Towards inferring reactor operations from high-level waste
Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past fissile material production. While it has typically focused on analyzing material in permanent reactor structures, spent fuel or high-level wa...
Saved in:
Published in | arXiv.org |
---|---|
Main Authors | , , |
Format | Paper Journal Article |
Language | English |
Published |
Ithaca
Cornell University Library, arXiv.org
26.02.2024
|
Subjects | |
Online Access | Get full text |
ISSN | 2331-8422 |
DOI | 10.48550/arxiv.2402.16443 |
Cover
Abstract | Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past fissile material production. While it has typically focused on analyzing material in permanent reactor structures, spent fuel or high-level waste also hold information about the reactor operation. In this computational study, we explore a Bayesian inference framework for reconstructing the operational history from measurements of isotope ratios from a sample of nuclear waste . We investigate two different inference models. The first model discriminates between three potential reactors of origin (Magnox, PWR, and PHWR) while simultaneously reconstructing the fuel burnup, time since irradiation, initial enrichment, and average power density. The second model reconstructs the fuel burnup and time since irradiation of two batches of waste in a mixed sample. Each of the models is applied to a set of simulated test data, and the performance is evaluated by comparing the highest posterior density regions to the corresponding parameter values of the test dataset. Both models perform well on the simulated test cases, which highlights the potential of the Bayesian inference framework and opens up avenues for further investigation |
---|---|
AbstractList | Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past fissile material production. While it has typically focused on analyzing material in permanent reactor structures, spent fuel or high-level waste also hold information about the reactor operation. In this computational study, we explore a Bayesian inference framework for reconstructing the operational history from measurements of isotope ratios from a sample of nuclear waste . We investigate two different inference models. The first model discriminates between three potential reactors of origin (Magnox, PWR, and PHWR) while simultaneously reconstructing the fuel burnup, time since irradiation, initial enrichment, and average power density. The second model reconstructs the fuel burnup and time since irradiation of two batches of waste in a mixed sample. Each of the models is applied to a set of simulated test data, and the performance is evaluated by comparing the highest posterior density regions to the corresponding parameter values of the test dataset. Both models perform well on the simulated test cases, which highlights the potential of the Bayesian inference framework and opens up avenues for further investigation Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past fissile material production. While it has typically focused on analyzing material in permanent reactor structures, spent fuel or high-level waste also hold information about the reactor operation. In this computational study, we explore a Bayesian inference framework for reconstructing the operational history from measurements of isotope ratios from a sample of nuclear waste . We investigate two different inference models. The first model discriminates between three potential