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ORIGINAL RESEARCH article

Front. Nucl. Eng.

Sec. Nuclear Safety

Volume 4 - 2025 | doi: 10.3389/fnuen.2025.1675308

This article is part of the Research TopicNuclear Engineering in the Age of AI: Developments, Deployment, and Operation CapabilitiesView all articles

An Entropy-Based Debiasing Approach to Quantifying Experimental Coverage for Novel Applications of Interest in the Nuclear Community

Provisionally accepted
Arvind  SundaramArvind Sundaram1Shiming  YinShiming Yin1Ugur  MertyurekUgur Mertyurek2*Hany  Abdel-KhalikHany Abdel-Khalik1
  • 1Purdue University, West Lafayette, United States
  • 2Oak Ridge National Laboratory, Oak Ridge, United States

The final, formatted version of the article will be published soon.

This manuscript proposes a novel information-theoretic approach to the quantification of experimental relevance, i.e., coverage, to achieve optimal data assimilation results for nuclear engineering applications. Specifically, this work posits the need for a new metric, called coverage (𝑞஼) of an application's quantity of interest, i.e., eigenvalue or power peaking for an advanced reactor concept, defined herein as the theoretically maximum achievable reduction in the quantity's uncertainty given measurements from a pool of experiments in a manner that is independent of the data assimilation procedure employed. Currently, reduction in a quantity's uncertainty is strongly biased by the underlying assumptions of the assimilation procedure to account for the under-determined nature of such problems and the similarity criterion employed to identify relevant experiments. To address this challenge, this work has developed a coverage metric, 𝑞஼, based on mutual information, which establishes a new conceptual framework for assessing coverage, one that is independent of the model parameters and responses degree of variations in both the experimental and application domains, i.e., linear vs non-linear, and their prior uncertainty distributions, i.e., Gaussian vs. non-Gaussian. The 𝑞஼ is an entropic measure capable of addressing coverage for general nonlinear problems with non-Gaussian uncertainties and inclusive of the measurement uncertainties from multiple experiments. Numerical experiments from manufactured analytical problems as well as a set of benchmarks from the ICSBEP handbook are employed to demonstrate its theoretical and practical performance as compared to the 𝑐௞-based experiment selection methodology, commonly employed in the neutronic community. The manuscript then employs other well-known adaptations to existing data assimilation methodologies for nonlinear and non-Gaussian problems capable of achieving the coverage posited by 𝑞஼.

Keywords: experimental coverage, Similarity analysis, nuclear criticality analysis, uncertainty quantification, Criticality safety, Bayesian data assimilation

Received: 29 Jul 2025; Accepted: 02 Oct 2025.

Copyright: © 2025 Sundaram, Yin, Mertyurek and Abdel-Khalik. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Ugur Mertyurek, mertyureku@ornl.gov

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