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Research Papers

Multi-Objective Optimization of the Impingement-Film Cooling Structure of a Gas Turbine Endwall Using Conjugate Heat Transfer Simulations

[+] Author and Article Information
Zhongran Chi

School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: chizr@sjtu.edu.cn

Haiqing Liu

Shanghai Advanced Research Institute,
Chinese Academy of Sciences,
Shanghai 200240, China

Shusheng Zang

School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received September 22, 2016; final manuscript received May 27, 2017; published online August 29, 2017. Assoc. Editor: Ting Wang.

J. Thermal Sci. Eng. Appl 10(2), 021004 (Aug 29, 2017) (11 pages) Paper No: TSEA-16-1271; doi: 10.1115/1.4037131 History: Received September 22, 2016; Revised May 27, 2017

This paper discusses the approach of cooling design optimization of a high-pressure turbine (HPT) endwall with applied 3D conjugate heat transfer (CHT) computational fluid dynamics (CFD). This study involved the optimization of the spacing of impingement jet array and the exit width of shaped holes, which are different for each cooling cavity. The optimization objectives were to reduce the wall-temperature level and to increase the aerodynamic performance. The optimization methodology consisted of an in-house parametric design and CFD mesh generation tool, a CHT CFD solver, a database of CFD results, a metamodel, and an algorithm for multi-objective optimization. The CFD tool was validated against experimental data of an endwall at CHT conditions. The metamodel, which could efficiently estimate the optimization objectives of new individuals without CFD runs, was developed and coupled with nondominated sorting genetic algorithm II (NSGA II) to accelerate the optimization process. Through the optimization search, the Pareto front of the problem was found in each iteration. The accuracy of metamodel with more iterations was improved by enriching database. But optimal designs found by the last iteration are almost identical with those of the first iteration. Through analyzing extra CFD results, it was demonstrated that the design variables in the Pareto front successfully reached the optimal values. The optimal pitches of impingement arrays could be decided accommodating the local thermal load while avoiding jet lift-off of film coolant. It was also suggested that cylindrical film holes near throat should be beneficial to both aerodynamic and cooling performances.

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References

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Chi, Z. , Liu, S. , Zang, S. , and Jiao, G. , 2015, “ Conjugate Heat Transfer Optimization of the Nonuniform Impingement Cooling Structure of a HPT 2nd Stage Vane,” ASME Paper No. GT2015-42097.
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Chi, Z. , Liu, H. , and Zang, S. , 2015, “ Semi-Inverse Design Optimization Method for Film-Cooling Arrangement of High-Pressure Turbine Vanes,” J. Propul. Power, 32(3), pp. 659–673.
Li, X. , Li, M. , Ren, J. , and Jiang, H. , 2015, “ Heat Transfer Measurements of Vane Endwall With Film Cooling and Impingement Cooling,” ASME Paper No. GT2015-43044.

Figures

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Fig. 1

Endwall with impingement-film cooling structure

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Fig. 2

Arrangement of film-cooling holes

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Fig. 3

Geometry of the shaped holes

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Fig. 4

Arrangement of impingement cooling holes

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Fig. 5

Computational domain

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Fig. 6

Mesh of the computational domain generated by the in-house code

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Fig. 7

Mesh for experimental validation of numerical models generated by the in-house code

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Fig. 8

Comparison of experimental and numerical results

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Fig. 9

Optimization flowchart

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Fig. 10

Comparison of CHT CFD results and initial metamodel predictions (mi,c/mi,g and po,g,tot)

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Fig. 11

Comparison of CHT CFD result and initial metamodel prediction (distribution of Tw)

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Fig. 12

First Pareto front of the optimization problem

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Fig. 13

Variables in the Pareto front of first iteration

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Fig. 14

Geometries of four representative individuals in the Pareto front of first iteration

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Fig. 15

Wall-temperature distributions of the four representative individuals by CHT CFD

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Fig. 16

Final Pareto front of the optimization problem

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Fig. 17

Variables in the Pareto front of final iteration

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Fig. 18

Relationship between g1 and ξ2

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Fig. 19

Streamlines from the film holes at cavity #3

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