Recent performance breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML) have led to unprecedented interest in AI/ML among nuclear engineers. However, the lack of dedicated benchmark exercises for the application of AI/ML techniques in nuclear engineering analyses limits their applicability and broader usage. For this reason, the Task Force on Artificial Intelligence and Machine Learning for Scientific Computing in Nuclear Engineering was established within the Expert Group on Reactor Systems Multi-Physics (EGMUP) to design benchmark exercises that will target important AI/ML activities and that will span various computational domains of interest from single physics up to multi-scale and multi-physics.

The first set of exercises proposed by the Task Force on Artificial Intelligence and Machine Learning for Scientific Computing in Nuclear Engineering are the Critical Heat Flux (CHF) Benchmark Exercises. These exercises focus on the prediction of CHF, a phenomenon that must be prevented to ensure the integrity of the first barrier in widely adopted nuclear power plant designs and constitutes an important design limit for the safe operation of reactors. 

This meeting serves as a kick-off meeting of the Critical Heat Flux (CHF) Benchmark Exercises.

Date: 30 October 2023, 13:00-15:00 CET.

The meeting includes a comprehensive overview of the benchmark specifications and serves to discuss the schedule and the organisation of the benchmark activity. 

Participation is open to organisations from NEA member countries.

Registration page