Machine Learning to Improve Buckling Predictions for Structural Optimization of Stiffened Structures.
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| Title: | Machine Learning to Improve Buckling Predictions for Structural Optimization of Stiffened Structures. |
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| Authors: | Engelstad, Sean P.1, Burke, Brian J.1, Kennedy, Graeme J.2 |
| Source: | AIAA Journal. Mar2026, Vol. 64 Issue 3, p1713-1728. 16p. |
| Abstract: | Buckling is a key failure mode in aircraft structural design. Full wingbox buckling analyses are expensive to include in structural optimizations, so closed-form buckling predictions at the panel level are often used. However, these closed-form solutions are limited to special cases that are analytic, such as simply supported boundary conditions, thin-walled panels, and high aspect ratios for shear buckling. To improve buckling predictions while maintaining computational efficiency for structural optimization, the authors propose the use of machine learning to augment closed-form solutions. Their machine learning models are trained on finite element datasets of stiffened panel buckling with nondimensional parameters informed by closed-form solutions. The authors identify a log transform linear asymptote property from the closed-form buckling solutions. This property is included in the Gaussian process (GP) models to improve model extrapolation for low-aspect-ratio and highly stiffened designs. The custom GP model achieves a 99% R² value for extrapolated data as compared to the closed form and provides accurate buckling predictions on a finite element dataset of mixed simply supported to clamped panels. With the mixed boundary condition dataset, potential weight savings are demonstrated of 3.12 and 11.7% on a subsonic wingbox and a supersonic wingbox, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of AIAA Journal is the property of American Institute of Aeronautics & Astronautics 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: 192368277 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine Learning to Improve Buckling Predictions for Structural Optimization of Stiffened Structures. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Engelstad%2C+Sean+P%2E%22">Engelstad, Sean P.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Burke%2C+Brian+J%2E%22">Burke, Brian J.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kennedy%2C+Graeme+J%2E%22">Kennedy, Graeme J.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22AIAA+Journal%22">AIAA Journal</searchLink>. Mar2026, Vol. 64 Issue 3, p1713-1728. 16p. – Name: Abstract Label: Abstract Group: Ab Data: Buckling is a key failure mode in aircraft structural design. Full wingbox buckling analyses are expensive to include in structural optimizations, so closed-form buckling predictions at the panel level are often used. However, these closed-form solutions are limited to special cases that are analytic, such as simply supported boundary conditions, thin-walled panels, and high aspect ratios for shear buckling. To improve buckling predictions while maintaining computational efficiency for structural optimization, the authors propose the use of machine learning to augment closed-form solutions. Their machine learning models are trained on finite element datasets of stiffened panel buckling with nondimensional parameters informed by closed-form solutions. The authors identify a log transform linear asymptote property from the closed-form buckling solutions. This property is included in the Gaussian process (GP) models to improve model extrapolation for low-aspect-ratio and highly stiffened designs. The custom GP model achieves a 99% R² value for extrapolated data as compared to the closed form and provides accurate buckling predictions on a finite element dataset of mixed simply supported to clamped panels. With the mixed boundary condition dataset, potential weight savings are demonstrated of 3.12 and 11.7% on a subsonic wingbox and a supersonic wingbox, respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of AIAA Journal is the property of American Institute of Aeronautics & Astronautics 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=192368277 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2514/1.J065925 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1713 Titles: – TitleFull: Machine Learning to Improve Buckling Predictions for Structural Optimization of Stiffened Structures. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Engelstad, Sean P. – PersonEntity: Name: NameFull: Burke, Brian J. – PersonEntity: Name: NameFull: Kennedy, Graeme J. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00011452 Numbering: – Type: volume Value: 64 – Type: issue Value: 3 Titles: – TitleFull: AIAA Journal Type: main |
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