Nuclear archaeology: reconstructing reactor histories from reprocessing waste

Volume: 59, December 2019, pages 39-46,

Authors: Antonio Figueroa and Malte Göttsche

¹_{Aachen Institute for Advanced Study in Computational Engineering Science, RWTH Aachen}

Abstract: Nuclear archaeology is a field dedicated to the reconstruction and quantification of the past production of fissile materials. As part of related research efforts, we examined in this study the possibilities and limitations of exploiting measurements of high-level waste to deduce parameters related to the operational history of reactors such as burnup. For the first stage of this project, we used high-fidelity reactor simulations to estimate spent-fuel compositions, and developed a surrogate model which can be used as a computationally less-expensive method to map combinations of input parameters to fuel compositions. This model gives us a better understanding of the challenges involved in solving the inverse problem of deducing the reactor history from waste measurements. A promising method to solve this inverse problem may be Bayesian inference, where prior existing information (e.g. a declaration by a state) can be taken into account, and waste measurements would be used to update this knowledge. This way, measurements may confirm the existing information, make it more accurate or identify inconsistencies which may indicate intentional or unintentional non-conformity of the declaration. For a proof of concept of the methodology, we examined in this study three simple scenarios in order to determine a few reactor parameters, given a hypothetical declaration by a state and a simulated measurement of the waste isotopic composition.

Keywords: nuclear archaeology; nuclear forensics; disarmament; verification; Bayesian inference.

Reference guideline:

Figueroa, A., & Göttsche, M. (2019). Nuclear archaeology: reconstructing reactor histories from reprocessing waste. ESARDA Bulletin - The International Journal of Nuclear Safeguards and Non-proliferation, 59, 39-46. https://doi.org/10.3011/ESARDA.IJNSNP.2019.13