Subgroup on Error Recovery and Experimental Coverage - AI+ML techniques
Ongoing
Schema highlighting the use of Data Assimilation techniques for informing criticality assessments

The nuclear criticality safety community has been a leader in developing methods for uncertainty characterisation in support of the safe handling of nuclear fuel. With the introduction of advanced reactors and fuel cycles concepts, the requirements for uncertainty characterisation are expected to be much more stringent due to lower anticipated safety margins with new material concepts and proposed higher enrichments. Such uncertainty characterisation involves integration of simulation results with measurements to improve predictions. This integration can be achieved using data assimilation techniques, which, however, necessitates a deeper understanding of how the latter work. The exchange of this understanding between all international stakeholders is necessary to support any possible worldwide expansion of nuclear power. 


The objective of this activity, under the auspices of the Working Party on Nuclear Criticality Safety (WPNCS) is to develop confidence in data assimilation techniques and adjusted simulations by demonstrating how different methods used by the community characterise error sources in simulations and quantify their impact for the application of interest through the comparison of methods using a toy exercise.

The activity consists in an analytical exercise (a toy case mimicking a group of experiments and two applications) with embedded error sources to help compare the performance of state-of-the-art data assimilation (DA) techniques. The cases and error sources are intentionally fictitious and designed to facilitate the analysis of the DA method themselves, and to limit the workload for the participants. The performance criteria are the ability to correctly identify and adjust for the embedded error sources and the ability to accurately quantify the experimental coverage for a given application.

The participants’ results include the adjustments proposed by the DA method of their choice, and the improved predictions. Any additional secondary results which may vary from a DA method to another are welcome to be reported by the participants. An overall analysis report detailing the participants’ results, and providing insight on the performance of the various methods, will be prepared.

 

Contact

Co-ordinator: U. Mertyurek (United States)

Monitor: W.J. Marshall (United States)

NEA Secretariat: J.-F. Martin

 

Members' working area

WPNCS SG14 working area (password protected | reminder)

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