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Nuclear data uncertainty quantification and data assimilation for a lead-cooled fast reactor: Using integral experiments for improved accuracy
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Research Group)
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

For the successful deployment of advanced nuclear systems and optimization of current reactor designs, high quality nuclear data are required. Before nuclear data can be used in applications they must first be evaluated, tested and validated against a set of integral experiments, and then converted into formats usable for applications. The evaluation process in the past was usually done by using differential experimental data which was then complemented with nuclear model calculations. This trend is fast changing due to the increase in computational power and tremendous improvements in nuclear reaction models over the last decade. Since these models have uncertain inputs, they are normally calibrated using experimental data. However, these experiments are themselves not exact. Therefore, the calculated quantities of model codes such as cross sections and angular distributions contain uncertainties. Since nuclear data are used in reactor transport codes as input for simulations, the output of transport codes contain uncertainties due to these data as well. Quantifying these uncertainties is important for setting safety margins; for providing confidence in the interpretation of results; and for deciding where additional efforts are needed to reduce these uncertainties. Also, regulatory bodies are now moving away from conservative evaluations to best estimate calculations that are accompanied by uncertainty evaluations.

In this work, the Total Monte Carlo (TMC) method was applied to study the impact of nuclear data uncertainties from basic physics to macroscopic reactor parameters for the European Lead Cooled Training Reactor (ELECTRA). As part of the work, nuclear data uncertainties of actinides in the fuel, lead isotopes within the coolant, and some structural materials have been investigated. In the case of the lead coolant it was observed that the uncertainty in the keff and the coolant void worth (except in the case of 204Pb), were large, with the most significant contribution coming from 208Pb. New 208Pb and 206Pb random nuclear data libraries with realistic central values have been produced as part of this work. Also, a correlation based sensitivity method was used in this work, to determine parameter - cross section correlations for different isotopes and energy groups.

Furthermore, an accept/reject method and a method of assigning file weights based on the likelihood function are proposed for uncertainty reduction using criticality benchmark experiments within the TMC method. It was observed from the study that a significant reduction in nuclear data uncertainty was obtained for some isotopes for ELECTRA after incorporating integral benchmark information. As a further objective of this thesis, a method for selecting benchmark for code validation for specific reactor applications was developed and applied to the ELECTRA reactor. Finally, a method for combining differential experiments and integral benchmark data for nuclear data adjustments is proposed and applied for the adjustment of neutron induced 208Pb nuclear data in the fast energy region.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. , 85 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1315
Keyword [en]
Total Monte Carlo, ELECTRA, nuclear data, uncertainty propagation, integral experiments, nuclear data adjustment, uncertainty reduction
National Category
Subatomic Physics
Identifiers
URN: urn:nbn:se:uu:diva-265502ISBN: 978-91-554-9407-0 (print)OAI: oai:DiVA.org:uu-265502DiVA: diva2:865971
Public defence
2015-12-17, polhemsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2015-11-24 Created: 2015-10-30 Last updated: 2016-01-13
List of papers
1. Combining Total Monte Carlo and Benchmarks for Nuclear Data Uncertainty Propagation on a Lead Fast Reactor's Safety Parameters
Open this publication in new window or tab >>Combining Total Monte Carlo and Benchmarks for Nuclear Data Uncertainty Propagation on a Lead Fast Reactor's Safety Parameters
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2014 (English)In: Nuclear Data Sheets, ISSN 0090-3752, E-ISSN 1095-9904, Vol. 118, 542-544 p.Article in journal (Refereed) Published
Abstract [en]

Analyses are carried out to assess the impact of nuclear data uncertainties on some reactor safety parameters for the European Lead Cooled Training Reactor (ELECTRA) using the Total Monte Carlo method. A large number of Pu-239 random ENDF-format libraries, generated using the TALYS based system were processed into ACE format with NJOY99.336 code and used as input into the Serpent Monte Carlo code to obtain distribution in reactor safety parameters. The distribution in keff obtained was compared with the latest major nuclear data libraries – JEFF-3.1.2, ENDF/B-VII.1 and JENDL-4.0. A method is proposed for the selection of benchmarks for specific applications using the Total Monte Carlo approach based on a correlation observed between the keff of a given system and the benchmark. Finally, an accept/reject criteria was investigated based on chi squared values obtained using the Pu-239 Jezebel criticality benchmark. It was observed that nuclear data uncertainties were reduced considerably from 748 to 443 pcm.

