Artificial intelligence for the Simulation of Severe AccidentS

Project Information

ASSAS

Grant agreement ID: 101059682

EC signature date 7 June 2022

Start date 1 November 2022

End date 31 October 2026

Funded under

• Euratom Research and Training Programme (EURATOM)

Total cost

€ 3 689 569,42

EU contribution

€ 3 008 131,60

Coordinated by

AUTORITE DE SURETE NUCLEAIRE ET DE RADIOPROTECTION
France

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Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Documents, reports (5)

Communication and dissemination plan

Description of the adopted strategy about communication and dissemination activities.

User expectations

More detailed description of the users' expectations concerning the functional specifications of the prototype simulator.

Minutes of the kick-off meeting

Summary of discussions held during the kick-off meeting.

Data management plan

First version of the project's data management plan

Report on modelling strategy

The report will summarize the strategy adopted by the consortium to develop surrogate and enhanced models for severe accident codes.

Publications

Peer reviewed articles (2)

Looking ahead to severe accident research

Author(s): Luis E. Herranz, Bastien Poubeau, Lionel Chailan and Fulvio Mascari
Published in: EPJ Nuclear Science and Technologies, Issue 11, 2025, ISSN 2491-9292
Publisher: EDP Sciences
DOI: 10.1051/EPJN/2025025

ASSAS project, Artificial intelligence for Simulation of Severe AccidentS; Simulator development and Isotopic Source Term proposals

Author(s): Rafael J. Caro, Isabel Parrado
Published in: EPJ Nuclear Sciences & Technologies, Issue 11, 2025, ISSN 2491-9292
Publisher: EDP Sciences
DOI: 10.1051/EPJN/2025014

Conference proceedings (2)

Preliminary strategies for training dataset generation and surrogate modelling of station blackout management measures in PWR

Author(s): Christophe D’Alessandro, Terttaliisa Lind, Raphaël Périllat, Gaëtan Blondet
Published in: Proceedings of the 11th European Review Meeting on Severe Accidents Research (ERMSAR2024), 2024, ISBN -1000174165
Publisher: Königlich Technische Hochschule Stockholm (KTH)
DOI: 10.5445/IR/1000174165

Horizon Euratom ASSAS project: can machine-learning make fast and accurate severe accident simulators a reality?

Author(s): Bastien Poubeau, Yann Richet, Lionel Chailan, Fulvio Mascari, Mattia Massone, Simone Gianfelici, Luis Enrique Herranz, Joan Fontanet, Terttaliisa Lind, Christophe D’alessandro, Jure Brence, Ivo Kljenak, Saso Dzeroski, Fabrizio Gabrielli
Published in: Proceedings of the 11th European Review Meeting on Severe Accidents Research (ERMSAR2024), 2024, ISBN -1000174165
Publisher: Königlich Technische Hochschule Stockholm (KTH)