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Nuclear data uncertainty propagation for a lead-cooled fast reactor: Combining TMC with criticality benchmarks for improved accuracy
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Research Group)
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

For the successful deployment of advanced nuclear systems and for optimization of current reactor designs, high quality and accurate nuclear data are required. Before nuclear data can be used in applications, they are first evaluated, benchmarked against 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 complimented with nuclear model calculations. This trend is fast changing because of increase in computational power and tremendous improvements in nuclear reaction theories over the last decade. Since these model codes are not perfect, they are usually validated against a large set of experimental data. However, since these experiments are themselves not exact, the calculated quantities of model codes such as cross sections, angular distributions etc., contain uncertainties. A major source of uncertainty being the input parameters to these model codes. Since nuclear data are used in reactor transport codes asinput for simulations, the output of transport codes ultimately contain uncertainties due to these data. Quantifying these uncertainties is therefore important for reactor safety assessment and also for deciding where additional efforts could be taken to reduce further, these uncertainties.

Until recently, these uncertainties were mostly propagated using the generalized perturbation theory. With the increase in computational power however, more exact methods based on Monte Carlo are now possible. In the Nuclear Research and Consultancy Group (NRG), Petten, the Netherlands, a new method called ’Total Monte carlo (TMC)’ has been developed for nuclear data evaluation and uncertainty propagation. An advantage of this approach is that, it eliminates the use of covariances and the assumption of linearity that is used in the perturbation approach.

In this work, we have applied the TMC methodology for assessing the impact of nuclear data uncertainties on reactor macroscopic parameters of the European Lead Cooled Training Reactor (ELECTRA). ELECTRA has been proposed within the GEN-IV initiative within Sweden. As part of the work, the uncertainties of plutonium isotopes and americium within the fuel, uncertainties of the lead isotopes within the coolant and some structural materials of importance have been investigated at the beginning of life. For the actinides, large uncertainties were observed in the k-eff due to Pu-238, 239, 240 nuclear data while for the lead coolant, the uncertainty in the k-eff for all the lead isotopes except for Pb-204 were large with significant contribution coming from Pb-208. The dominant contributions to the uncertainty in the k-eff came from uncertainties in the resonance parameters for Pb-208.

Also, before the final product of an evaluation is released, evaluated data are tested against a large set of integral benchmark experiments. Since these benchmarks differ in geometry, type, material composition and neutron spectrum, their selection for specific applications is normally tedious and not straight forward. As a further objective in this thesis, methodologies for benchmark selection based the TMC method have been developed. This method has also been applied for nuclear data uncertainty reduction using integral benchmarks. From the results obtained, it was observed that by including criticality benchmark experiment information using a binary accept/reject method, a 40% and 20% reduction in nuclear data uncertainty in the k-eff was achieved for Pu-239 and Pu-240 respectively for ELECTRA.

Place, publisher, year, edition, pages
Uppsala universitet, 2014. , 100 p.
National Category
Other Physics Topics
Research subject
Applied Nuclear Physics
Identifiers
URN: urn:nbn:se:uu:diva-224687OAI: oai:DiVA.org:uu-224687DiVA: diva2:717816
Opponent
Supervisors
Available from: 2014-05-23 Created: 2014-05-17 Last updated: 2014-05-23Bibliographically approved
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. Selecting benchmarks for reactor calculations
Open this publication in new window or tab >>Selecting benchmarks for reactor calculations
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2014 (English)In: PHYSOR 2014 - The Role of Reactor Physics toward a Sustainable Future, 2014Conference paper, Published paper (Refereed)
Abstract [en]

Criticality, reactor physics, fusion and shielding benchmarks are expected to play important roles in GENIV design, safety analysis and in the validation of analytical tools used to design these reactors. For existing reactor technology, benchmarks are used to validate computer codes and test nuclear data libraries. However the selection of these benchmarks are usually done by visual inspection which is dependent on the expertise and the experience of the user and there by resulting in a user bias in the process. In this paper we present a method for the selection of these benchmarks for reactor applications based on Total Monte Carlo (TMC). Similarities betweenan application case and one or several benchmarks are quantified using the correlation coefficient. Based on the method, we also propose an approach for reducing nuclear data uncertainty using integral benchmark experiments as an additional constrain on nuclear reaction models: a binary accept/reject criterion. Finally, the method was applied to a full Lead Fast Reactor core and a set of criticality benchmarks.

Keyword
Criticality benchmarks, ELECTRA, TMC, nuclear data, GENIV, reactor calculations
National Category
Subatomic Physics
Research subject
Physics with specialization in Applied Nuclear Physics
Identifiers
urn:nbn:se:uu:diva-216828 (URN)
Conference
PHYSOR 2014 International Conference; Kyoto, Japan; 28 Sep. - 3 Oct., 2014
Available from: 2014-01-26 Created: 2014-01-26 Last updated: 2017-01-25Bibliographically approved

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