Advanced UAV design optimization through deep learning-based surrogate models

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dc.contributor.authorKarali, Hasan
dc.contributor.authorInalhan, Gokhan
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2024-08-29T12:59:35Z
dc.date.available2024-08-29T12:59:35Z
dc.date.freetoread2024-08-29
dc.date.issued2024-08-14
dc.date.pubOnline2024-08-14
dc.description.abstractThe conceptual design of unmanned aerial vehicles (UAVs) presents significant multidisciplinary challenges requiring the optimization of aerodynamic and structural performance, stealth, and propulsion efficiency. This work addresses these challenges by integrating deep neural networks with a multiobjective genetic algorithm to optimize UAV configurations. The proposed framework enables a comprehensive evaluation of design alternatives by estimating key performance metrics required for different operational requirements. The design process resulted in a significant improvement in computational time over traditional methods by more than three orders of magnitude. The findings illustrate the framework’s capability to optimize UAV designs for a variety of mission scenarios, including specialized tasks such as intelligence, surveillance, and reconnaissance (ISR), combat air patrol (CAP), and Suppression of Enemy Air Defenses (SEAD). This flexibility and adaptability was demonstrated through a case study, showcasing the method’s effectiveness in tailoring UAV configurations to meet specific operational requirements while balancing trade-offs between aerodynamic efficiency, stealth, and structural weight. Additionally, these results underscore the transformative impact of integrating AI into the early stages of the design process, facilitating rapid prototyping and innovation in aerospace engineering. Consequently, the current work demonstrates the potential of AI-driven optimization to revolutionize UAV design by providing a robust and effective tool for solving complex engineering problems.
dc.description.journalNameAerospace
dc.description.sponsorshipEngineering and Physical Sciences Research Council and BAE Systems
dc.identifier.citationKarali H, Inalhan G, Tsourdos A. (2024) Advanced UAV design optimization through deep learning-based surrogate models. Aerospace, Volume 11, Issue 8, August 2024, Article number 669
dc.identifier.eissn2226-4310
dc.identifier.elementsID551729
dc.identifier.issn2226-4310
dc.identifier.issueNo8
dc.identifier.paperNo669
dc.identifier.urihttps://doi.org/10.3390/aerospace11080669
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22842
dc.identifier.volumeNo11
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/2226-4310/11/8/669
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject40 Engineering
dc.subject4001 Aerospace Engineering
dc.subjectMachine Learning and Artificial Intelligence
dc.subject7 Affordable and Clean Energy
dc.subject4001 Aerospace engineering
dc.titleAdvanced UAV design optimization through deep learning-based surrogate models
dc.typeArticle
dcterms.dateAccepted2024-08-12

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