| 1. |
- Helgesson, Petter, 1986-, et al.
(författare)
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Combining Total Monte Carlo and Unified Monte Carlo : Bayesian nuclear data uncertainty quantification from auto-generated experimental covariances
- 2017
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Ingår i: Progress in nuclear energy (New series). - : Elsevier. - 0149-1970 .- 1878-4224. ; 96, s. 76-96
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Tidskriftsartikel (refereegranskat)abstract
- 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 TMC and Unified Monte Carlo-B (UMC-B) are combined to include experimental data in TMC. 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.In this implementation, the impact from the weighting is small for many of the applications. In some cases, this can be explained by the fact that the distributions used as priors are too narrow to be valid as such. 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, i.e., the region of interest is poorly resolved. This convergence issue can be due to the parameter distributions used as priors or model defects, for example.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.
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| 2. |
- Helgesson, Petter, 1986-, et al.
(författare)
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Fitting a defect non-linear model with or without prior, distinguishing nuclear reaction products as an example
- 2017
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Ingår i: Review of Scientific Instruments. - : AIP Publishing. - 0034-6748 .- 1089-7623. ; 88
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Tidskriftsartikel (refereegranskat)abstract
- Fitting parametrized functions to data is important for many researchers and scientists. If the model is non-linear and/or defect, it is not trivial to do correctly and to include an adequate uncertainty analysis. This work presents how the Levenberg-Marquardt algorithm for non-linear generalized least squares fitting can be used with a prior distribution for the parameters, and how it can be combined with Gaussian processes to treat model defects. An example, where three peaks in a histogram are to be distinguished, is carefully studied. In particular, the probability r1 for a nuclear reaction to end up in one out of two overlapping peaks is studied. Synthetic data is used to investigate effects of linearizations and other assumptions. For perfect Gaussian peaks, it is seen that the estimated parameters are distributed close to the truth with good covariance estimates. This assumes that the method is applied correctly; for example, prior knowledge should be implemented using a prior distribution, and not by assuming that some parameters are perfectly known (if they are not). It is also important to update the data covariance matrix using the fit if the uncertainties depend on the expected value of the data (e.g., for Poisson counting statistics or relative uncertainties). If a model defect is added to the peaks, such that their shape is unknown, a fit which assumes perfect Gaussian peaks becomes unable to reproduce the data, and the results for r1 become biased. It is, however, seen that it is possible to treat the model defect with a Gaussian process with a covariance function tailored for the situation, with hyper-parameters determined by leave-one-out cross validation. The resulting estimates for r1 are virtually unbiased, and the uncertainty estimates agree very well with the underlying uncertainty.
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| 3. |
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| 4. |
- Helgesson, Petter, 1986-, et al.
(författare)
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Treating model defects with a Gaussian Process prior for the parameters
- 2017
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The covariance information in TENDL is obtained by propagating uncertainties of, e.g., TALYSparameters to the observables, and by attempting to match the parameter uncertainties to the experimental data. This results in model-driven covariances with strong energy‐energy correlations, which can lead to erroneously estimated uncertainties for both differential and integral observables.Further, the model driven approach is sensitive to model defects, which can introduce biases and underestimated uncertainties.To resolve the issue of model defects in nuclear data (ND) evaluation, models the defect with a Gaussian process. This can reduce biases and give more correct covariances, including weakerenergy‐energy correlations. In the presented work, we continue the development of using Gaussian processes to treat model defects in ND evaluation, within a TENDL framework. The Gaussian processes are combined with the Levenberg‐Marquardt algorithm for non‐linear fitting, which reduces the need for a prior distribution. Further, it facilitates the transfer of knowledge to other nuclides by working in the parameter domain. First, synthetic data is used to validate the quality of both mean values and covariances provided by the method. After this, we fit TALYS parameters and a model defect correction to the 56Fe data in EXFOR.
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| 5. |
- Helgesson, Petter, 1986-, et al.
(författare)
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Uncertainty driven nuclear data evaluation including thermal (n,alpha) applied to Ni-59
- 2017
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Ingår i: Nuclear Data Sheets. - : Elsevier BV. - 0090-3752 .- 1095-9904. ; 145, s. 1-24
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a novel approach to the evaluation of nuclear data (ND), combining experimental data for thermalcross sections with resonance parameters and nuclear reaction modeling. The method involves sampling of variousuncertain parameters, in particular uncertain components in experimental setups, and provides extensive covarianceinformation, including consistent cross-channel correlations over the whole energy spectrum. The method is developed for, and applied to, Ni-59, but may be used as a whole, or in part, for other nuclides. Ni-59 is particularly interesting since a substantial amount of Ni-59 is produced in thermal nuclear reactors by neutron capture in Ni-58 and since it has a non-threshold (n,α) cross section. Therefore, Ni-59 gives a very important contribution to the helium production in stainless steel in a thermal reactor. However, current evaluated ND libraries contain old information for Ni-59, without any uncertainty information. The work includes a study of thermal cross section experiments and a novel combination of this experimental information, giving the full multivariate distribution of the thermal cross sections. In particular, the thermal (n,α) cross section is found to be (12.7 ± .7) b. This is consistent with, but yet different from, current established values. Further, the distribution of thermal cross sections is combined with reported resonance parameters, and with TENDL-2015 data, to provide full random ENDF files; all this is done in a novel way, keeping uncertainties and correlations in mind. The random files are also condensed into one single ENDF file with covariance information, which is now part ofa beta version of JEFF 3.3.Finally, the random ENDF files have been processed and used in an MCNP model to study the helium productionin stainless steel. The increase in the (n,α) rate due to Ni-59 compared to fresh stainless steel is found to be a factor of 5.2 at a certain time in the reactor vessel, with a relative uncertainty due to the Ni-59 data of 5.4 %.
