DOAJ Open Access 2025

An entropy-based debiasing approach to quantifying experimental coverage for novel applications of interest in the nuclear community

Arvind Sundaram Shiming Yin Ugur Mertyurek Hany Abdel-Khalik

Abstrak

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 (qC) 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, qC, 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 qC 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 ck-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 qC.

Penulis (4)

A

Arvind Sundaram

S

Shiming Yin

U

Ugur Mertyurek

H

Hany Abdel-Khalik

Format Sitasi

Sundaram, A., Yin, S., Mertyurek, U., Abdel-Khalik, H. (2025). An entropy-based debiasing approach to quantifying experimental coverage for novel applications of interest in the nuclear community. https://doi.org/10.3389/fnuen.2025.1675308

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Informasi Jurnal
Tahun Terbit
2025
Sumber Database
DOAJ
DOI
10.3389/fnuen.2025.1675308
Akses
Open Access ✓