Cross section covariances: a user perspective

Palmiotti, G.; Salvatores, M.

doi:10.1051/epjn/2018021

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Reduced confidence in the fission cross section can have real consequences for the allowed power level and operating regimes for these systems. A study conducted by SG46 (126) found that many individual reactions were modified by several percent from ENDF/B-VII.1 to ENDF/B-VIII.0. Figure 8 shows some examples.…”

Section: Nuclear Energymentioning

confidence: 99%

Our Future Nuclear Data Needs

Bernstein

Brown

Koning

et al. 2019

Annu. Rev. Nucl. Part. Sci.

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A well-established knowledge of nuclear phenomena including fission, reaction cross sections, and structure/decay properties is critical for applications ranging from the design of new reactors to nonproliferation to the production of radioisotopes for the diagnosis and treatment of illness. However, the lack of a well-quantified, predictive theoretical capability means that most nuclear observables must be measured directly and used to calibrate empirical models, which in turn provide the data needed for these applications. In many cases, either there is a lack of data needed to guide the models or the results of the different measurements are discrepant, leading to the development of evaluation methodologies to provide recommended values and uncertainties. In this review, we describe the nuclear data evaluation process and the international community that carries it out. We then discuss new measurements and improved theory and/or modeling needed to address future challenges in applied nuclear science.

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Section: Nuclear Energymentioning

confidence: 99%

Our Future Nuclear Data Needs

Bernstein

Brown

Koning

et al. 2019

Annu. Rev. Nucl. Part. Sci.

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“…The next essential ingredient of DA À prior uncertainties À is crucial for any Bayesian inference technique [1,19,[35][36][37].…”

Section: Information Content Of the Posterior Bias And Uncertaintiesmentioning

confidence: 99%

“…In fact, DA integrate somehow an information brought with the used IEs onto corrected nuclear parameters and their uncertainties [35]. In the years of DA practical implementations an intensive discussion arisen on the question how to interpret an appearance in posterior CND cross-covariance members have not been in prior ones [36]. It was found that the crosscovariance members in a posterior CND always contain the traces of IEs data [1,19,36,37], characterizing in certain extent an efficiency of the adjustment [19].…”

Section: Information Content Of the Posterior Bias And Uncertaintiesmentioning

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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“…To quantify effectiveness of integral data available for us to test evaluated nuclear data, we propose methods to quantify independency of a set of integral data, to choose a minimum set of proper integral data from a huge amount of integral data, and to quantify possibility of validation of each nuclear data with a given set of integral data. Furthermore, this concept is utilized to interpret the nuclear data compensation effect [6], which has been recently raised in the community of nuclear data.…”

Section: Introductionmentioning

confidence: 99%