Application of Machine Learning Techniques in Calibration and Data Reduction of Multihole ProbesSource: Journal of Turbomachinery:;2024:;volume( 147 ):;issue: 007::page 71011-1DOI: 10.1115/1.4067123Publisher: 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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contributor author | Mirhashemi, Arman | |
contributor author | Juangphanich, Paht | |
contributor author | Miki, Kenji | |
date accessioned | 2025-04-21T10:28:09Z | |
date available | 2025-04-21T10:28:09Z | |
date copyright | 12/16/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 0889-504X | |
identifier other | turbo_147_7_071011.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4306260 | |
description 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Application of Machine Learning Techniques in Calibration and Data Reduction of Multihole Probes | |
type | Journal Paper | |
journal volume | 147 | |
journal issue | 7 | |
journal title | Journal of Turbomachinery | |
identifier doi | 10.1115/1.4067123 | |
journal fristpage | 71011-1 | |
journal lastpage | 71011-10 | |
page | 10 | |
tree | Journal of Turbomachinery:;2024:;volume( 147 ):;issue: 007 | |
contenttype | Fulltext |