Num. Contratto:
101059682

Coordinatore: IRSN INSTITUT DE RADIOPROTECTION ET DE SURETE NUCLEAIRE (Francia)

Responsabile ENEA: MASCARI FULVIO - NUC-ENER-SIC (BO-MARTIRI MONTE SOLE)

Ruolo ENEA: PARTNER

Sito Web: https://fr.linkedin.com/company/assas-horizon-europe-project?trk=public_post_feed-actor-name

Programma Quadro: Euratom2027

Programma UE: Euratom fissione - Fissione nucleare e radioprotezione

Area Tematica: Energy - fission

Tipo Progetto: RIA - Research and Innovation Action

Descrizione:

The ASSAS project aims at developing a proof-of-concept SA (severe accident) simulator based on ASTEC (Accident Source Term Evaluation Code). The prototype basic-principle simulator will model a simplified generic Western-type pressurized light water reactor (PWR). It will have a graphical user interface to control the simulation and visualize the results. It will run in real-time and even much faster for some phases of the accident. The prototype will be able to show the main phenomena occurring during a SA, including invessel and ex-vessel phases. It is meant to train students, nuclear energy professionals and non-specialists. In addition to its direct use, the prototype will demonstrate the feasibility of developing different types of fast-running SA simulators, while keeping the accuracy of the underlying physical models. Thus, different computational solutions will be explored in parallel. Code optimisation and parallelisation will be implemented. Beside these reliable techniques, different machine-learning methods will be tested to develop fast surrogate models. This alternate path is riskier, but it could drastically enhance the performances of the code. A comprehensive review of ASTEC's structure and available algorithms will be performed to define the most relevant modelling strategies, which may include the replacement of specific calculations steps, entire modules of ASTEC or more global surrogate models. Solutions will be explored to extend the models developed for the PWR simulator to other reactor types and SA codes. The training data-base of SA sequences used for machine-learning will be made openly available. Developing an enhanced version of ASTEC and interfacing it with a commercial simulation environment will make it possible for the industry to develop engineering and full-scale simulators in the future. These can be used to design SA management guidelines, to develop new safety systems and to train operators to use them.

Attività svolta da ENEA:

ENEA è coinvolta nei seguenti work package: . WP1 dedicato alla definizione della strategia di modellazione ed al supporto degli altri WPs. . WP2 dedicato alla generazione del database necessario per il training degli algoritmi di machine learning per i modelli surrogati . WP3 dedicato all’esplorazione dell’applicabilità di diverse metodologie di machine learning e sviluppo di modelli surrogati . WP6 dedicato alla comunicazione ed alla disseminazione dei risultati del progetto, all’organizzazione delle attività di training e alle conclusioni del progetto.

Note:

Posizione:
1721

Codice Atto:
PF6AAU

Atto di approvazione ENEA:
078 /2022/FSN

Unità alla stipula:
FSN-SICNUC-SIN

Costo eleggibile del Progetto: 3.700.349,00

Contributo al Progetto: 3.008.132,00

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Costo eleggibile per ENEA: 247.500,00

Contributo ad ENEA: 160.875,00

Anno di stipula:
2022

Durata in mesi:
48

Data di inizio:
01-11-2022

Data di scadenza:
31-10-2026