Dragonfly Rotor Optimization Using Machine Learning Applied to an OVERFLOW-Generated Airfoil Database.
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| Title: | Dragonfly Rotor Optimization Using Machine Learning Applied to an OVERFLOW-Generated Airfoil Database. |
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| Authors: | Cornelius, Jason K.1 jason@perseusdefense.com, Schmitz, Sven2 |
| Source: | Journal of the American Helicopter Society. Apr2026, Vol. 71 Issue 2, p1-14. 14p. |
| Subjects: | Rotor dynamics, Surrogate-based optimization, Machine learning, United States. National Aeronautics & Space Administration, Rotorcraft |
| Abstract: | NASA's fourth New Frontiers Mission is the Titan Dragonfly relocatable lander. This coaxial quadrotor vehicle is scheduled to launch on a rocket in 2028 with the goal of exploring Titan's prebiotic chemistry and habitability. The multirotor design for this unique application has evolved to meet constraints such as Titan's cryogenic atmosphere at 95 K (-288°F), gravity 14% that of Earth's, atmospheric density 4.4 times of Earth's standard sea level, and the inability to test the entire system under these conditions until the first flight on Titan. This paper focuses on rotor design aspects of the Dragonfly lander and introduces a framework for multirotor design optimization considering multiple flight conditions. A new OVERFLOW Machine Learning Airfoil Performance (PALMO) database is first presented. PALMO is then incorporated into a Bayesian optimization framework and applied to a four-rotor system (one side of the Dragonfly lander). Training data are generated on each iteration of the optimization using the CAMRAD-II comprehensive analysis to evaluate rotor designs over the planned mission profile. An optimal design for the four-rotor system was found with approximately 900 rotor designs analyzed in CAMRAD-II, which required 9 million queries of the PALMO surrogate models. This demonstration case evaluated 10,000,000 candidate rotor designs in 5.5 h on 114 CPU cores using uniform inflow, and in 27.8 h using the prescribed wake model. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the American Helicopter Society is the property of American Helicopter Society dba Vertical Flight 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193895966 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dragonfly Rotor Optimization Using Machine Learning Applied to an OVERFLOW-Generated Airfoil Database. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cornelius%2C+Jason+K%2E%22">Cornelius, Jason K.</searchLink><relatesTo>1</relatesTo><i> jason@perseusdefense.com</i><br /><searchLink fieldCode="AR" term="%22Schmitz%2C+Sven%22">Schmitz, Sven</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Helicopter+Society%22">Journal of the American Helicopter Society</searchLink>. Apr2026, Vol. 71 Issue 2, p1-14. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Rotor+dynamics%22">Rotor dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Surrogate-based+optimization%22">Surrogate-based optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22United+States%2E+National+Aeronautics+%26+Space+Administration%22">United States. National Aeronautics & Space Administration</searchLink><br /><searchLink fieldCode="DE" term="%22Rotorcraft%22">Rotorcraft</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: NASA's fourth New Frontiers Mission is the Titan Dragonfly relocatable lander. This coaxial quadrotor vehicle is scheduled to launch on a rocket in 2028 with the goal of exploring Titan's prebiotic chemistry and habitability. The multirotor design for this unique application has evolved to meet constraints such as Titan's cryogenic atmosphere at 95 K (-288°F), gravity 14% that of Earth's, atmospheric density 4.4 times of Earth's standard sea level, and the inability to test the entire system under these conditions until the first flight on Titan. This paper focuses on rotor design aspects of the Dragonfly lander and introduces a framework for multirotor design optimization considering multiple flight conditions. A new OVERFLOW Machine Learning Airfoil Performance (PALMO) database is first presented. PALMO is then incorporated into a Bayesian optimization framework and applied to a four-rotor system (one side of the Dragonfly lander). Training data are generated on each iteration of the optimization using the CAMRAD-II comprehensive analysis to evaluate rotor designs over the planned mission profile. An optimal design for the four-rotor system was found with approximately 900 rotor designs analyzed in CAMRAD-II, which required 9 million queries of the PALMO surrogate models. This demonstration case evaluated 10,000,000 candidate rotor designs in 5.5 h on 114 CPU cores using uniform inflow, and in 27.8 h using the prescribed wake model. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of the American Helicopter Society is the property of American Helicopter Society dba Vertical Flight 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: 10.4050/JAHS.71.022001 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Rotor dynamics Type: general – SubjectFull: Surrogate-based optimization Type: general – SubjectFull: Machine learning Type: general – SubjectFull: United States. National Aeronautics & Space Administration Type: general – SubjectFull: Rotorcraft Type: general Titles: – TitleFull: Dragonfly Rotor Optimization Using Machine Learning Applied to an OVERFLOW-Generated Airfoil Database. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cornelius, Jason K. – PersonEntity: Name: NameFull: Schmitz, Sven IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00028711 Numbering: – Type: volume Value: 71 – Type: issue Value: 2 Titles: – TitleFull: Journal of the American Helicopter Society Type: main |
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