.ac.be

R. Olive

Villaroche 77550, France
e-mail: remi.olive@snecma.fr

P. Picot

Villaroche 77550, France
e-mail: philippe.picot@snecma.fr

1Corresponding author.

2Present address: Zucrow Laboratories, Purdue University, 500 Allison Road, West Lafayette, IN 47907.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received November 15, 2015; final manuscript received March 2, 2016; published online July 27, 2016. Assoc. Editor: Jim Downs.

J. Turbomach 138(12), 121011 (Jul 27, 2016) (12 pages) Paper No: TURBO-15-1260; doi: 10.1115/1.4033975 History: Received November 15, 2015; Revised March 02, 2016

Superior rotor tip geometries possess the potential to simultaneously mitigate aerodynamic losses and severe thermal loads onto the rotor overtip region. However, classical design strategies are usually constrained to a specific type of geometry, narrowing the spread of shape topologies considered during the design phase. The current paper presents two novel multi-objective optimization methodologies that enable the exploration of a broad range of distinct tip configurations for unshrouded rotor blades. The first methodology is a shape optimization process that creates a fully carved blade tip shape defined through a Bezier surface controlled by 40 parameters. Combined with a differential evolution (DE) optimization strategy, this approach is applied to a rotor blade for two tip gap sizes: 0.85% (tight) and 1.38% (design) of the blade span. The second methodology is based on a topology optimization process that targets the creation of arbitrary tip shapes comprising one or multiple rims with a fixed height. The tip section of the blade has been divided into more than 200 separate zones, where each zone can be either part of an upstanding rim or part of the cavity floor. This methodology was tested with a level-set approach in combination with a DE optimizer and coupled to an optimization routine based on genetic algorithms (GAs). The current study was carried out on a modern high-pressure turbine operating at engine-like Reynolds and high subsonic outlet Mach numbers. A fully hexahedral unstructured mesh was used to discretize the fluid domain. The aerothermal performance of each tip profile was evaluated accurately through Reynolds-averaged Navier–Stokes (RANS) simulations adopting the shear-stress transport (SST) turbulence model. Multi-objective optimizations were set for both design strategies that target higher aerodynamic rotor efficiencies and simultaneous minimization of the heat load. This paper illustrates a wide variety of profiles obtained throughout the optimization and compares the performance of the different strategies. The research shows the potential of such novel methodologies to reach new unexplored types of blade tip designs with enhanced aerothermal performances.

Copyright © 2016 by ASME
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Fig. 3

Cooled turbine tip geometry for the solver validation (a) and hexahedral mesh (b)

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Fig. 4

Aerodynamic validation of the blade loading (a) and overtip casing pressure distribution (b)

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Fig. 2

Grid sensitivity analysis: (a) effect on rotor efficiency and (b) effect on tip heat transfer

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Fig. 1

Computational domain

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Fig. 5

Validation of the heat transfer on the tip cavity floor (a) and overtip casing (b)

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Fig. 6

Illustration of the carved tip parametrization

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Fig. 7

Block discretization for the squealer-like optimization: (a) block discretization pattern and (b) graphical example of the block-up/block-down tip surface generation

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Fig. 8

Binary and level-set approach for the squealer-like optimization

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Fig. 9

Flowchart of the optimization methodology

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Fig. 11

Strategy sequence for the squealer-like optimization

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Fig. 10

Strategy sequence for the carved tip optimization

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Fig. 12

The Pareto front for the carved tip optimization at design and tight clearance

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Fig. 17

Downstream vorticity in the upper 50% of the blade span against the efficiency

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Fig. 18

Total tip heat transfer (W) versus the surface-averaged tip heat load (W/m2)

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Fig. 15

The Pareto front for the squealer-like optimizations

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Fig. 16

Blade torque versus the massflow for every evaluated profile

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Fig. 19

Tip heat transfer (W) in function of the heat load RMS value

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Fig. 20

Heat transfer to the SS and PS for the upper 25% and the full blade span

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Fig. 13

Convergence evolution of the optimization objectives for the tight clearance ((a) and (b)) and design clearance ((c) and (d))

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Fig. 14

Zoom on the carved optimization Pareto front (a) and a detailed view of four optimal shapes (b)




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