Assay data of spent nuclear fuel: the lab-work behind the numbers
Computational modelling for spent nuclear fuel (SNF) characterization is already widely used and continuously further developed for a plethora of safety related applications and licensing issues in SNF management. An essential step in the development of these methodologies is the validation: the dem...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1168460/full |
| _version_ | 1797786837921562624 |
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| author | Stefaan Van Winckel Rafael Alvarez-Sarandes Daniel Serrano Purroy Laura Aldave de las Heras |
| author_facet | Stefaan Van Winckel Rafael Alvarez-Sarandes Daniel Serrano Purroy Laura Aldave de las Heras |
| author_sort | Stefaan Van Winckel |
| collection | DOAJ |
| description | Computational modelling for spent nuclear fuel (SNF) characterization is already widely used and continuously further developed for a plethora of safety related applications and licensing issues in SNF management. An essential step in the development of these methodologies is the validation: the demonstration that the SNF elemental and isotopic composition is sufficiently accurately predicted by the code calculations. This validation step requires experimentally measured nuclide concentrations in SNF, together with an estimation of related uncertainties. The SFCOMPO 2.0 database of OECD/NEA is a database of such publicly available assay data of SNF. A basic understanding of all analytical steps that finally result in assay data of SNF is important for modelers when assessing the “fit-for-validation” requirement of an experimental dataset. The aim of this article is to explain users of such datasets the complex analytical pathway towards assay data. Points of attention, challenges and pitfalls all along the analytical pathway will be discussed, from sampling, dissolution procedures, necessary dilutions and separations, available analytical techniques, some related uncertainties, up to reporting of the results. |
| first_indexed | 2024-03-13T01:14:34Z |
| format | Article |
| id | doaj.art-5e2c2596d6c74d64b133e76369f88000 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-13T01:14:34Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-5e2c2596d6c74d64b133e76369f880002023-07-05T13:55:21ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-07-011110.3389/fenrg.2023.11684601168460Assay data of spent nuclear fuel: the lab-work behind the numbersStefaan Van WinckelRafael Alvarez-SarandesDaniel Serrano PurroyLaura Aldave de las HerasComputational modelling for spent nuclear fuel (SNF) characterization is already widely used and continuously further developed for a plethora of safety related applications and licensing issues in SNF management. An essential step in the development of these methodologies is the validation: the demonstration that the SNF elemental and isotopic composition is sufficiently accurately predicted by the code calculations. This validation step requires experimentally measured nuclide concentrations in SNF, together with an estimation of related uncertainties. The SFCOMPO 2.0 database of OECD/NEA is a database of such publicly available assay data of SNF. A basic understanding of all analytical steps that finally result in assay data of SNF is important for modelers when assessing the “fit-for-validation” requirement of an experimental dataset. The aim of this article is to explain users of such datasets the complex analytical pathway towards assay data. Points of attention, challenges and pitfalls all along the analytical pathway will be discussed, from sampling, dissolution procedures, necessary dilutions and separations, available analytical techniques, some related uncertainties, up to reporting of the results.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1168460/fullassay dataspent nuclear fuelchemical analysisradiometric techniquesmass spectrometrymeasurement uncertainty |
| spellingShingle | Stefaan Van Winckel Rafael Alvarez-Sarandes Daniel Serrano Purroy Laura Aldave de las Heras Assay data of spent nuclear fuel: the lab-work behind the numbers Frontiers in Energy Research assay data spent nuclear fuel chemical analysis radiometric techniques mass spectrometry measurement uncertainty |
| title | Assay data of spent nuclear fuel: the lab-work behind the numbers |
| title_full | Assay data of spent nuclear fuel: the lab-work behind the numbers |
| title_fullStr | Assay data of spent nuclear fuel: the lab-work behind the numbers |
| title_full_unstemmed | Assay data of spent nuclear fuel: the lab-work behind the numbers |
| title_short | Assay data of spent nuclear fuel: the lab-work behind the numbers |
| title_sort | assay data of spent nuclear fuel the lab work behind the numbers |
| topic | assay data spent nuclear fuel chemical analysis radiometric techniques mass spectrometry measurement uncertainty |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1168460/full |
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