Artificial intelligence for the Simulation of Severe AccidentS

Informazioni relative al progetto

ASSAS

ID dell’accordo di sovvenzione: 101059682

Data della firma CE 7 Giugno 2022

Data di avvio 1 Novembre 2022

Data di completamento 30 Aprile 2027

Finanziato da

• Euratom Research and Training Programme (EURATOM)

Costo totale

€ 3 629 130,92

Contributo UE

€ 3 008 131,60

Coordinato da

AUTORITE DE SURETE NUCLEAIRE ET DE RADIOPROTECTION
France

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (6)

Communication and dissemination plan

Description of the adopted strategy about communication and dissemination activities.

1st activity report

Report required 60 days after the end of the first reporting period.

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.

Other (1)

Database infrastructure

The deliverable is the operating database infrastructure to be used to share the training data for surrogate modelling.

Pubblicazioni

Peer reviewed articles (4)

Nuclear Engineering and Design

Autori: A. Ramos, A. Carrasco, J. Fontanet, L.E. Herranz, D. Ramos, M. Díaz, J.M. Zazo, O. Cabellos, J. Moraleda
Pubblicato in: Nuclear Engineering and Design, Numero 417, 2024, ISSN 0029-5493
Editore: Elsevier BV
DOI: 10.1016/J.NUCENGDES.2023.112842

Looking ahead to severe accident research

Autori: Luis E. Herranz, Bastien Poubeau, Lionel Chailan and Fulvio Mascari
Pubblicato in: EPJ Nuclear Science and Technologies, Numero 11, 2025, ISSN 2491-9292
Editore: EDP Sciences
DOI: 10.1051/EPJN/2025025

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

Autori: Rafael J. Caro, Isabel Parrado
Pubblicato in: EPJ Nuclear Sciences & Technologies, Numero 11, 2025, ISSN 2491-9292
Editore: EDP Sciences
DOI: 10.1051/EPJN/2025014

Latent space modeling of parametric and time-dependent PDEs using neural ODEs

Autori: Alessandro Longhi, Danny Lathouwers, Zoltán Perkó
Pubblicato in: Computer Methods in Applied Mechanics and Engineering, Numero 448, 2026, ISSN 0045-7825
Editore: Elsevier BV
DOI: 10.1016/J.CMA.2025.118394

Conference proceedings (2)

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

Autori: Christophe D’Alessandro, Terttaliisa Lind, Raphaël Périllat, Gaëtan Blondet
Pubblicato in: Proceedings of the 11th European Review Meeting on Severe Accidents Research (ERMSAR2024), 2024, ISBN -1000174165
Editore: 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?

Autori: 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
Pubblicato in: Proceedings of the 11th European Review Meeting on Severe Accidents Research (ERMSAR2024), 2024, ISBN -1000174165
Editore: Königlich Technische Hochschule Stockholm (KTH)