reactors of origin (Magnox, PWR, and PHWR) while simultaneously reconstructing the fuel burnup, time since irradiation, initial enrichment, and average power density. The second model reconstructs the fuel burnup and time since irradiation of two batches of waste in a mixed sample. Each of the models is applied to a set of simulated test data, and the performance is evaluated by comparing the highest posterior density regions to the corresponding parameter values of the test dataset. Both models perform well on the simulated test cases, which highlights the potential of the Bayesian inference framework and opens up avenues for further investigation |
Author | Jung, Benjamin Göttsche, Malte Figueroa, Antonio |
Author_xml | – sequence: 1 givenname: Benjamin surname: Jung fullname: Jung, Benjamin – sequence: 2 givenname: Antonio surname: Figueroa fullname: Figueroa, Antonio – sequence: 3 givenname: Malte surname: Göttsche fullname: Göttsche, Malte |
BackLink | https://doi.org/10.1016/j.net.2024.02.031$$DView published paper (Access to full text may be restricted) https://doi.org/10.48550/arXiv.2402.16443$$DView paper in arXiv |
BookMark | eNotz7tOwzAYBWALgUQpfQAmIjEnOL_vbKjiJlViyR45id26Su1gpy28PaFlOsvR0flu0KUP3iB0V-KCSsbwo47f7lAAxVCUnFJygWZASJlLCnCNFiltMcbABTBGZuipCkcdu5Q5b02Mzq-zaHQ7hpiFwUQ9uuBTZmPYZRu33uS9OZg-O-o0mlt0ZXWfzOI_56h6famW7_nq8-1j-bzKNQPIW9t2DDQnkhneCtlpgRlnBhOlraaNUKUiVEDZdkoqQXjTUAuWqg53kgtM5uj-PHuC1UN0Ox1_6j9gfQJOjYdzY4jha2_SWG_DPvrpUw2KACnZRCe_9e1UYA |
ContentType | Paper Journal Article |
Copyright | 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0 |
Copyright_xml | – notice: 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: http://creativecommons.org/licenses/by/4.0 |
DBID | 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS GOX |
DOI | 10.48550/arxiv.2402.16443 |
DatabaseName | ProQuest SciTech Collection ProQuest Technology Collection ProQuest Materials Science & Engineering ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC ProQuest Central Technology Collection (via ProQuest SciTech Premium Collection) ProQuest One Community College ProQuest Central SciTech Premium Collection ProQuest Engineering Collection Engineering Database ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering collection arXiv.org |
DatabaseTitle | Publicly Available Content Database Engineering Database Technology Collection ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) Engineering Collection |
DatabaseTitleList | Publicly Available Content Database |
Database_xml | – sequence: 1 dbid: GOX name: arXiv.org url: http://arxiv.org/find sourceTypes: Open Access Repository – sequence: 2 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Physics |
EISSN | 2331-8422 |
ExternalDocumentID | 2402_16443 |
Genre | Working Paper/Pre-Print |
GroupedDBID | 8FE 8FG ABJCF ABUWG AFKRA ALMA_UNASSIGNED_HOLDINGS AZQEC BENPR BGLVJ CCPQU DWQXO FRJ HCIFZ L6V M7S M~E PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS GOX |
ID | FETCH-LOGICAL-a522-cfcd52a6385e6c78da70565e039afa4b791934721cd989736bb4f2f49d0d86703 |
IEDL.DBID | BENPR |
IngestDate | Tue Jul 22 23:01:59 EDT 2025 Mon Jun 30 09:10:02 EDT 2025 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | false |
IsScholarly | false |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-a522-cfcd52a6385e6c78da70565e039afa4b791934721cd989736bb4f2f49d0d86703 |
Notes | SourceType-Working Papers-1 ObjectType-Working Paper/Pre-Print-1 content type line 50 |
