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    Multidisciplinary Optimization of a Mixed-Flow Impeller With 140 Parameters and Its Shapley Additive Explanations

    Source: Journal of Turbomachinery:;2024:;volume( 147 ):;issue: 006::page 61008-1
    Author:
    Xi, Guang
    ,
    Zhang, Yong
    ,
    Ji, Cheng
    ,
    Zhang, Chenqing
    DOI: 10.1115/1.4067030
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In recent years, we have made several improvements to the geometric parameterization method, the surrogate model method, and the sampling method, with the goal of making the traditional surrogate model-based optimization method applicable to aerodynamic optimization of hundreds of parameters with reasonable computational cost. However, increasing the number of control parameters raises two additional issues. First, the impeller geometry becomes too complex to ensure the required mechanical performance. Second, the optimization mechanism becomes difficult to understand with too many parameters. To address the first issue, this paper builds a multidisciplinary optimization platform to achieve the optimization of a large flow coefficient mixed-flow impeller under 140 control parameters, resulting in a significant improvement in both aerodynamic and mechanical performance. To address the latter, a novel machine learning interpretation tool, Shapley additive explanations (SHAP), is introduced in this paper. Using this methodology, the contribution of all 140 parameter values in the final optimal impeller to each aspect of the performance improvement is presented in this paper, providing the first in-depth understanding of the intricate mechanisms involved in the multidisciplinary optimization of hundreds of control parameters.
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      Multidisciplinary Optimization of a Mixed-Flow Impeller With 140 Parameters and Its Shapley Additive Explanations

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4305524
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    contributor authorXi, Guang
    contributor authorZhang, Yong
    contributor authorJi, Cheng
    contributor authorZhang, Chenqing
    date accessioned2025-04-21T10:06:55Z
    date available2025-04-21T10:06:55Z
    date copyright11/22/2024 12:00:00 AM
    date issued2024
    identifier issn0889-504X
    identifier otherturbo_147_6_061008.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305524
    description abstractIn recent years, we have made several improvements to the geometric parameterization method, the surrogate model method, and the sampling method, with the goal of making the traditional surrogate model-based optimization method applicable to aerodynamic optimization of hundreds of parameters with reasonable computational cost. However, increasing the number of control parameters raises two additional issues. First, the impeller geometry becomes too complex to ensure the required mechanical performance. Second, the optimization mechanism becomes difficult to understand with too many parameters. To address the first issue, this paper builds a multidisciplinary optimization platform to achieve the optimization of a large flow coefficient mixed-flow impeller under 140 control parameters, resulting in a significant improvement in both aerodynamic and mechanical performance. To address the latter, a novel machine learning interpretation tool, Shapley additive explanations (SHAP), is introduced in this paper. Using this methodology, the contribution of all 140 parameter values in the final optimal impeller to each aspect of the performance improvement is presented in this paper, providing the first in-depth understanding of the intricate mechanisms involved in the multidisciplinary optimization of hundreds of control parameters.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleMultidisciplinary Optimization of a Mixed-Flow Impeller With 140 Parameters and Its Shapley Additive Explanations
    typeJournal Paper
    journal volume147
    journal issue6
    journal titleJournal of Turbomachinery
    identifier doi10.1115/1.4067030
    journal fristpage61008-1
    journal lastpage61008-15
    page15
    treeJournal of Turbomachinery:;2024:;volume( 147 ):;issue: 006
    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
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