Analysis of dynamic reactivity by Monte Carlo methods: The impact of nuclear data

Zoia, Andrea; Jouanne, C.; Siréta, P.; Leconte, Pierre; Braoudakis, G.; Wong, Lindee

doi:10.1016/j.anucene.2017.06.012

Cited by 9 publications

(5 citation statements)

References 38 publications

(56 reference statements)

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“…Since no new experimental data was produced or became available after the work of Subgroup 6 there was in principle no justification to replace the DN data. In the process of developing JEFF- 3.3, it was demonstrated that for 235 U the DN data taken from ENDF/B-VII.0 lead to a wrong evaluation of the average DN precursor's half-life [127]. In addition, IPEN and SPERT benchmarks both concluded that JEFF-3.1.1 data provides the best C/E agreement on the dynamic reactivity from the Inhour equation, due to the better evaluation of the a i and λ i data.…”

Section: 3])mentioning

confidence: 99%

The joint evaluated fission and fusion nuclear data library, JEFF-3.3

et al. 2020

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The joint evaluated fission and fusion nuclear data library 3.3 is described. New evaluations for neutroninduced interactions with the major actinides 235 U, 238 U and 239 Pu, on 241 Am and 23 Na, 59 Ni, Cr, Cu, Zr, Cd, Hf, W, Au, Pb and Bi are presented. It includes new fission yields, prompt fission neutron spectra and average number of neutrons per fission. In addition, new data for radioactive decay, thermal neutron scattering, gamma-ray emission, neutron activation, delayed neutrons and displacement damage are presented. JEFF-3.3 was complemented by files from the TENDL project. The libraries for photon, proton, deuteron, triton, helion and alpha-particle induced reactions are from TENDL-2017. The demands for uncertainty quantification in modeling led to many new covariance data for the evaluations. A comparison between results from model calculations using the JEFF-3.3 library and those from benchmark experiments for criticality, delayed neutron yields, shielding and decay heat, reveals that JEFF-3.3 performes very well for a wide range of nuclear technology applications, in particular nuclear energy.

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Section: 3])mentioning

confidence: 99%

The joint evaluated fission and fusion nuclear data library, JEFF-3.3

et al. 2020

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“…With the advent of the era of big data, the economic industrial chain is becoming more and more complex, and the characteristics of economic cycle fluctuation will be reflected in the data changes of some economic indicators [13][14][15]. In the economic fluctuation impact index system, according to the time sequence differences of indicators, they are usually divided into leading indicators, consistent indicators, and lagging indicators [16][17][18][19]. e leading indicator refers to the indicator that the occurrence time of its cycle turning point is stably ahead of the corresponding turning point of the overall market cycle and changes ahead of the overall market fluctuation in time.…”

Section: Index System Of Influencing Factors Of Economicmentioning

confidence: 99%

Analyzing the Influencing Factors of Economic Fluctuations in the Era of Big Data

Xiong

2022

Scientific Programming

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In view of the problems of poor correlation and poor calculation accuracy of the existing factors affecting economic fluctuations, this paper designs a new method for analyzing the factors affecting economic fluctuations based on the characteristics of the era of big data. First, after determining the factors influencing the economic fluctuations in the era of big data, the calculation method of key impact indicators is clarified. Then, the entropy law method is used to assign a weight value to the index, and the set is formed after the index is gathered. Then, the EM algorithm is used to assign the expected value to the data in the set. Finally, the set of influencing factors of the economic fluctuations is taken as input information, and the correlation evaluation model of influencing factors of economic fluctuations is constructed by the Monte Carlo method to complete the analysis of the influencing factors. The experimental results show that the correlation coefficient of influencing factors of economic fluctuations in the era of big data analyzed in this paper is high and the calculation accuracy of influencing factors is high, which proves the feasibility of this method.

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“…A preliminary comparison for this facility between fundamental α-eigenp time dependent calculations has been carried out in [26]. Moreover, the kinetics ters of the CROCUS core for the k-eigenvalue formulation has been previously co in [4] and the associated reactivity was examined in [27]. In the following, we w tigate the behaviour of the fundamental forward and adjoint modes of the k and lations for this core, and we will then examine the impact of their respective shap kinetics parameters and on the reactivity.…”

Section: Analysis Of the Crocus Benchmarkmentioning

confidence: 99%

“…The fundamental eigenmodes will puted by using TRIPOLI-4 ® over 111 energy meshes and along 14 fuel pin positio the core center to the outer region, denoted by the red rectangular regions in Figu number of cycles and the particles per cycle simulated for these configurations a in Table 16 for the forward simulations and in Table 17 for the adjoint simulation A preliminary comparison for this facility between fundamental α-eigenpairs and time dependent calculations has been carried out in [26]. Moreover, the kinetics parameters of the CROCUS core for the k-eigenvalue formulation has been previously computed in [4] and the associated reactivity was examined in [27]. In the following, we will investigate the behaviour of the fundamental forward and adjoint modes of the k and α formulations for this core, and we will then examine the impact of their respective shapes on the kinetics parameters and on the reactivity.…”

Section: Analysis Of the Crocus Benchmarkmentioning

confidence: 99%

Comparison of Direct and Adjoint k and α-Eigenfunctions

Vitali

Chevallier

Jinaphanh

et al. 2021

JNE

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Modal expansions based on k-eigenvalues and α-eigenvalues are commonly used in order to investigate the reactor behaviour, each with a distinct point of view: the former is related to fission generations, whereas the latter is related to time. Well-known Monte Carlo methods exist to compute the direct k or α fundamental eigenmodes, based on variants of the power iteration. The possibility of computing adjoint eigenfunctions in continuous-energy transport has been recently implemented and tested in the development version of TRIPOLI-4®, using a modified version of the Iterated Fission Probability (IFP) method for the adjoint α calculation. In this work we present a preliminary comparison of direct and adjoint k and α eigenmodes by Monte Carlo methods, for small deviations from criticality. When the reactor is exactly critical, i.e., for k0 = 1 or equivalently α0 = 0, the fundamental modes of both eigenfunction bases coincide, as expected on physical grounds. However, for non-critical systems the fundamental k and α eigenmodes show significant discrepancies.

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Analysis of dynamic reactivity by Monte Carlo methods: The impact of nuclear data

Cited by 9 publications

References 38 publications

The joint evaluated fission and fusion nuclear data library, JEFF-3.3

The joint evaluated fission and fusion nuclear data library, JEFF-3.3

Analyzing the Influencing Factors of Economic Fluctuations in the Era of Big Data

Comparison of Direct and Adjoint k and α-Eigenfunctions

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