OpenAccessLink | https://www.proquest.com/docview/2932315422?pq-origsite=%requestingapplication%&accountid=15518 |
PQID | 2932315422 |
PQPubID | 2050157 |
ParticipantIDs | arxiv_primary_2402_16443 proquest_journals_2932315422 |
PublicationCentury | 2000 |
PublicationDate | 20240226 2024-02-26 |
PublicationDateYYYYMMDD | 2024-02-26 |
PublicationDate_xml | – month: 02 year: 2024 text: 20240226 day: 26 |
PublicationDecade | 2020 |
PublicationPlace | Ithaca |
PublicationPlace_xml | – name: Ithaca |
PublicationTitle | arXiv.org |
PublicationYear | 2024 |
Publisher | Cornell University Library, arXiv.org |
Publisher_xml | – name: Cornell University Library, arXiv.org |
SSID | ssj0002672553 |
Score | 1.8667755 |
SecondaryResourceType | preprint |
Snippet | Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past... Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past... |
SourceID | arxiv proquest |
SourceType | Open Access Repository Aggregation Database |
SubjectTerms | Archaeology Bayesian analysis Fissionable materials Irradiation Isotope ratios Magnox Nuclear reactors PHWR Physics - Physics and Society Pressurized water reactors Radioactive wastes Spent nuclear fuels Statistical inference |
SummonAdditionalLinks | – databaseName: arXiv.org dbid: GOX link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwdV09T8MwED21nVgQCFALBXlgNSS248RsCFEqJGAJUrbIjm0JCaVVUz5-PmcnFQNijZwh9-zce5e7F4BL1BzMKYbaxDJLhU81Vd4aanTjpcsMF9GB7-lZLl_FY5VVIyC7WRi9-X777P2BTXcdSv9XSOgFH8OYsSCuHl6q_uNktOIa1v-uQ44ZL_15tcZ8sTiA_YHokdsemUMYufYIbsrYpdqR0AO1CSU1gqQt1M3Jau16MDoSRj5I8BGm76Glh3xphOIYysV9ebekw98LqEZOQxvf2Ixp3N6Zk01eWJ0j18hcwpX2WphcIXUSqL8aqwqVc2mM8MwLZRNbSDyHJzBpV62bAkkZ6g6bepdIIxJjDStsFnzMJPNpwvkMpvGZ63VvUFGHcNQxHDOY78JQD5uzqzHDI6tDkNjp_3eewR7D_B2nt-UcJtvNhzvH_Ls1FxGEH8GnhIE priority: 102 providerName: Cornell University |
Title | Towards inferring reactor operations from high-level waste |
URI | https://www.proquest.com/docview/2932315422 https://arxiv.org/abs/2402.16443 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LS8NAEB7aBsGbT1qtJQevaZPNZpMIIih9ILQWqdBb2M3ugiBtTOvj5G93dpvoQfASSAKB7OzOfPP6BuASfQ6iUoK-iSTSozrgXqql8ATPNVORCKll4JvO2OSJ3i-jZQNmdS-MKausdaJV1HKdmxj5AM0SQhH8MrkpXj0zNcpkV-sRGrwarSCvLcVYExxUyZHfAud2OJs__kRdCIsRQ4e79KYl8xrw8vP5vW-SDH10HUz3jmMf_VHO1uKMDsCZ80KVh9BQqyPYs4Wa-eYYrha2znXjmiqq0gTlXIR9JvLurgu1E-fGNU0jrmEi9l5MUZD7wVGYJ7AYDRd3E6-af-BxREVernMZEY4HJFIsjxPJY0QrkfLDlGtORZwi-KLoweUyTdI4ZEJQTTRNpS8Thif5FFqr9Uq1wQ0Iei4y0MpngvpCCpLIyDChMaIDPww70Lb_nBU7iovMLEdml6MD3XoZsmp7b7JfYZz9__oc9gmiANsDzrrQ2pZv6gKt-Fb0oJmMxr1KQHg3fljidfo1_AbpOZ7O |
linkProvider | ProQuest |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LTwIxEJ4gxOjNZ0BR96DHhaXtdndNjAcVQR7xgAm3TbttExMDyKLoj_I_Oi2gBxNvXHeTJp2ZTr9vOg-Ac-QcRCcEuYkiymemIfzEKOlLkRmuQ0mZ68DX6_PWE3sYhsMCfK1qYWxa5conOketxpmNkdfxWkIogiuT68mrb6dG2dfV1QiNhVl09OccKVt-1b5F_V4Q0rwb3LT85VQBXyDW8DOTqZAINLtQ8yyKlYgQA4Q6oIkwgskoQUjDkBdlKomTiHIpmSGGJSpQMcfzgctuQIlRSm0GYdy8_wnpEB4hQKeLt1PXKawuph_P7zX7glFDXmJLg0ru0x_P766z5g6UHsVET3ehoEd7sOmyQLN8Hy4HLok292yK1tRG_DzElDas740nemEruWcrUjzb5th_sRlH3lygpRzAYB1iOITiaDzSZfAaBGmRahgdcMkCqSSJVWjbrHFiGgGlFSi7PaeTRf-M1IojdeKoQHUlhnR5dvL0V9NH__8-g63WoNdNu-1-5xi2CcINV2zOq1CcTd_0CcKFmTx1SvIgXbNRfAMWsdBG |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Towards+inferring+reactor+operations+from+high-level+waste&rft.jtitle=arXiv.org&rft.au=Jung%2C+Benjamin&rft.au=Figueroa%2C+Antonio&rft.au=G%C3%B6ttsche%2C+Malte&rft.date=2024-02-26&rft.pub=Cornell+University+Library%2C+arXiv.org&rft.eissn=2331-8422&rft_id=info:doi/10.48550%2Farxiv.2402.16443 |