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Research Papers: Gas Turbines: Manufacturing, Materials, and Metallurgy

Design for Additive Manufacturing: Internal Channel Optimization

[+] Author and Article Information
M. Pietropaoli

Department of Aeronautics,
Imperial College of London,
London SW7 2AZ, UK
e-mail: m.pietropaoli14@imperial.ac.uk

R. Ahlfeld

Department of Aeronautics,
Imperial College of London,
London SW7 2AZ, UK
e-mail: r.ahlfeld14@imperial.ac.uk

F. Montomoli

Department of Aeronautics,
Imperial College of London,
London SW7 2AZ, UK
e-mail: f.montomoli@imperial.ac.uk

A. Ciani

GE Oil & Gas Nuovo Pignone s.r.l.,
Florence 50127, Italy
e-mail: alessandro.ciani@ge.com

M. D'Ercole

GE Oil & Gas Nuovo Pignone s.r.l.,
Florence 50127, Italy
e-mail: michele.dercole@ge.com

Contributed by the Marine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 5, 2016; final manuscript received November 1, 2016; published online April 25, 2017. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(10), 102101 (Apr 25, 2017) (8 pages) Paper No: GTP-16-1304; doi: 10.1115/1.4036358 History: Received July 05, 2016; Revised November 01, 2016

The new possibilities offered by additive manufacturing (AM) can be exploited in gas turbines to produce a new generation of complex and efficient internal coolant systems. The flexibility offered by this new manufacturing method needs a paradigm shift in the design approach, and a possible solution is offered by topology optimization. The overall goal of this work is to propose an innovative method to design internal channels in gas turbines that fully exploit AM capabilities. The present work contains a new application of a fluid topology sedimentation method to optimize the internal coolant geometries with minimal pressure losses while maximizing the heat exchange. The domain is considered as a porous medium with variable porosity: the solution is represented by the final solid distribution that constitutes the optimized structure. In this work, the governing equations for an incompressible flow in a porous medium are considered together with a conjugate heat transfer equation that includes porosity-dependent thermal diffusivity. An adjoint optimization approach with steepest descent method is used to build the optimization algorithm. The simulations are carried out on three different geometries: a U-bend, a straight duct, and a rectangular box. For the U-bend, a series of splitter is automatically generated by the code, minimizing the stagnation pressure losses. In the straight duct and in the rectangular box, the impact of different choices of the weights and of the definition of the porosity-dependent thermal diffusivity is analyzed. The results show the formation of splitters and bifurcations in the box and “riblike” structures in the straight duct, which enhance the heat transfer.

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References

Figures

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Fig. 1

Computational cost expressed as a function of the number of cells in the grid

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Fig. 2

Computational domains used for topology optimization of the U-bend. On the black lines, the velocity distribution is computed and compared.

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Fig. 3

Construction of flow benders for different inflow velocities. On the left 6 m/s and on the right 10 m/s.

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Fig. 4

Optimized shape for the U-bend channel for different inflow velocities. On the left 6 m/s and on the right 10 m/s.

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Fig. 5

Construction of flow benders for different aspect ratios. On the left (1:2) and on the right (2:2).

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Fig. 6

Optimized shape for the U-bend channel for different aspect ratios. On the left (1:2) and on the right (2:2).

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Fig. 7

Vertical velocity profiles obtained on the section line in the return channel

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Fig. 8

Three-dimensional adjoint optimization

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Fig. 9

Test case geometry, rectangular box

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Fig. 10

Test case geometry, straight duct with back facing step

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Fig. 11

Schematic of the test cases

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Fig. 12

Construction of different ducts in the rectangular box depending on the weights used for pressure drop and heat transfer. From top left, ω̂2=0; top right, ω̂2=0.005; bottom left, ω̂2=0.01; and bottom right, ω̂2=0.1.

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Fig. 13

Optimal shape in the rectangular box for different choices of the weights for pressure losses and heat transfer. From top left, ω̂2=0; top right, ω̂2=0.005; bottom left, ω̂2=0.01; and bottom right, ω̂2=0.1.

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Fig. 14

Temperature distribution in the rectangular box depending on choices of the weights for pressure losses and heat transfer. From top left, ω̂2=0; top right, ω̂2=0.005; bottom left, ω̂2=0.01; and bottom right, ω̂2=0.1.

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Fig. 15

Construction of different ducts in the rectangular box depending on the model for k. On the left, k is assumed to be constant, while on the right the ratio between ksolid and kfluid is 10.

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Fig. 16

Optimal shape in the rectangular box depending on the model for k. On the left, k is assumed to be constant, while on the right the ratio between ksolid and kfluid is 10.

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Fig. 17

Construction of serpentine duct for pressure drop and heat transfer optimization

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Fig. 18

Optimized shape of the straight duct for pressure drop and heat transfer optimization

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