Abstract:
The article presents a comprehensive review of state-of-the-art approaches to diagnosing and assessing the quality of data assimilation results in neutronics modeling problems. Despite the widespread use of data assimilation procedures worldwide to refine the parameters of neutronics models based on reactor experiment results there is a lack of attention given to the issues of diagnostics and quality assessment in this specific area. This stage is crucial in ensuring the reliability and accuracy of assimilation results. By adhering to relevant recommendations, it is possible to avoid obtaining non-physical solutions, minimize compensatory effects when adjusting initial data, and include contradictory experiments in the analysis. The article discusses the most popular metrics and approaches for assessing the quality of covariance data, as well as indicators of informativeness and similarity between reactor physics experiments and the target object. It also covers methods for identifying contradictory experiments and diagnosing the quality of the solution using various statistical indicators. The article highlights the areas of application for different metrics and approaches, as well as their advantages and disadvantages, providing recommendations for their use.
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