Keyword
GENIV, reactor safety parameters, ELECTRA, nuclear data uncertainty, TMC
National Category
Other Physics Topics
Research subject
Applied Nuclear Physics
Identifiers
urn:nbn:se:uu:diva-197305 (URN)10.1016/j.nds.2014.04.129 (DOI)000347704400128 ()
Conference
International conference on nuclear data for science and technology, 4-8 March, 2013, New York USA
Projects
Total Monte Carlo
Available from: 2013-03-22 Created: 2013-03-21 Last updated: 2017-12-06Bibliographically approved
2. Uncertainty and correlation analysis of lead nuclear data on reactor parameters for the European Lead Cooled Training Reactor
Open this publication in new window or tab >>Uncertainty and correlation analysis of lead nuclear data on reactor parameters for the European Lead Cooled Training Reactor
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2015 (English)In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100, Vol. 75, 26-37 p.Article in journal (Refereed) Published
Abstract [en]

The Total Monte Carlo (TMC) method was used in this study to assess the impact of Pb-204, 206, 207, 208 nuclear data uncertainties on reactor safety parameters for the ELECTRA reactor. Relatively large uncertainties were observed in the k-eff and the coolant void worth (CVW) for all isotopes except for Pb-204 with signicant contribution coming from Pb-208 nuclear data; the dominant eectcame from uncertainties in the resonance parameters; however, elastic scattering cross section and the angular distributions also had signicant impact. It was also observed that the k-eff distribution for Pb-206, 207, 208 deviates from a Gaussian distribution with tails in the high k-eff region. An uncertainty of 0.9% on the k-eff and 3.3% for the CVW due to lead nuclear data were obtained. As part of the work, cross section-reactor parameter correlations were also studied using a Monte Carlo sensitivity method. Strong correlations were observed between the k-eff and (n,el) cross section for all the lead isotopes. The correlation between the (n,inl) and the k-eff was also found to be signicant.

Keyword
TMC, nuclear data uncertainty, lead isotopes, safety parameters, ELECTRA, fuel cycle
National Category
Subatomic Physics
Research subject
Applied Nuclear Physics
Identifiers
urn:nbn:se:uu:diva-224524 (URN)10.1016/j.anucene.2014.07.043 (DOI)000347493400004 ()
Projects
GENIUS project
Available from: 2014-05-13 Created: 2014-05-13 Last updated: 2017-12-05Bibliographically approved
3. Propagation of nuclear data uncertainties for ELECTRA burn-up calculations
Open this publication in new window or tab >>Propagation of nuclear data uncertainties for ELECTRA burn-up calculations
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2014 (English)In: Nuclear Data Sheets, ISSN 0090-3752, E-ISSN 1095-9904, Vol. 118, 527-530 p.Article in journal (Refereed) Published
Abstract [en]

The European Lead-Cooled Training Reactor (ELECTRA) has been proposed as a training reactor for fast systems within the Swedish nuclear program. It is a low -power fast reactor cooled by pure liquid lead. In this work, we propagate the uncertainties in 239Pu transport data to uncertainties in the fuel inventory of ELECTRA during the reactor life using the Total Monte Carlo approach(TMC). Within the TENDL project the nuclear models input parameters were randomized within their uncertainties and 740 239Pu nuclear data libraries were generated. These libraries are used as inputs to reactor codes, in our case SERPENT, to perform uncertainty analysis of nuclear reactor inventory during burn-up. The uncertainty in the inventory determines uncertainties in: the long term radio-toxicity, the decay heat, the evolution of reactivity parameters, gas pressure and volatile fission product content. In this work, a methodology called fast TMC is utilized, which reduces the overall calculation time. The uncertainty in the long-term radiotoxicity, decay heat, gas pressureand volatile fission products were found to be insignificant. However, the uncertainty of some minor actinides were observed to be rather large and therefore their impact on multiple recycling should be investigated further. It was also found that, criticality benchmarks can be used to reduce inventory uncertainties due to nuclear data. Further studies are needed to include fission yield uncertainties, more isotopes, and a larger set of benchmarks.

