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Selecting benchmarks for reactor calculations
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Research Group)
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Reactions Group)
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Reactions Group)
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Reactions Group)
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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.

Place, publisher, year, edition, pages
2014.
Keyword [en]
Criticality benchmarks, ELECTRA, TMC, nuclear data, GENIV, reactor calculations
National Category
Subatomic Physics
Research subject
Physics with specialization in Applied Nuclear Physics
Identifiers
URN: urn:nbn:se:uu:diva-216828OAI: oai:DiVA.org:uu-216828DiVA: diva2:691048
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
In thesis
1. Nuclear data uncertainty propagation for a lead-cooled fast reactor: Combining TMC with criticality benchmarks for improved accuracy
Open this publication in new window or tab >>Nuclear data uncertainty propagation for a lead-cooled fast reactor: Combining TMC with criticality benchmarks for improved accuracy
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:nbn:se:uu:diva-224687 (URN)
Opponent
Supervisors
Available from: 2014-05-23 Created: 2014-05-17 Last updated: 2014-05-23Bibliographically approved

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Alhassan, ErwinSjöstrand, HenrikHelgesson, PetterÖsterlund, Michael
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