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Overview

Main Objective: Developing a proof-concept for a severe accident simulator

Budget: 4 M€, including 3 M€ from the EU (Horizon Euratom programme)

Labelled in August 2021 by SNETP – The Sustainable Nuclear Energy Technology Platform
Supporting and promoting the safe, reliable and efficient operation of nuclear systems
https://snetp.eu/

14 Partners:

IRSN (coordinator), JSI (Slovenia), KIT (Germany), KTH (Sweden), Tecnatom (Spain), ENEA (Italy), TU Delft (Netherlands), CS Group (France), PHIMECA (France), Ciemat (Spain), IVS (Slovakia), Energorisk (Ukraine), BelV (Belgium), PSI (Switzerland, associated partner)

Calendar: November 2022 – October 2026

Coordinator contact: Bastien POUBEAU (bastien.poubeau@irsn.fr)


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 in-vessel 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.

Key words: Artificial Intelligence, severe accident, simulator, knowledge transfer, surrogate models, human-machine interface, digital twin, accident mitigation systems