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Towards Transparent, Reproducible And Justified Nuclear Data Uncertainty Propagation For Lwr Applications
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Reactions)
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Reaktion)ORCID iD: 0000-0002-7595-8024
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. IAEA. (Nuclear reaction group)
(Laboratory for Reactor Physics Systems Behaviour, Paul Scherrer Institut, Villigen, Switzerland)
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2015 (English)Conference paper, Abstract (Other academic)
Abstract [en]

Any calculated quantity is practically meaningless without estimates on the uncertainty of theobtained results, not the least when it comes to, e.g., safety parameters in a nuclear reactor. Oneof the sources of uncertainty in reactor physics computations or simulations are the uncertaintiesof the so called nuclear data, i.e., cross sections, angular distributions, fission yields, etc. Thecurrently dominating method for propagating nuclear data uncertainties (using covariance dataand sensitivity analysis) suffers from several limitations, not the least in how the the covariancedata is produced – the production relies to a large extent on personal judgment of nuclear dataevaluators, leading to results which are difficult to reproduce from fundamental principles.Further, such a method assumes linearity, it in practice limits both input and output to bemodeled as Gaussian distributions, and the covariance data in the established nuclear datalibraries is incomplete.“Total Monte Carlo” (TMC) is a nuclear data uncertainty propagation method based on randomsampling of nuclear reaction model parameters which aims to resolve these issues. The methodhas been applied to various applications, ranging from pin cells and criticality safety benchmarksto full core neutronics as well as models including thermo-hydraulics and transients. However,TMC has been subject to some critique since the distributions of the nuclear model parameters,and hence of the nuclear data, has not been deduced from really rigorous statistical theory. Thispresentation briefly discusses the ongoing work on how to use experimental data to approachjustified results from TMC, including the effects of correlations between experimental datapoints and the assessment of such correlations. In this study, the random nuclear data libraries areprovided with likelihood weights based on their agreement to the experimental data, as a meansto implement Bayes' theorem.Further, it is presented how TMC is applied to an MCNP-6 model of shielding fuel assemblies(SFA) at Ringhals 3 and 4. Since the damage from the fast neutron flux may limit the lifetimes ofthese reactors, parts of the fuel adjacent to the pressure vessel is replaced by steel (the SFA) toprotect the vessel, in particular the four points along the belt-line weld which have been exposedto the largest fluence over time. The 56Fe data uncertainties are considered, and the estimatedrelative uncertainty at a quarter of the pressure vessel is viewed in Figure 1 (right) as well as theflux pattern itself (left). The uncertainty in the flux reduction at a selected sensitive point is 2.5± 0.2 % (one standard deviation). Applying the likelihood weights does not have muchimpact for this case, which could indicate that the prior distribution for the 56Fe data is too“narrow” (the used libraries are not really intended to describe a prior distribution), and that thetrue uncertainty is substantially greater. Another explanation could be that the dominating sourceof uncertainty is the high-energy resonances which are treated inefficiently by such weights.In either case, the efforts to approach justified, transparent, reproducible and highly automatizednuclear data uncertainties shall continue. On top of using libraries that are intended to describeprior distributions and treating the resonance region appropriately, the experimental correlationsshould be better motivated and the treatment of outliers shall be improved. Finally, it is probablynecessary to use experimental data in a more direct sense where a lot of experimental data isavailable, since the nuclear models are imperfect.Figure 1. The high energy neutron flux at the reactor pressure vessel in the SFA model, and thecorresponding propagated 56Fe data uncertainty.

Place, publisher, year, edition, pages
Keyword [en]
Nuclear data uncertainty, Shielding fuel assemblies, Total Monte Carlo
National Category
Subatomic Physics
URN: urn:nbn:se:uu:diva-274547OAI: oai:DiVA.org:uu-274547DiVA: diva2:896895
The 17th meeting on Reactor Physics in the Nordic Countries Chalmers University of Technology, Gothenburg, Sweden, May 11-12, 2015
Available from: 2016-01-22 Created: 2016-01-22 Last updated: 2016-07-18Bibliographically approved

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Helgesson, PetterSjöstrand, HenrikJ. Koning, ArjanAlhassan, ErwinPomp, Stephan
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