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Experimental data and Total Monte Carlo: Towards justified, transparent and complete nuclear data uncertainties
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Reactions)
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

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 59Ni. 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 59Ni 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.

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: diva2:865178
Presentation
2015-10-13, Polhemssalen, Ångströmslaboratoriet, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2015-11-04 Created: 2015-10-27 Last updated: 2015-11-04Bibliographically approved
List of papers
1. Incorporating Experimental Information in the Total Monte Carlo Methodology Using File Weights
Open this publication in new window or tab >>Incorporating Experimental Information in the Total Monte Carlo Methodology Using File Weights
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2015 (English)In: Nuclear Data Sheets, ISSN 0090-3752, E-ISSN 1095-9904, Vol. 123, no SI, 214-219 p.Article in journal (Refereed) Published
Abstract [en]

Some criticism has been directed towards the Total Monte Carlo method because experimental information has not been taken into account in a statistically well-founded manner. In this work, a Bayesian calibration method is implemented by assigning weights to the random nuclear data files and the method is illustratively applied to a few applications. In some considered cases, the estimated nuclear data uncertainties are significantly reduced and the central values are significantly shifted. The study suggests that the method can be applied both to estimate uncertainties in a more justified way and in the search for better central values. Some improvements are however necessary; for example, the treatment of outliers and cross-experimental correlations should be more rigorous and random files that are intended to be prior files should be generated.

National Category
Physical Sciences
Research subject
Physics with specialization in Applied Nuclear Physics
Identifiers
urn:nbn:se:uu:diva-224670 (URN)10.1016/j.nds.2014.12.037 (DOI)000348490700039 ()
Available from: 2014-05-16 Created: 2014-05-16 Last updated: 2017-12-05Bibliographically approved
2. Including experimental information in TMC using file weights from automatically generated experimental covariance matrices
Open this publication in new window or tab >>Including experimental information in TMC using file weights from automatically generated experimental covariance matrices
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

The Total Monte Carlo methodology (TMC) for nuclear data (ND) uncertainty propagation has been subject to some critique because the nuclear reaction parameters are sampled from distributions which have not been rigorously determined from experimental data. In this study, it is thoroughly explained how random ND files are weighted with likelihood function values computed by comparing the ND files to experimental data, using experimental covariance matrices generated from information in the experimental database EXFOR and a set of simple rules. A proof that such weights give a consistent implementation of Bayes' theorem is provided. The impact of the weights is mainly studied for a set of integral systems/applications, e.g., a set of shielding fuel assemblies which shall prevent aging of the pressure vessels of the Swedish nuclear reactors Ringhals 3 and 4.For many applications, the weighting does not have much impact, something which can be explained by too narrow prior distributions. Another possible explanation is that the integral systems are highly sensitive to resonance parameters, which effectively are not treated in this work. In other cases, only a very small number of files get significantly large weights, which can be due to the prior parameter distributions or model defects.Further, some parameters used in the rules for the EXFOR interpretation have been varied. The observed impact from varying one parameter at a time is not very strong. This can partially be due to the general insensitivity to the weights seen for many applications, and there can be strong interaction effects. The automatic treatment of outliers has a quite large impact, however. To approach more justified ND uncertainties, the rules for the EXFOR interpretation shall be further discussed and developed, in particular the rules for rejecting outliers, and random ND files that are intended to describe prior distributions shall be generated. Further, model defects need to be treated.

Keyword
Nuclear data, Uncertainty propagation, Total Monte Carlo, Experimental correlations
National Category
Physical Sciences
Research subject
Physics with specialization in Applied Nuclear Physics
Identifiers
urn:nbn:se:uu:diva-265323 (URN)
Available from: 2015-10-27 Created: 2015-10-27 Last updated: 2015-11-04
3. Sampling of systematic errors to estimate likelihood weights in nuclear data uncertainty propagation
Open this publication in new window or tab >>Sampling of systematic errors to estimate likelihood weights in nuclear data uncertainty propagation
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2016 (English)In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 807, 137-149 p.Article in journal (Refereed) Published
Abstract [en]

In methodologies for nuclear data (ND) uncertainty assessment and propagation based on random sampling, likelihood weights can be used to infer experimental information into the distributions for the ND. As the included number of correlated experimental points grows large, the computational time for the matrix inversion involved in obtaining the likelihood can become a practical problem. There are also other problems related to the conventional computation of the likelihood, e.g., the assumption that all experimental uncertainties are Gaussian. In this study, a way to estimate the likelihood which avoids matrix inversion is investigated; instead, the experimental correlations are included by sampling of systematic errors. It is shown that the model underlying the sampling methodology (using univariate normal distributions for random and systematic errors) implies a multivariate Gaussian for the experimental points (i.e., the conventional model). It is also shown that the likelihood estimates obtained through sampling of systematic errors approach the likelihood obtained with matrix inversion as the sample size for the systematic errors grows large. In studied practical cases, it is seen that the estimates for the likelihood weights converge impractically slowly with the sample size, compared to matrix inversion. The computational time is estimated to be greater than for matrix inversion in cases with more experimental points, too. Hence, the sampling of systematic errors has little potential to compete with matrix inversion in cases where the latter is applicable. Nevertheless, the underlying model and the likelihood estimates can be easier to intuitively interpret than the conventional model and the likelihood function involving the inverted covariance matrix. Therefore, this work can both have pedagogical value and be used to help motivating the conventional assumption of a multivariate Gaussian for experimental data. The sampling of systematic errors could also be used in cases where the experimental uncertainties are not Gaussian, and for other purposes than to compute the likelihood, e.g., to produce random experimental data sets for a more direct use in ND evaluation.

Place, publisher, year, edition, pages
Elsevier, 2016
Keyword
Nuclear data, Uncertainty propagation, Experimental correlations, Systematic uncertainty, Total Monte Carlo
National Category
Physical Sciences
Research subject
Physics with specialization in Applied Nuclear Physics
Identifiers
urn:nbn:se:uu:diva-265321 (URN)10.1016/j.nima.2015.10.024 (DOI)000365596200019 ()
Available from: 2015-10-27 Created: 2015-10-27 Last updated: 2017-12-01

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