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    Uncertainty Quantification and Robustness Optimization Methods for the Aero-Thermal Performance of Turbine Endwall Contour

    Source: Journal of Thermal Science and Engineering Applications:;2026:;volume( 018 ):;issue:010::page 301
    Author:
    Zhang, Kaiyuan
    ,
    Li, Zhiyu
    ,
    Zhang, Chaocai
    ,
    Li, Zhigang
    ,
    Li, Jun
    DOI: 10.1115/1.4071271
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Abstract. Endwall contouring is a widely used technique for enhancing aerodynamic performance in turbine cascades. However, the manufacturing process of contoured endwalls inherently involves geometric errors, and thus, the performance of the contoured endwall always exhibits large uncertain deviations from design values. Currently, the uncertainty quantification method and robustness design criteria of contoured endwall performance are still insufficient, and the endwall contour is usually optimized under deterministic operation conditions instead of considering its robustness under operational uncertainties. Therefore, by using the Kriging surrogate model for global performance evaluation and polynomial chaos expansion (PCE) for uncertainty quantification, an uncertainty quantification and robustness optimization framework is established in this article. It is then implemented for the robust optimization of an endwall contour, with respect to both aerodynamic and cooling performance objectives. The robustly contour design is compared with traditional deterministic single-objective optimization results, and the advantages and necessity of robustness optimization superior to deterministic optimization of the endwall contour are revealed. The results show that the robustly designed endwall contour mainly enhances the pressure side and mid-pitch endwall cooling performance, showing an average η increase of 0.04. The contour in the upstream region causes greater uncertainties in endwall cooling parameters. The region near the leading edge on the pressure side endwall has the largest cooling effectiveness uncertainty with a standard deviation of 0.1, suggesting additional cooling designs for better protection. The robustly endwall contour design achieves a 0.7% reduction in pressure loss compared to a flat endwall, while its standard deviation of pressure loss is only 23% that of the deterministic optimization design. The robustly contour design enhances the aerodynamic robustness by accelerating the separation of the passage vortex from the endwall, thereby making it less influenced by downstream endwall contouring and near-endwall fluid. The endwall contouring at z/Cax = 0.143 and 0.71 dominates the cascade aerodynamic performance uncertainty.
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      Uncertainty Quantification and Robustness Optimization Methods for the Aero-Thermal Performance of Turbine Endwall Contour

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4315407
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    contributor authorZhang, Kaiyuan
    contributor authorLi, Zhiyu
    contributor authorZhang, Chaocai
    contributor authorLi, Zhigang
    contributor authorLi, Jun
    date accessioned2026-08-23T07:39:26Z
    date available2026-08-23T07:39:26Z
    date copyright2026/10/01
    date issued2026
    identifier issn1948-5085
    identifier othertsea-25-1418.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4315407
    description abstractAbstract. Endwall contouring is a widely used technique for enhancing aerodynamic performance in turbine cascades. However, the manufacturing process of contoured endwalls inherently involves geometric errors, and thus, the performance of the contoured endwall always exhibits large uncertain deviations from design values. Currently, the uncertainty quantification method and robustness design criteria of contoured endwall performance are still insufficient, and the endwall contour is usually optimized under deterministic operation conditions instead of considering its robustness under operational uncertainties. Therefore, by using the Kriging surrogate model for global performance evaluation and polynomial chaos expansion (PCE) for uncertainty quantification, an uncertainty quantification and robustness optimization framework is established in this article. It is then implemented for the robust optimization of an endwall contour, with respect to both aerodynamic and cooling performance objectives. The robustly contour design is compared with traditional deterministic single-objective optimization results, and the advantages and necessity of robustness optimization superior to deterministic optimization of the endwall contour are revealed. The results show that the robustly designed endwall contour mainly enhances the pressure side and mid-pitch endwall cooling performance, showing an average η increase of 0.04. The contour in the upstream region causes greater uncertainties in endwall cooling parameters. The region near the leading edge on the pressure side endwall has the largest cooling effectiveness uncertainty with a standard deviation of 0.1, suggesting additional cooling designs for better protection. The robustly endwall contour design achieves a 0.7% reduction in pressure loss compared to a flat endwall, while its standard deviation of pressure loss is only 23% that of the deterministic optimization design. The robustly contour design enhances the aerodynamic robustness by accelerating the separation of the passage vortex from the endwall, thereby making it less influenced by downstream endwall contouring and near-endwall fluid. The endwall contouring at z/Cax = 0.143 and 0.71 dominates the cascade aerodynamic performance uncertainty.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleUncertainty Quantification and Robustness Optimization Methods for the Aero-Thermal Performance of Turbine Endwall Contour
    typeJournal Paper
    journal volume18
    journal issue10
    journal titleJournal of Thermal Science and Engineering Applications
    identifier doi10.1115/1.4071271
    journal fristpage301
    journal lastpage312
    page12
    treeJournal of Thermal Science and Engineering Applications:;2026:;volume( 018 ):;issue:010
    contenttypeFulltext
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