Use of International Criticality Safety Benchmark Evaluation Project Data for Validation of the ABBN Cross-Section Library with the MMK-KENO Code

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

doi:10.13182/nse03-35

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…The sample standard deviation (STD) value of 321 pcm is almost twice lower as compared to the σ ND results. Without further investigations, this situation can be tentatively explained by two main reasons: at first the STD includes only parts of the σ ND value and at second the use of general purpose ND uncertainties (not adjusted for a particular application type, like CSE for LWR) is expected to be over-conservative and therefore the techniques for ND/CM (or calculation bias and uncertainty) adjustments are in development and use [5,7,14,[22][23][24][25]52]. Typical level of the Monte Carlo statistical precision in the individual benchmark calculations is below 20 pcm, however for the NUSS/MCNP(X) calculations the statistical uncertainty of the individual runs was about~70-90 pcm to reduce the computation time.…”

Section: General Description and Assessmentmentioning

confidence: 99%

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 Assessmentmentioning

confidence: 99%

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

et al. 2016

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“…From the very beginning, nuclear technological science intended all concepts and statements to have a solid basis in reality. In all domains of nuclear engineering from design to safety regulation, it seems crucial having access to objective observations, including operational background, basic and dedicated Integral Experimental (IE) programs [39][40][41][42][43][44].…”

Section: Best Practice In Data Assimilation Worldwidementioning

confidence: 99%

“…Because of many reasons, the number of representative integral experiments was very limited while a few available give a discrepancy on several percent of k eff , that correspond to one-third or, even, onehalf of critical mass. Later, the dedicated parametric experimental program was established with under-moderated 240 Pu containing critical assemblies [19,25,41,42]. As a result, the experiments confirmed an existence of the issue while used, then, DA helped characterizing specific safety margins by posterior bias and uncertainties [19] remaining, though, unclear which namely nuclide-reaction led to these discrepancies.…”

Section: Rd Application: Step Toward Ontological Uncertainty Treatmentmentioning

confidence: 99%

Nuclear data assimilation, scientific basis and current status

Ivanov

Saint-Jean

Sobes

2021

EPJ Nuclear Sci. Technol.

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The use of Data Assimilation methodologies, known also as a data adjustment, liaises the results of theoretical and experimental studies improving an accuracy of simulation models and giving a confidence to designers and regulation bodies. From the mathematical point of view, it approaches an optimized fit to experimental data revealing unknown causes by known consequences that would be crucial for data calibration and validation. Data assimilation adds value in a ND evaluation process, adjusting nuclear data to particular application providing so-called optimized design-oriented library, calibrating nuclear data involving IEs since all theories and differential experiments provide the only relative values, and providing an evidence-based background for validation of Nuclear data libraries substantiating the UQ process. Similarly, it valorizes experimental data and the experiments, as such involving them in a scientific turnover extracting essential information inherently contained in legacy and newly set up experiments, and prioritizing dedicated basic experimental programs. Given that a number of popular algorithms, including deterministic like Generalized Linear Least Square methodology and stochastic ones like Backward and Hierarchic or Total Monte-Carlo, Hierarchic Monte-Carlo, etc., being different in terms of particular numerical formalism are, though, commonly grounded on the Bayesian theoretical basis. They demonstrated sufficient maturity, providing optimized design-oriented data libraries or evidence-based backgrounds for a science-driven validation of general-purpose libraries in a wide range of practical applications.

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