000150789 001__ 150789
000150789 005__ 20251017144652.0
000150789 0247_ $$2doi$$a10.1016/j.net.2025.103462
000150789 0248_ $$2sideral$$a142713
000150789 037__ $$aART-2025-142713
000150789 041__ $$aeng
000150789 100__ $$aPaz-Peñuelas-Oliván, J.
000150789 245__ $$aMachine-learning methods for blind characterisation of nuclear fuel assemblies
000150789 260__ $$c2025
000150789 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150789 5203_ $$aThe global prevalence of uranium as fissile fuel in nuclear reactors, paired with its transmutation to plutonium inside the core in an isotopic ratio corresponding to a direct-use material in significant quantities, makes the handling of spent nuclear fuel an important and sensitive matter towards non-proliferation efforts. A fast and reliable method for characterising spent fuel is thus desirable for spent nuclear fuel reprocessing and storage facilities.
We propose a non-destructive, blind and fast measure method of the quantity of spent nuclear fuel inside an assembly. By measuring photon fluency collectively for the complete assembly we determine the number of present fuel rods without the need to open the array and manually check. For this, we circle a detector set-up around the assembly and feed its measurements into a neural network for a prediction. Different specifically designed architectures based on dense and convolutional layers are trained on synthetically generated data using self-developed code on python. We arrive at the election of a convolutional network for optimal results.
We achieve an exact prediction with over three sigmas of confidence (99.85% accuracy) thanks to the double detector set-up we introduce in this article, proving the prediction power of neural networks in this instance with a relatively simple measure configuration.
000150789 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000150789 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000150789 700__ $$0(orcid)0000-0002-3253-7027$$aRuz, J.
000150789 773__ $$g57, 6 (2025), 103462 [13 pp.]$$pNuclear Engineering and Technology$$tNUCLEAR ENGINEERING AND TECHNOLOGY$$x1738-5733
000150789 8564_ $$s3180525$$uhttps://zaguan.unizar.es/record/150789/files/texto_completo.pdf$$yVersión publicada
000150789 8564_ $$s2447925$$uhttps://zaguan.unizar.es/record/150789/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000150789 909CO $$ooai:zaguan.unizar.es:150789$$particulos$$pdriver
000150789 951__ $$a2025-10-17-14:36:46
000150789 980__ $$aARTICLE