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    Application of Machine Learning Techniques in Calibration and Data Reduction of Multihole Probes

    Source: Journal of Turbomachinery:;2024:;volume( 147 ):;issue: 007::page 71011-1
    Author:
    Mirhashemi, Arman
    ,
    Juangphanich, Paht
    ,
    Miki, Kenji
    DOI: 10.1115/1.4067123
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This work presents procedures for implementing machine learning methods into existing algorithms for multihole probe calibration and data reduction. It demonstrates that using artificial neural networks (ANNs) can decrease the amount of calibration data needed to achieve a specific calibration uncertainty by over 50%, while also significantly reducing data reduction times. Instead of surface fitting methods, ANNs are employed. Initially, directional calibration coefficients related to flow angles are computed based on pressure measurements, and then these flow angles serve as input parameters for subsequent ANNs to iteratively define Mach number, static pressure, and total pressure. In an alternative approach, new calibration coefficients directly relate pressure measurements from the five-hole probe to the quantities of interest, thereby eliminating the need for iterative algorithms used in conventional surface fitting methods. This method offers several advantages: an average increase of less than 1% in calibration uncertainty for flow angles and a significant reduction in data reduction times to a few seconds on average. Additionally, the methodology is confirmed to avoid both overfitting and underfitting.
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      Application of Machine Learning Techniques in Calibration and Data Reduction of Multihole Probes

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4306260
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    contributor authorMirhashemi, Arman
    contributor authorJuangphanich, Paht
    contributor authorMiki, Kenji
    date accessioned2025-04-21T10:28:09Z
    date available2025-04-21T10:28:09Z
    date copyright12/16/2024 12:00:00 AM
    date issued2024
    identifier issn0889-504X
    identifier otherturbo_147_7_071011.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306260
    description abstractThis work presents procedures for implementing machine learning methods into existing algorithms for multihole probe calibration and data reduction. It demonstrates that using artificial neural networks (ANNs) can decrease the amount of calibration data needed to achieve a specific calibration uncertainty by over 50%, while also significantly reducing data reduction times. Instead of surface fitting methods, ANNs are employed. Initially, directional calibration coefficients related to flow angles are computed based on pressure measurements, and then these flow angles serve as input parameters for subsequent ANNs to iteratively define Mach number, static pressure, and total pressure. In an alternative approach, new calibration coefficients directly relate pressure measurements from the five-hole probe to the quantities of interest, thereby eliminating the need for iterative algorithms used in conventional surface fitting methods. This method offers several advantages: an average increase of less than 1% in calibration uncertainty for flow angles and a significant reduction in data reduction times to a few seconds on average. Additionally, the methodology is confirmed to avoid both overfitting and underfitting.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleApplication of Machine Learning Techniques in Calibration and Data Reduction of Multihole Probes
    typeJournal Paper
    journal volume147
    journal issue7
    journal titleJournal of Turbomachinery
    identifier doi10.1115/1.4067123
    journal fristpage71011-1
    journal lastpage71011-10
    page10
    treeJournal of Turbomachinery:;2024:;volume( 147 ):;issue: 007
    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