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Experimental data and Total Monte Carlo: Towards justified, transparent and complete nuclear data uncertaintiesPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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2015 (English)Licentiate thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Uppsala universitet, 2015.
##### National Category

Physical Sciences
##### Research subject

Physics with specialization in Applied Nuclear Physics
##### Identifiers

URN: urn:nbn:se:uu:diva-265330OAI: oai:DiVA.org:uu-265330DiVA, id: diva2:865178
##### Presentation

2015-10-13, Polhemssalen, Ångströmslaboratoriet, Uppsala, 10:15 (English)
##### Opponent

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##### Supervisors

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PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt520",{id:"formSmash:j_idt520",widgetVar:"widget_formSmash_j_idt520",multiple:true}); Available from: 2015-11-04 Created: 2015-10-27 Last updated: 2015-11-04Bibliographically approved
##### List of papers

The applications of nuclear physics are many with one important being nuclear power, which can help decelerating the climate change. In any of these applications, so-called nuclear data (ND, numerical representations of nuclear physics) is used in computations and simulations which are necessary for, e.g., design and maintenance. The ND is not perfectly known - there are uncertainties associated with it - and this thesis concerns the quantification and propagation of these uncertainties. In particular, methods are developed to include experimental data in the Total Monte Carlo methodology (TMC). The work goes in two directions. One is to include the experimental data by giving weights to the different "random files" used in TMC. This methodology is applied to practical cases using an automatic interpretation of an experimental database, including uncertainties and correlations. The weights are shown to give a consistent implementation of Bayes' theorem, such that the obtained uncertainty estimates in theory can be correct, given the experimental data. In the practical implementation, it is more complicated. This is much due to the interpretation of experimental data, but also because of model defects - the methodology assumes that there are parameter choices such that the model of the physics reproduces reality perfectly. This assumption is not valid, and in future work, model defects should be taken into account. Experimental data should also be used to give feedback to the distribution of the parameters, and not only to provide weights at a later stage.The other direction is based on the simulation of the experimental setup as a means to analyze the experiments in a structured way, and to obtain the full joint distribution of several different data points. In practice, this methodology has been applied to the thermal (n,*α*), (n,p), (n,*γ*) and (n,tot) cross sections of ^{59}Ni. For example, the estimated expected value and standard deviation for the (n,*α*) cross section is (12.87 ± 0.72) b, which can be compared to the established value of (12.3 ± 0.6) b given in the work of Mughabghab. Note that also the correlations to the other thermal cross sections as well as other aspects of the distribution are obtained in this work - and this can be important when propagating the uncertainties. The careful evaluation of the thermal cross sections is complemented by a coarse analysis of the cross sections of ^{59}Ni at other energies. The resulting nuclear data is used to study the propagation of the uncertainties through a model describing stainless steel in the spectrum of a thermal reactor. In particular, the helium production is studied. The distribution has a large uncertainty (a standard deviation of (17 ± 3) \%), and it shows a strong asymmetry. Much of the uncertainty and its shape can be attributed to the more coarse part of the uncertainty analysis, which, therefore, shall be refined in the future.

1. Incorporating Experimental Information in the Total Monte Carlo Methodology Using File Weights$(function(){PrimeFaces.cw("OverlayPanel","overlay717742",{id:"formSmash:j_idt574:0:j_idt578",widgetVar:"overlay717742",target:"formSmash:j_idt574:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Including experimental information in TMC using file weights from automatically generated experimental covariance matrices$(function(){PrimeFaces.cw("OverlayPanel","overlay865170",{id:"formSmash:j_idt574:1:j_idt578",widgetVar:"overlay865170",target:"formSmash:j_idt574:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Sampling of systematic errors to estimate likelihood weights in nuclear data uncertainty propagation$(function(){PrimeFaces.cw("OverlayPanel","overlay865156",{id:"formSmash:j_idt574:2:j_idt578",widgetVar:"overlay865156",target:"formSmash:j_idt574:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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