Grech La Rosa, Andrea;
Simpson, Peter;
Zammit, Ryan;
(2024)
Exploring the Opportunities of Generative Artificial Intelligence in Concept Ship Design.
In:
Proceedings of the 15th International Marine Design Conference (IMDC 2024).
IMDC
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Abstract
Designing vessels is a complex process requiring the consideration of numerous aspects to develop a successful design. Ship and submarine design often requires the designer to approximate, make assumptions, consider scenarios and imagine how the vessel may be used in operation. Having the ability to consult for feedback or request additional information may be a welcome aid. The onset of Generative AI (GENAI) presents a new opportunity to integrate this resource into the workflows of the concept ship design process. Augmenting the design process could have a positive impact on the outcome of the design, further improving various qualities such as performance, sustainability, equality, diversity and inclusion. Aspects associated with weight groups, payload catalogues, technical analysis and layout set out will be explored to investigate whether GENAI could add value to the design process. A case study will be used to facilitate this investigation, taking note of GENAI’s content throughout the process.
Type: | Proceedings paper |
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Title: | Exploring the Opportunities of Generative Artificial Intelligence in Concept Ship Design |
Event: | 15th International Marine Design Conference (IMDC 2024) |
Dates: | 2 Jun 2024 - 6 Jun 2024 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.59490/imdc.2024.752 |
Publisher version: | https://doi.org/10.59490/imdc.2024.752 |
Language: | English |
Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. |
Keywords: | Generative Artificial Intelligence, concept ship design, text to text, text to image, Chat GPT, Gemini, Dal E |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
URI: | https://discovery.ucl.ac.uk/id/eprint/10194541 |
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