Offer details

Stage Benchmark of dynamic approaches (and tools) for Probabilistic Safety Assessment (2023-91328)

Posted on 16/12/2023

EDF Main characteristics of the job offer
Contract type:
Internship
Level of education:
Master, DEA, DESS
Specializations:
Ingineering/R&D/Expertise
Country / Region:
France / Ile-de-France
Department:
Essonne (91)
City:
PALAISEAU

Description of the offer

EDF is one of the largest producers of electricity from nuclear power in the world. Safety of its nuclear fleet is a top priority of the company. In France, Probabilistic Safety Assessment (PSA) is an obligatory part of the safety demonstration for nuclear installations.

Several steps need to be accomplished within PSA methodology in order to perform the risk quantification. Transient calculations (TH or severe accident) are used within PSA to construct the accidental sequences. These sequences are later used to construct Event Trees/Fault Trees of PSA models.

The quality and the realism of the PSA largely depend on the simplification assumptions that are often performed in the process. Sometimes, more realistic analysis may be necessary for specific case. In order to perform this analysis, advanced methods (often referred as dynamic methods) have been developped for 30 years.

At EDF R&D, we have an experience of development and application of advanced methods for PSA. These methods explicitly bring temporal evolution of the system into the consideration. Therefore, these methods should provide by far larger degree of realism of the PSA.

 

Internship Goals

The goal of this internship is to benchmark several dynamic approaches (and tools) for a given case study.

After having familiarized with the PSA process, the dynamic method and the case study, the intern will be asked to tackle the problem with one approach at the time. Ideally, the benchmark will compare three pre-selected approaches. Each approach will require the intern to use a different tool.

As a result of this internship, the intern will develop modeling skills as well as a good knowledge of risk analysis methods and in particular of the research field of dynamic methods for risk analysis.

 


-          Duration : 6 months

-          Starting date : the internship should start ideally at the first semester 2024 (but dates can be arranged according to the student availability)

Desired profile

Profile :

-          Master in Engineering (Formation école d’ingénieur ou master 2)

-          Modeling experience

-          Solid knowledge of Python (or any other object oriented) programming language.

-          Good research attitude

-          Critical thinking

-          Good level of autonomy

Skills :

Critical thinking,

Good level of autonomy,

Modeling experience

Good research attitude

Solid knowledge of Python (or any other object oriented) programming language.