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    A Novel Multi-Fidelity Surrogate for Efficient Turbine Design Optimization

    Source: Journal of Turbomachinery:;2023:;volume( 146 ):;issue: 004::page 41011-1
    Author:
    Wang, Qineng
    ,
    Song, Liming
    ,
    Guo, Zhendong
    ,
    Li, Jun
    ,
    Feng, Zhenping
    DOI: 10.1115/1.4064228
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: To solve the turbine design optimization problems efficiently, surrogate-based optimization algorithms are frequently used. To further reduce the cost of turbine design, the multi-fidelity surrogate (MFS)-based optimization is proposed by the researchers, who resort to augmenting the small number of expensive high-fidelity (HF) samples by a large portion of low-fidelity (LF) but cheap samples in surrogate modeling and optimization process. Nonetheless, according to our observations, the MFS-based optimization sometimes can only have better convergence rate at the early stage of optimization process, but yielding worse final solution than the single-fidelity surrogate (SFS)-based optimization that uses high-fidelity samples alone. The reason behind can be explained as follows. With the increase of HF samples in the optimization process, the LF samples can cause negative effect and therefore misleading the optimization search. To address the above issue, an ensemble weighted multi-fidelity surrogate (EMFS) is proposed. Specifically, the density-based spatial clustering of applications with noise is used to detect the region where the MFS cannot build a more accurate surrogate, and a local SFS is built there. Then, an EMFS is built by combining the MFS and SFS with adaptive weights, which is used to guide the optimization process. The related algorithm is named as multi- and single-fidelity surrogate fused optimization (MSFO). Through tests on GE-E3 blade optimization and the film cooling layout design of a turbine endwall, the effectiveness of proposed MSFO is well demonstrated.
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      A Novel Multi-Fidelity Surrogate for Efficient Turbine Design Optimization

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    contributor authorWang, Qineng
    contributor authorSong, Liming
    contributor authorGuo, Zhendong
    contributor authorLi, Jun
    contributor authorFeng, Zhenping
    date accessioned2024-12-24T18:45:01Z
    date available2024-12-24T18:45:01Z
    date copyright12/21/2023 12:00:00 AM
    date issued2023
    identifier issn0889-504X
    identifier otherturbo_146_4_041011.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4302674
    description abstractTo solve the turbine design optimization problems efficiently, surrogate-based optimization algorithms are frequently used. To further reduce the cost of turbine design, the multi-fidelity surrogate (MFS)-based optimization is proposed by the researchers, who resort to augmenting the small number of expensive high-fidelity (HF) samples by a large portion of low-fidelity (LF) but cheap samples in surrogate modeling and optimization process. Nonetheless, according to our observations, the MFS-based optimization sometimes can only have better convergence rate at the early stage of optimization process, but yielding worse final solution than the single-fidelity surrogate (SFS)-based optimization that uses high-fidelity samples alone. The reason behind can be explained as follows. With the increase of HF samples in the optimization process, the LF samples can cause negative effect and therefore misleading the optimization search. To address the above issue, an ensemble weighted multi-fidelity surrogate (EMFS) is proposed. Specifically, the density-based spatial clustering of applications with noise is used to detect the region where the MFS cannot build a more accurate surrogate, and a local SFS is built there. Then, an EMFS is built by combining the MFS and SFS with adaptive weights, which is used to guide the optimization process. The related algorithm is named as multi- and single-fidelity surrogate fused optimization (MSFO). Through tests on GE-E3 blade optimization and the film cooling layout design of a turbine endwall, the effectiveness of proposed MSFO is well demonstrated.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Novel Multi-Fidelity Surrogate for Efficient Turbine Design Optimization
    typeJournal Paper
    journal volume146
    journal issue4
    journal titleJournal of Turbomachinery
    identifier doi10.1115/1.4064228
    journal fristpage41011-1
    journal lastpage41011-10
    page10
    treeJournal of Turbomachinery:;2023:;volume( 146 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian