Development of an AI-enhanced conceptual aircraft design synergy for the rapid prediction of future drone concepts

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.

Publication Details

Journal
Brunel University Research Archive (BURA) (Brunel University London)
Published
2025-06-30
Primary Topic
Geochemistry and Geologic Mapping
Type
article
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article

Development of an AI-enhanced conceptual aircraft design synergy for the rapid prediction of future drone concepts

Brunel University Research Archive (BURA) (Brunel University London)
Geochemistry and Geologic Mapping
article

Development of an AI-enhanced conceptual aircraft design synergy for the rapid prediction of future drone concepts

article en

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.

Brunel University Research Archive (BURA) (Brunel University London)
Peace, Justice and strong institutions
Openalex Percentile: Top 41%
Geochemistry and Geologic Mapping
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