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Data library of irradiated fuel salt and off-gas tank composition for a molten salt reactor concept produced with Serpent2 and SOURCES 4C codes

Authors :
Vaibhav Mishra
Zsolt Elter
Erik Branger
Sophie Grape
Sorouche Mirmiran
Source :
Data in Brief, Vol 54, Iss , Pp 110314- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

This paper describes the methodology used to create a fuel data library comprising safeguards-relevant quantities that may be useful for verification of spent nuclear fuel (SNF) produced by simulating a concept Molten Salt Reactor (MSR). The Monte-Carlo particle transport code, Serpent2 and the calculation code SOURCES 4C were used to compile this fuel data library. The data library is based on the Compact Molten Salt Reactor (CMSR) concept being developed by Seaborg Technologies (based in Copenhagen, Denmark). The library includes data such as nuclide mass densities for a total of 1398 nuclides (in g/cm3), as well as total decay heat production (denoted by suffix the ‘TOT_DH’) in Watts, total gamma photon emission rates (denoted by the suffix ‘TOT_GS’) in photos per second, and the total activity (denoted by suffix ‘TOT_A’) in Becquerel. Lastly, the data also includes total neutron emission rates from 1) spontaneous fission (denoted by ‘SF’ and reported in neutrons per second per cm3), and 2) (ɑ, n) reactions (denoted by ‘AN’ and reported in neutrons per second per cm3) for the fuel salt. These quantities are reported for a range of burnup-initial enrichment-cooling time (or collectively known as, BIC) parameters. The resulting fuel data library is an extension of a previously published data library for the same reactor concept but with one significant change. The current library is based on a more realistic model of the CMSR involving movement of gaseous and volatile fission products (GFP and VFP) from the core via an Off-Gas System (OGS). The dataset is made available for public use in a compressed binary format as an HDF5 (or Hierarchical Data Format) file that can be parsed using data analysis tools such as Pandas.

Details

Language :
English
ISSN :
23523409
Volume :
54
Issue :
110314-
Database :
Directory of Open Access Journals
Journal :
Data in Brief
Publication Type :
Academic Journal
Accession number :
edsdoj.6dab66fb7d1046079b4df3019d188c81
Document Type :
article
Full Text :
https://doi.org/10.1016/j.dib.2024.110314