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| 6. |
- Hernandez-Solis, Augusto, 1980-, et al.
(författare)
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Propagation of neutron-reaction uncertainties through multi-physics models of novel LWR's
- 2017
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Ingår i: ND 2016. - Les Ulis : EDP Sciences. - 9782759890200
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Konferensbidrag (refereegranskat)abstract
- The novel design of the renewable boiling water reactor (RBWR) allows a breeding ratio greater than unity and thus, it aims at providing for a self-sustained fuel cycle. The neutron reactions that compose the different microscopic cross-sections and angular distributions are uncertain, so when they are employed in the determination of the spatial distribution of the neutron flux in a nuclear reactor, a methodology should be employed to account for these associated uncertainties. In this work, the Total Monte Carlo (TMC) method is used to propagate the different neutron-reactions (as well as angular distributions) covariances that are part of the TENDL-2014 nuclear data (ND) library. The main objective is to propagate them through coupled neutronic and thermal-hydraulic models in order to assess the uncertainty of important safety parameters related to multi-physics, such as peak cladding temperature along the axial direction of an RBWR fuel assembly. The objective of this study is to quantify the impact that ND covariances of important nuclides such as U-235, U-238, Pu-239 and the thermal scattering of hydrogen in H2O have in the deterministic safety analysis of novel nuclear reactors designs.
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| 7. |
- Sjöstrand, Henrik, 1978-, et al.
(författare)
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Adjustment of nuclear data libraries using integral benchmarks
- 2017
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Integral experiments can be used to adjust ND-libraries and consequently the uncertainty response in important applications. . In this work we show how we can use integral experiments in a consistent way to adjust the TENDL library. A Bayesian method based on assigning weights to the different random files using a maximum likelihood function [1] is used. Emphasis is put on the problems that arise from multiple isotopes being present in a benchmark [2]. The challenges in using multiple integral experiments are also addressed, including the correlation between the different integral experiments.Methods on how to use the Total Monte Carlo method to select benchmarks for reactor application will further be discussed. In particular in respect to the so-called fast correlation coefficient and the fast-TMC method [3]
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| 8. |
- Sjöstrand, Henrik, 1978-, et al.
(författare)
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Choosing Nuclear Data evaluation techniques to obtain complete and motivated covariances
- 2017
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The quality of evaluated nuclear data and its covariances is affected by the choice of the evaluation algorithm. The evaluator can choose to evaluate in the observable domain or the parameter domain and choose to use a Monte Carlo- or deterministic techniques[1]. The evaluator can also choose to model potential model-defects using, e.g., Gaussian Processes [2]. In this contribution, the performance of different evaluation techniques is investigated by using synthetic data. Different options for how to model the model-defects are also discussed.In addition, the example of a new Ni-59 is presented where different co-variance driven evaluation techniques are combined to create a final file for JEFF-3.3 [3]. Keywords: Total Monte Carlo, Nuclear data evaluationAMS subject classifications. 62P35; 81V35; 62-07; References[1] P.Helgesson, D.Neudecker, H.Sjöstrand, M.Grosskopf, D.Smith, R.Capote; Assessment of Novel Techniques for Nuclear Data Evaluation, 16th International Symposium of Reactor Dosimetry (ISRD16) (2017)[2] G. Schnabel, Large Scale Bayesian Nuclear Data Evaluation with Consistent Model Defects, Ph.D. thesis, Technishe Universitätt Wien (2015)[3] P.Helgesson, H.Sjöstrand, D.Rochman; Uncertainty driven nuclear data evaluation including thermal (n,alpha): applied to Ni-59; Nuclear Data Sheets 145 (2017) 1–24
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| 9. |
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| 10. |
- Sjöstrand, Henrik, 1978-, et al.
(författare)
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Propagation of nuclear data uncertainties for fusion power measurements
- 2017
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Ingår i: ND 2016. - Les Ulis : EDP Sciences. - 9782759890200
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Konferensbidrag (refereegranskat)abstract
- Neutron measurements using neutron activation systems are an essential part of the diagnostic system at large fusion machines such as JET and ITER. Nuclear data is used to infer the neutron yield. Consequently, high-quality nuclear data is essential for the proper determination of the neutron yield and fusion power. However, uncertainties due to nuclear data are not fully taken into account in uncertainty analysis for neutron yield calibrations using activation foils. This paper investigates the neutron yield uncertainty due to nuclear data using the so-called Total Monte Carlo Method. The work is performed using a detailed MCNP model of the JET fusion machine; the uncertainties due to the cross-sections and angular distributions in JET structural materials, as well as the activation cross-sections in the activation foils, are analysed. It is found that a significant contribution to the neutron yield uncertainty can come from uncertainties in the nuclear data.
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