Under the guidance of the Working Party on Scientific Issues of Reactor Systems (WPRS) , the expert group performs specific tasks associated with fuel performance aspects of present and future nuclear power systems. Reactor types considered include, but are not limited to, the following:
Focus is on LWRs for modelling, all systems for inclusion of data within the International Fuel Performance Experiments (IFPE) database .
The group provides expert advice to the WPRS and the nuclear community on the development needs (data and methods, validation experiments, scenario studies) for existing and proposed fuel designs. A key activity associated with this objective is the identification and preservation of appropriate experimental data, in order to provide specific technical information regarding:
Technical information will generally be derived from a combination of direct experimental evidence and/or the results of theoretical benchmark analyses using accurate, validated modelling methods. In either case the availability of suitable experimental data is a fundamental requirement. A key objective of the group will therefore be to help identify, evaluate and preserve this type of experimental data. In this context the expert group will monitor, steer and support the continued development of the International Fuel Performance Experiments (IFPE) database. To facilitate the dissemination of technical information and knowledge through activities such as workshops, benchmark studies and training activities.
Glyn Rossiter (UK)
All NEA member countries WPRS members' working area
|Full participant||European Commission (under the NEA Statute)|
Observer (international organisation)
|International Atomic Energy Agency (by agreement)|
WPRS and associated Expert Group meetings are held approximately every twelve months.
Next meeting: 19th Meeting of WPRS, 21st - 25th of Febuary 2022.
The aim of the benchmark is to improve understanding and modelling of pellet-cladding mechanical interaction (PCMI) amongst NEA member organisations. This is achieved by comparing PCMI predictions of different fuel performance codes for a number of cases.