National Category
Other Physics Topics
Research subject
Applied Nuclear Physics
Identifiers
urn:nbn:se:uu:diva-209383 (URN)10.1016/j.nds.2014.04.125 (DOI)000347704400124 ()
Conference
International conference on nuclear data for science and technology, 4-8 March, 2013, New York USA.
Available from: 2013-10-18 Created: 2013-10-18 Last updated: 2017-12-06Bibliographically approved
4. On the use of integral experiments for uncertainty reduction of reactor macroscopic parameters within the TMC methodology
Open this publication in new window or tab >>On the use of integral experiments for uncertainty reduction of reactor macroscopic parameters within the TMC methodology
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2016 (English)In: Progress in nuclear energy (New series), ISSN 0149-1970, E-ISSN 1878-4224, Vol. 88, 43-52 p.Article in journal (Refereed) Published
Abstract [en]

The current nuclear data uncertainties observed in reactor safety parameters for some nuclides call for safety concerns especially with respect to the design of GEN-IV reactors and must therefore be reduced significantly. In this work, uncertainty reduction using criticality benchmark experiments within the Total Monte Carlo methodology is presented. Random nuclear data libraries generated are processed and used to analyze a set of criticality benchmarks. Since the calculated results for each random nuclear data used are different, an algorithm was used to select (or assign weights to) the libraries which give a good description of experimental data for the analyses of the benchmarks. The selected or weighted libraries were then used to analyze the ELECTRA reactor. By using random nuclear data libraries constrained with only differential experimental data as our prior, the uncertainties observed were further reduced by constraining the files with integral experimental data to obtain a posteriori uncertainties on the k(eff). Two approaches are presented and compared: a binary accept/reject and a method of assigning file weights based on the likelihood function. Significant reductions in (PU)-P-239 and Pb-208 nuclear data uncertainties in the k(eff) were observed after implementing the two methods with some criticality benchmarks for the ELELIRA reactor. (C) 2015 Elsevier Ltd. All rights reserved.

Keyword
Nuclear data, uncertainty reduction, binary accept/reject, file weights, Total Monte Carlo, criticality benchmarks, ELECTRA
National Category
Subatomic Physics
Research subject
Physics with specialization in Applied Nuclear Physics
Identifiers
urn:nbn:se:uu:diva-264410 (URN)10.1016/j.pnucene.2015.11.015 (DOI)000372564400006 ()
Funder
Swedish Research Council
Available from: 2015-10-11 Created: 2015-10-11 Last updated: 2017-12-01Bibliographically approved
5. Selecting benchmarks for reactor simulations: an application to a Lead Fast Reactor
Open this publication in new window or tab >>Selecting benchmarks for reactor simulations: an application to a Lead Fast Reactor
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2016 (English)In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100, Vol. 96, 158-169 p.Article in journal (Refereed) Published
Abstract [en]

For several decades reactor design has been supported by computer codes for the investigation of reactor behavior under both steady state and transient conditions. The use of computer codes to simulate reactor behavior enables the investigation of various safety scenarios saving time and cost. There has been an increase in the development of in-house (local) codes by various research groups in recent times for preliminary design of specific or targeted nuclear reactor applications. These codes must be validated and calibrated against experimental benchmark data with their evolution and improvements. Given the large number of benchmarks available, selecting these benchmarks for reactor calculations and validation of simulation codes for specific or target applications can be rather tedious and difficult. In the past, the traditional approach based on expert judgement using information provided in various handbooks, has been used for the selection of these benchmarks. This approach has been criticized because it introduces a user bias into the selection process. This paper presents a method for selecting these benchmarks for reactor calculations for specific reactor applications based on the Total Monte Carlo (TMC) method. First, nuclear model parameters are randomly sampled within a given probability distribution and a large set of random nuclear data files are produced using the TALYS code system. These files are processed and used to analyze a target reactor system and a set of criticality benchmarks. Similarity between the target reactor system and one or several benchmarks is quantified using a similarity index. The method has been applied to the European Lead Cooled Reactor (ELECTRA) and a set of plutonium and lead sensitive criticality benchmarks using the effective multiplication factor (keffkeff). From the study, strong similarity were observed in the keffkeff between ELECTRA and some plutonium and lead sensitive criticality benchmarks. Also, for validation purposes, simulation results for a list of selected criticality benchmarks simulated with the MCNPX and SERPENT codes using different nuclear data libraries have been compared with experimentally measured benchmark keff values.

Keyword
Benchmark selection; Criticality benchmarks; Random nuclear data; Total Monte Carlo (TMC); Code validation
National Category
Subatomic Physics
Identifiers
urn:nbn:se:uu:diva-264414 (URN)10.1016/j.anucene.2016.05.033 (DOI)000380600300017 ()
External cooperation:
Funder
Swedish Research Council
Available from: 2015-10-11 Created: 2015-10-11 Last updated: 2017-12-01Bibliographically approved

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