Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/31979
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Burke, M | - |
dc.contributor.author | Gatto, A | - |
dc.coverage.spatial | Rome, Italy | - |
dc.date.accessioned | 2025-09-12T14:57:49Z | - |
dc.date.available | 2025-09-12T14:57:49Z | - |
dc.date.issued | 2025-06-30 | - |
dc.identifier | ORCiD: Alvin Gatto https://orcid.org/0000-0003-4443-0451 | - |
dc.identifier.citation | Burke, M. and . (2025) 'Development of an AI-enhanced conceptual aircraft design synergy for the rapid prediction of future drone concepts', UAVFUT Symposium: UAV Future Applications/Services and Specific Technologies, 11th European Conference for AeroSpace Sciences (EUCASS), Rome, Italy, 30 June-4 July, pp. 1 - 12. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/31979 | - |
dc.description.abstract | The use of Unmanned Aerial Vehicles(UAVs) has expanded rapidly over the last decade. These systems have an almost limitless scope of application with resupply, surveillance, monitoring, and logistics representing but a few. Having such a wide scope, a means to rapidly, efficiently and accurately develop new designs fit-forpurpose would offer a significant advantage to developers given their inherent need to maximize potential within a competitive marketplace. This work attempts to leverage the capabilities of Artificial Intelligence(AI) for this purpose through the development of a functional AI model aimed primarily at enhancing initial conceptual design metric prediction using limited inputs and/or datasets. Overall, this synergy shows the potential to improve this process significantly through facilitating faster, more cost-effective design cycle iterations allowing ultimately more effective and efficient decision making. | en_US |
dc.description.sponsorship | The work was financially supported under project “DATA3: Drone Design using AI for Transport Applications 3(Grant No 10126519)” as part of the UKRI Innovate UK Feasibility studies for AI solutions: Series 2 competition. | en_US |
dc.format.extent | 1 - 12 | - |
dc.format.medium | Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | EUCASS | en_US |
dc.relation.uri | https://eucass2025.eu/keydates/ | - |
dc.source | UAVFUT Symposium: UAV Future Applications/Services and Specific Technologies, 11th European Conference for AeroSpace Sciences (EUCASS) | - |
dc.source | UAVFUT Symposium: UAV Future Applications/Services and Specific Technologies, 11th European Conference for AeroSpace Sciences (EUCASS) | - |
dc.title | Development of an AI-enhanced conceptual aircraft design synergy for the rapid prediction of future drone concepts | en_US |
dc.type | Conference Paper | en_US |
pubs.finish-date | 2025-07-04 | - |
pubs.finish-date | 2025-07-04 | - |
pubs.publication-status | Published | - |
pubs.start-date | 2025-06-30 | - |
pubs.start-date | 2025-06-30 | - |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | 758.34 kB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.