NUSS-RF: stochastic sampling-based tool for nuclear data sensitivity and uncertainty quantification

Zhu, Ting; Vasiliev, A.; Ferroukhi, H.; Pautz, Andreas; Tarantola, Stefano

doi:10.1080/00223131.2015.1040864

Cited by 13 publications

(10 citation statements)

References 12 publications

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“…The methods used by NUSS are similar to those of XSUSA (Cross Section Uncertainty and Sensitivity Analysis) [31,32], NUDUNA [34], SANDY [35], TMC [58], SAMPLER [59], SHARK-X [39], though the practical realization and application focus may differ. More in-depth comparison of NUSS with the alternative and similar tools can be found in [40,[47][48][49]. It is believed that the use of NUSS will allow performing more efficient CSE assessments in the future, finally resulting in optimized strategies for spent nuclear fuel operation in Switzerland [54,55].…”

Section: Subject Of the Given Studymentioning

confidence: 99%

“…However, in the case of consideration of arbitrary system responses, the way of computing the sensitivity coefficients in a production manner still needs further developments [15]. Alternatively, when, for instance, nuclear data stochastic sampling is performed with a tool like NUSS, it is straight forward to assess the Pearson correlation coefficient, r, for a pair of calculation sets, e.g., with different models and/or outputs [47,48,50,51,75] (note also that r 2 is equivalent to the "coefficient of determination" [76], which can be used to assess linear relation between the output and input values, as demonstrated in [31,75], but here the discussion is limited to only different systems output correlations in which case r 2 can be used to assess the proportion of variance in common between two outputs [76]; actually the NUSS tool has been further extended to the version NUSS-RF (where -RF stands for "Random balance design and Fourier amplitude sensitivity testing") to allow performance of Global Sensitivity Analysis, which concerns the first order global sensitivity indices instead of the local (one at a time) linear sensitivity coefficients [21,46,48,49], but for the context of the given paper we limit the discussion by using only the regular NUSS tool.…”

Section: Evaluation and Preliminary Assessment Of Correlationsmentioning

confidence: 99%

“…The uncertainty of the fast neutron flux in the control rod tip volume was estimated as slightly below 5%, which is not far from the value of 4% given here in Table 1 for the CR absorber materials. Actually, the sandwich rule operation and the stochastic sampling typically provide very close results for nuclear data uncertainty propagations in both criticality and dosimetry applications [37,39,40,[47][48][49]64,74,75].…”

Section: General Description and Assessmentmentioning

confidence: 99%

“…Naturally, the stochastic sampling simulations bring an alternative option for the correlation analysis, compared to the deterministic based assessments. Noticeably, the simple random sampling can be easily applied in a black box manner with any model/code for analysis of any system response of interest and with minimum associated approximations, while more advanced sampling techniques may be offered, e.g., for global sensitivity studies [45][46][47][48][49]. Nevertheless, the stochastic sampling based way for assessment of system similarities with respect to nuclear data effects is still novice and is also still mainly limited to k eff value analysis, as e.g., applied in [50,51].…”

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: Subject Of the Given Studymentioning

confidence: 99%

Section: Evaluation and Preliminary Assessment Of Correlationsmentioning

confidence: 99%

Section: General Description and Assessmentmentioning

confidence: 99%

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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“…The second refers to computational approaches that rely on the repeated random sampling of chosen parameters to obtain statistical outcomes of selected responses. Several codes have been developed to sample the nuclear data evaluations, including SANDY [6], SAMPLER [7], XSUSA [8], NUDUNA [9] and NUSS [10]. Semi-empirical model codes such as TALYS [11] are also used to generate sampled nuclear data evaluations based on the direct sampling of parameters used within physics models.…”

Section: Introductionmentioning

confidence: 99%

JEFF-3.3 covariance application to ICSBEP using SANDY and NDAST

2019

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Providing reliable estimates of the nuclear data contribution to the uncertainty of well-known integral benchmarks is fundamental to the validation and verification process for a nuclear data library. The Nuclear Energy Agency has produced and maintains the NDaST sensitivity tool, which integrates the DICE sensitivities and nuclear data covariances. This system has been used to rigorously and efficiently provide direct feedback to evaluators and streamline validation. For its future evolution and to identify high-priority development areas, NDaST is continuously compared against state-of-the-art codes that use different uncertainty propagation methodologies. In this work, NDaST was compared to the nuclear data sampling code SANDY for several ICSBEP criticality benchmarks using the JEFF-3.3 evaluated data. Despite excellent overall agreement for cross sections and fission neutron multiplcities, discrepancies due to processed covariance descriptions for angular distributions and prompt fission neutron spectra have identified areas where coordinated development of nuclear data covariance descriptions should be prioritised.

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Comparison of nuclear data uncertainties with other nuclear fuel cycle uncertainty sources

Skarbeli

Eusebio-Yebra

Romojaro

et al. 2021

Intl J of Energy Research

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Nuclear data uncertainties have proven to be crucial in many areas of nuclear science such as the reactor or isotopic calculations. Nevertheless, when it comes to fuel cycle simulations (which are nowadays including uncertainty analyses), their relevance is discussed. Although it has been reported that they have an impact on the inventories, it is not clear if their effect can be negligible when compared with uncertainties produced by other fuel cycle parameters and consequently, if nuclear data uncertainties have to be considered in the fuel cycle studies. In this work, this comparison has been addressed for an open fuel cycle. First, the nuclear data uncertainties contained in the covariance matrices have been propagated with a Monte Carlo methodology, which has been verified with an integral experiment. After that, the results have been compared with uncertainties typically taking part in fuel cycle simulations from two different perspectives: an unknown future fuel cycle with relatively large uncertainties and a past known fuel cycle with small uncertainties.Results show that even for the most conservative cases based on open fuel cycles using PWR technologies, the uncertainties due to nuclear data are larger or at least comparable to the fuel cycle uncertainties due to parameters. Henceforth, with the adoption of advanced nuclear fuel cycles using newer technologies, it is expected that nuclear data uncertainties will play even more relevance in fuel cycle analyses. With these results, it has been concluded that nuclear data cannot be disregarded in uncertainty assessments in fuel cycle studies.

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NUSS-RF: stochastic sampling-based tool for nuclear data sensitivity and uncertainty quantification

Cited by 13 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

JEFF-3.3 covariance application to ICSBEP using SANDY and NDAST

Comparison of nuclear data uncertainties with other nuclear fuel cycle uncertainty sources

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