Influence of the Correlations of Experimental Uncertainties on Criticality Prediction

Ivanova, Tatiana; Nikolaev, M.N.; Raskach, K. F.; Rozhikhin, Evgeny; Tsiboulia, A. M.

doi:10.13182/nse03-27

Cited by 14 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be however mentioned that conventionally the validation C/E results are the subject of statistical evaluations, which allow quantifying the calculation methodology accuracy with certain confidence (e.g., by deriving the average C/E bias, its uncertainty and/or bounding C/E tolerance value [83]). It is also known that the existence of correlations between benchmark models involved in the methodology validation process (even if originated from the common sensitivities to nuclear data) should be taken into account to prevent errors in estimation of the calculation bias [84], and such use of the correlation coefficients is also foreseen at PSI [54]. If a weighting procedure is applied to the C/E values processing (e.g., to derive a weighted sample mean and variance), it should also take into account the experimental uncertainties CM as the weight components.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

View full text Add to dashboard Cite

show abstract

“…in [1]. In recent years, several authors discussed the fact that depending on the application case and the choice of experiments, the effect of correlated experimental data on the determination of the bias, its uncertainty, and the resulting safety margins has to be considered [2,3,4,5,6,7,8,9,10]. The questions arising in the field of determination and handling of integral experimental covariance matrices in the process of code validation are also discussed in the Expert Group on Uncertainty Analysis for Criticality Safety Assessment (UACSA), a sub-group of the Working Party on Nuclear Criticality Safety (WPNCS) of the Nuclear Energy Agency (NEA) within the Organization for Economic Co-operation and Development (OECD).…”

Section: Introductionmentioning

confidence: 99%

Modeling of critical experiments and its impact on integral covariance matrices and correlation coefficients

Peters

Sommer

Stuke

2016

Annals of Nuclear Energy

View full text Add to dashboard Cite

In this manuscript we study the modeling of experimental data and its impact on the resulting integral experimental covariance and correlation matrices. By investigating a set of three low enriched and water moderated UO 2 fuel rod arrays we found that modeling the same set of data with different, yet reasonable assumptions concerning the fuel rod composition and its geometric properties leads to significantly different covariance matrices or correlation coefficients. Following a Monte Carlo sampling approach, we show for nine different modeling assumptions the corresponding correlation coefficients and sensitivity profiles for each pair of the effective neutron multiplication factor k eff . Within the 95% confidence interval the correlation coefficients vary from 0 to 1, depending on the modeling assumptions. Our findings show that the choice of modeling can have a huge impact on integral experimental covariance matrices. When the latter are used in a validation procedure to derive a bias, this procedure can be affected by the choice of modeling assumptions, too. The correct consideration of correlated data seems to be inevitable if the experimental data in a validation procedure is limited or one cannot rely on a sufficient number of uncorrelated data sets, e.g. from different laboratories using different setups etc.

show abstract

“…The assignment of the experimental correlations between benchmarks might be useful to assess the importance of selecting/removing experiments in the benchmarking activities [38]. The implementation in DICE of an extended set of correlations in ICSBEP experiments will lead to a better understanding of the real performance of nuclear data libraries.…”

Section: Resultsmentioning

confidence: 99%

Benchmarking and validation activities within JEFF project

et al. 2017

View full text Add to dashboard Cite

The challenge for any nuclear data evaluation project is to periodically release a revised, fully consistent and complete library, with all needed data and covariances, and ensure that it is robust and reliable for a variety of applications. Within an evaluation effort, benchmarking activities play an important role in validating proposed libraries. The Joint Evaluated Fission and Fusion (JEFF) Project aims to provide such a nuclear data library, and thus, requires a coherent and efficient benchmarking process. The aim of this paper is to present the activities carried out by the new JEFF Benchmarking and Validation Working Group, and to describe the role of the NEA Data Bank in this context.

show abstract

Influence of the Correlations of Experimental Uncertainties on Criticality Prediction

Cited by 14 publications

References 1 publication

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

Modeling of critical experiments and its impact on integral covariance matrices and correlation coefficients

Benchmarking and validation activities within JEFF project

Contact Info

Product

Resources

About