Transforming Systems Engineering in Nuclear Projects with Generative AI: A Path to Efficiency and Compliance

Jérémy Bourdon, Julien Rodriguez, Quentin Lesigne, Pauline Suchet, Berenger Fister, Loic Montagne, Olivier Malhomme, Lies Benmiloud-Bechet, Robert Plana

2025

Abstract

This article explores the integration of generative artificial intelligence (AI) into nuclear systems engineering to improve efficiency and compliance. The Generative Systems Engineering (GenSE) project is transforming traditional systems engineering processes by leveraging AI across the entire plant lifecycle. Key challenges addressed include the extraction and reformulation of requirements, their allocation within the Product Breakdown Structure (PBS), and integration with existing engineering tools. To meet these challenges, a specialized Large Language Model (LLM) tailored for Nuclear Engineering, named "CurieLM", has been developed through fine-tuning. A workflow has been developed, using CurieLM, to automate requirements extraction, ensure quality assurance according to INCOSE guidelines, and facilitate allocation while maintaining compliance with ISO 15288 and ISO 24641 and integrating with SysML tools. The case study on a MOX fuel fabrication plant shows significant time reductions: 88% in requirements extraction, 87% in reformulation, and 66% in allocation to PBS. These improvements are accompanied by a gain in quality, based on feedback from requirements engineers. However, human verification remains essential to interpret and validate the results. In conclusion, the article highlights the potential of AI to transform systems engineering, while highlighting the need for collaboration between humans and AI to guarantee the quality of decisions.

Paper Citation

in Harvard Style

Bourdon J., Rodriguez J., Lesigne Q., Suchet P., Fister B., Montagne L., Malhomme O., Benmiloud-Bechet L. and Plana R. (2025). Transforming Systems Engineering in Nuclear Projects with Generative AI: A Path to Efficiency and Compliance. In Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - Volume 1: MBSE-AI Integration; ISBN 978-989-758-729-0, SciTePress, pages 395-406. DOI: 10.5220/0013443400003896

in Bibtex Style

@conference{mbse-ai integration25,
author={Jérémy Bourdon and Julien Rodriguez and Quentin Lesigne and Pauline Suchet and Berenger Fister and Loic Montagne and Olivier Malhomme and Lies Benmiloud-Bechet and Robert Plana},
title={Transforming Systems Engineering in Nuclear Projects with Generative AI: A Path to Efficiency and Compliance},
booktitle={Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - Volume 1: MBSE-AI Integration},
year={2025},
pages={395-406},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013443400003896},
isbn={978-989-758-729-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - Volume 1: MBSE-AI Integration
TI - Transforming Systems Engineering in Nuclear Projects with Generative AI: A Path to Efficiency and Compliance
SN - 978-989-758-729-0
AU - Bourdon J.
AU - Rodriguez J.
AU - Lesigne Q.
AU - Suchet P.
AU - Fister B.
AU - Montagne L.
AU - Malhomme O.
AU - Benmiloud-Bechet L.
AU - Plana R.
PY - 2025
SP - 395
EP - 406
DO - 10.5220/0013443400003896
PB - SciTePress