Nuclear data uncertainty for criticality-safety: Monte Carlo vs. linear perturbation

Rochman, D.; Vasiliev, A.; Ferroukhi, H.; Zhu, Ting; Marck, S. C. van der; Koning, A. J.

doi:10.1016/j.anucene.2016.01.042

Cited by 32 publications

(33 citation statements)

References 12 publications

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“…Nevertheless, it can be mentioned that in certain criticality calculations noticeable differences were observed between the nominal values of the calculation outputs obtained with nominal nuclear data libraries versus the sample mean values, obtained with randomly sampled libraries together with the sample skewness different from zero [38]. Similar observations are now under analysis for the dosimetry calculations.…”

Section: General Description and Assessmentsupporting

confidence: 53%

“…With the modern computation capabilities the stochastic sampling approach became also attractive for uncertainty quantifications and in the last decade a number of computation tools has been developed over the world towards the propagation of nuclear data uncertainties in neutronic calculations, ranging from tools for "simple" criticality stationary calculations, up to time-dependent calculations (including isotopic kinetics/fuel depletion and reactor dynamics/transient modeling in the vocabulary of nuclear engineering community) [31][32][33][34][35][36][37][38][39][40][41][42][43][44]. When it is possible, the stochastic and deterministic methods are cross-verified against each other [33,37,39] or even work in combination to provide the best capabilities for calculation simulations and results analysis [44].…”

Section: Introductionmentioning

confidence: 99%

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Exploring Stochastic Sampling in Nuclear Data Uncertainties Assessment for Reactor Physics Applications and Validation Studies

et al. 2016

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Abstract:The quantification of uncertainties of various calculation results, caused by the uncertainties associated with the input nuclear data, is a common task in nuclear reactor physics applications. Modern computation resources and improved knowledge on nuclear data allow nowadays to significantly advance the capabilities for practical investigations. Stochastic sampling is the method which has received recently a high momentum for its use and exploration in the domain of reactor design and safety analysis. An application of a stochastic sampling based tool towards nuclear reactor dosimetry studies is considered in the given paper with certain exemplary test evaluations. The stochastic sampling not only allows the input nuclear data uncertainties propagation through the calculations, but also an associated correlation analysis performance with no additional computation costs and for any parameters of interest can be done. Thus, an example of assessment of the Pearson correlation coefficients for several models, used in practical validation studies, is shown here. As a next step, the analysis of the obtained information is proposed for discussion, with focus on the systems similarities assessment. The benefits of the employed method and tools with respect to practical reactor dosimetry studies are consequently outlined.

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Section: General Description and Assessmentsupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Exploring Stochastic Sampling in Nuclear Data Uncertainties Assessment for Reactor Physics Applications and Validation Studies

et al. 2016

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“…They have already been used and characterized in previous work, see for instance references [2,3,26]. All possible nuclear data are varied, such as cross sections, angular distributions, emitted spectra and multiplicities.…”

Section: Correlation From Integral Benchmarksmentioning

confidence: 99%

“…Many simulation tools are capable of using such matrices to propagate nuclear data uncertainties on final quantities, with either perturbation theories [1][2][3][4][5][6], or Monte Carlo sampling [3,[7][8][9][10][11][12][13]. These results can for instance be used for the review procedure of new facilities, or during the safety assessment of new reactor core designs [14].…”

Section: Introductionmentioning

confidence: 99%

Correlationν̅_p − σ − χin the fast neutron range via integral information

Rochman

Bauge

Vasiliev

et al. 2017

EPJ Nuclear Sci. Technol.

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Abstract. This paper presents a Bayesian approach based on integral experiments to create correlations which do not appear with differential data. Some quantities such as the fission cross section (s), neutron multiplicity (n p ), neutron spectra (x), etc. are usually neither modeled together nor measured in coincidence, thus there is no correlation matrices in evaluated nuclear data libraries. One can nevertheless use the information from integral experiments such as fast criticality-safety benchmarks to correlate such quantities for possible inclusion in nuclear data libraries. A simple Bayesian set of equations is presented with random nuclear data, similarly to the usual methods applied with differential data. An example for 239 Pu is proposed.

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“…The term uncertainty as used in this work is represented by an estimated one standard deviation of the distribution under consideration. The TMC method has been presented extensively in several references: [3,4,19,20,21] as well as applied to many applications: [10,22,23,24,25,26,27,28,29]. The first step in our updating scheme involves the identification of model input parameters (and the corresponding parameter widths) that play significant roles in defining or characterizing the reaction observables of interest.…”

Section: Updating Schemementioning

confidence: 99%

Bayesian updating for data adjustments and multi-level uncertainty propagation within Total Monte Carlo

Alhassan

Rochman

Sjöstrand

et al. 2020

Annals of Nuclear Energy

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In this work, a method is proposed for combining differential and integral benchmark experimental data within a Bayesian framework for nuclear data adjustments and multi-level uncertainty propagation using the Total Monte Carlo method. First, input parameters to basic nuclear physics models implemented within the state of the art nuclear reactions code, TALYS, were sampled from uniform distributions and randomly varied to produce a large set of random nuclear data files. Next, a probabilistic data assimilation was carried out by computing the likelihood function for each random nuclear data file based first on only differential experimental data (1st update) and then on integral benchmark data (2nd update). The individual likelihood functions from the two updates were then combined into a global likelihood function which was used for the selection of the final 'best' file. The proposed method has been applied for the adjustment of 208 Pb in the fast neutron energy region below 20 MeV. The 'best' file from the adjustments was compared with available experimental data from the EXFOR database as well as evaluations from the major nuclear data libraries and found to compare favourably.

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Nuclear data uncertainty for criticality-safety: Monte Carlo vs. linear perturbation

Cited by 32 publications

References 12 publications

Exploring Stochastic Sampling in Nuclear Data Uncertainties Assessment for Reactor Physics Applications and Validation Studies

Exploring Stochastic Sampling in Nuclear Data Uncertainties Assessment for Reactor Physics Applications and Validation Studies

Correlationν̅_p − σ − χin the fast neutron range via integral information

Bayesian updating for data adjustments and multi-level uncertainty propagation within Total Monte Carlo

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Nuclear data uncertainty for criticality-safety: Monte Carlo vs. linear perturbation

Cited by 32 publications

References 12 publications

Exploring Stochastic Sampling in Nuclear Data Uncertainties Assessment for Reactor Physics Applications and Validation Studies

Exploring Stochastic Sampling in Nuclear Data Uncertainties Assessment for Reactor Physics Applications and Validation Studies

Correlationν̅p − σ − χin the fast neutron range via integral information

Bayesian updating for data adjustments and multi-level uncertainty propagation within Total Monte Carlo

Contact Info

Product

Resources

About

Correlationν̅_p − σ − χin the fast neutron range via integral information