Sensitivity analysis of large body of control parameters in machine learning control of a square-back Ahmed body.
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| Title: | Sensitivity analysis of large body of control parameters in machine learning control of a square-back Ahmed body. |
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| Authors: | Deng, G. M.1, Fan, D. W.1, Zhang, B. F.1, Liu, K.1, Zhou, Y.1 yuzhou@hit.edu.cn |
| Source: | Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences. Jan2023, Vol. 479 Issue 2269, p1-28. 28p. |
| Subjects: | Machine learning, Sensitivity analysis, Drag reduction, Simplex algorithm, Taylor's series |
| Abstract: | Active drag reduction (DR) of a square-back Ahmed body is experimentally studied based on machine learning or artificial intelligence (AI) control. The control system consists of four independently operated arrays of pulsed microjets, 25 pressure taps and an explorative downhill simplex method controller. Two strategies, i.e. asymmetric and symmetric actuations, are investigated, with 12 and 9 control parameters, respectively. Both achieve a DR by 13%, though with distinct flow physics and control mechanisms behind. A model linking the control parameters with the cost is developed based on Taylor expansion around the K-nearest neighbours of the smallest cost obtained from the AI control, resulting in a substantially reduced deviation between measured and predicted costs, especially when involving a large number of control parameters, compared with that based on Taylor expansion around the optimum cost. Sensitivity analysis, conducted based on the model, indicates that the control efficiency, i.e. the ratio of the power saving from DR to the total power consumption, may reach 55 and 78 for the symmetric and asymmetric strategies, respectively, given a 1--2% sacrifice on DR. This efficiency greatly exceeds that (26.5) obtained by Fan et al. (Fan et al. 2020 Phys. Fluids 32, 125117. (doi:10.1063/5.0033156)), whose independent control parameters are only three. [ABSTRACT FROM AUTHOR] |
| Copyright of Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences is the property of Royal Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 161541677 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Sensitivity analysis of large body of control parameters in machine learning control of a square-back Ahmed body. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Deng%2C+G%2E+M%2E%22">Deng, G. M.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Fan%2C+D%2E+W%2E%22">Fan, D. W.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+B%2E+F%2E%22">Zhang, B. F.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Liu%2C+K%2E%22">Liu, K.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Y%2E%22">Zhou, Y.</searchLink><relatesTo>1</relatesTo><i> yuzhou@hit.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+Royal+Society+A%3A+Mathematical%2C+Physical+%26+Engineering+Sciences%22">Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences</searchLink>. Jan2023, Vol. 479 Issue 2269, p1-28. 28p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Drag+reduction%22">Drag reduction</searchLink><br /><searchLink fieldCode="DE" term="%22Simplex+algorithm%22">Simplex algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Taylor's+series%22">Taylor's series</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Active drag reduction (DR) of a square-back Ahmed body is experimentally studied based on machine learning or artificial intelligence (AI) control. The control system consists of four independently operated arrays of pulsed microjets, 25 pressure taps and an explorative downhill simplex method controller. Two strategies, i.e. asymmetric and symmetric actuations, are investigated, with 12 and 9 control parameters, respectively. Both achieve a DR by 13%, though with distinct flow physics and control mechanisms behind. A model linking the control parameters with the cost is developed based on Taylor expansion around the K-nearest neighbours of the smallest cost obtained from the AI control, resulting in a substantially reduced deviation between measured and predicted costs, especially when involving a large number of control parameters, compared with that based on Taylor expansion around the optimum cost. Sensitivity analysis, conducted based on the model, indicates that the control efficiency, i.e. the ratio of the power saving from DR to the total power consumption, may reach 55 and 78 for the symmetric and asymmetric strategies, respectively, given a 1--2% sacrifice on DR. This efficiency greatly exceeds that (26.5) obtained by Fan et al. (Fan et al. 2020 Phys. Fluids 32, 125117. (doi:10.1063/5.0033156)), whose independent control parameters are only three. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences is the property of Royal Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1098/rspa.2022.0280 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 1 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Sensitivity analysis Type: general – SubjectFull: Drag reduction Type: general – SubjectFull: Simplex algorithm Type: general – SubjectFull: Taylor's series Type: general Titles: – TitleFull: Sensitivity analysis of large body of control parameters in machine learning control of a square-back Ahmed body. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Deng, G. M. – PersonEntity: Name: NameFull: Fan, D. W. – PersonEntity: Name: NameFull: Zhang, B. F. – PersonEntity: Name: NameFull: Liu, K. – PersonEntity: Name: NameFull: Zhou, Y. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 13645021 Numbering: – Type: volume Value: 479 – Type: issue Value: 2269 Titles: – TitleFull: Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences Type: main |
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