Nuclide Composition Benchmark Data Set for Verifying Burnup Codes on Spent Light Water Reactor Fuels

Nakahara, Y.; Suyama, Kenya; Inagawa, Jun; Nagaishi, Ryuji; Kurosawa, Setsumi; Kohno, Nobuaki; Onuki, Mamoru; Mochizuki, Hiroki

doi:10.13182/nt02-2

Cited by 44 publications

(47 citation statements)

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“…Nakahara et al 22) reported C/Es as 1.10 for Table 7 Results of interpolation analysis of SRAC and comparison (C/E) with measurements 15) for heavy metal isotopic inventories of SF98 samples of the 8Â8-2 assembly (Unit: g/TIHM) (average C/E: 0.97), and 242 Pu (average C/E: 0.92) exceed the measurement uncertainties and also reported by other studies on the different fuel samples. 4,5,7,9,11,12,22) On the other hand, Fujiwara et al 6) reported C/E larger than 1.0 for 238 Pu.…”

Section: Analyses Of Pellet Average Data (1) Analysis Methodsmentioning

confidence: 99%

“…237 U(-)! 237 Np, 7) the improvement of the C/E Table 9 Results of interpolation analysis of SRAC and comparison (C/E) with measurements 15) for heavy metal isotopic inventories of SF99 samples of the 8Â8-2 assembly (Unit: g/TIHM) of 236 U by using the latest nuclear libraries such as JENDL-3.3 and JEFF 3.0 leads to improving the C/E of 237 Np. 9,23) Among the minor actinides, inventories of many isotopes except 241 Am are underestimated by the analysis as shown in Fig.…”

Section: Analyses Of Pellet Average Data (1) Analysis Methodsmentioning

confidence: 99%

“…A void axial distribution of the assembly is also available in the reference 15) as a standard one published in the Application for Permission for the Installation of the plant: however, it does not include the time-dependent history. On the other hand, the node average values of the in-channel void fractions and burnups of the 8Â8-4 fuel assemblies were provided by the plant operator for the present study as the records of a core monitoring system for the specific axial nodes of the assemblies, which include the destructive fuel samples.…”

Section: â8 Fuel Assemblies and Their Irradiation Historiesmentioning

confidence: 99%

“…The chemical assay of the pellet samples of the BWR 8Â8-2 fuel assembly was performed at the Japan Atomic Energy Agency 15) and isotopic dilution mass spectrometry (IDM) was applied to Nd, U, Pu, Sm isotopes with the spikes in the forms of 150 Nd and 233 U. The measurement uncertainties 15) are (1) 15) Inventory data are given in mass ratio to the initial heavy metal for the end of the irradiation of the assembly (g/TIHM).…”

Section: â8 Fuel Assemblies and Their Irradiation Historiesmentioning

confidence: 99%

“…The measurement uncertainties 15) are (1) 15) Inventory data are given in mass ratio to the initial heavy metal for the end of the irradiation of the assembly (g/TIHM). The atomic percent fission (%FIMA: fissions per initial metal atom in %) of the samples was determined by the Nd-148 method 18) with the inventory data of U, Pu, Am, Cm, and 148 Nd and the evaluated effective fission yield of 148 Nd, and a conversion factor of 9.6 was used to obtain the burnups in a unit of GWd/t from %FIMA.…”

Section: â8 Fuel Assemblies and Their Irradiation Historiesmentioning

confidence: 99%

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Nuclear Analysis of PIE Data of Irradiated BWR 8×8-2 and 8×8-4 UO₂Fuel Assemblies

Yamamoto

Yamamoto²

2008

Journal of Nuclear Science and Technology

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The measured pellet average inventories of actinides and fission product nuclides on the fifteen samples taken from a three-cycle irradiation BWR 8Â8-2 UO 2 assembly were compared with those of assembly burnup calculations using a collision probability method (SRAC) with the JENDL-3.2 nuclear data library. Am. The major FP nuclides contributing to neutron absorption such as Nd, Cs, Eu, and Sm are almost well reproduced by the present calculations. The measured pellet average burnups and major actinide inventories on the twenty samples taken from four BWR 8Â8-4 UO 2 assemblies were also compared with those of the burnup calculations using SRAC and a continuous energy Monte Carlo burnup analysis code (MVP-BURN). Most of the calculated pellet average burnups of both codes agree with the measurements within the range of AE10%. The general trends of the measured pellet radial distributions of actinide and FP nuclides on six samples of the 8Â8-4 UO 2 assemblies were well reproduced by the burnup calculations of MVP-BURN.

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Section: Analyses Of Pellet Average Data (1) Analysis Methodsmentioning

confidence: 99%

Section: Analyses Of Pellet Average Data (1) Analysis Methodsmentioning

confidence: 99%

Section: â8 Fuel Assemblies and Their Irradiation Historiesmentioning

confidence: 99%

Section: â8 Fuel Assemblies and Their Irradiation Historiesmentioning

confidence: 99%

Section: â8 Fuel Assemblies and Their Irradiation Historiesmentioning

confidence: 99%

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Nuclear Analysis of PIE Data of Irradiated BWR 8×8-2 and 8×8-4 UO₂Fuel Assemblies

Yamamoto

Yamamoto²

2008

Journal of Nuclear Science and Technology

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Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling

Radaideh

Kozłowski

2019

Int J Energy Res

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Summary A novel and modern framework for energy modeling is developed in this paper with a focus on nuclear energy modeling and simulation. The framework combines multiphysics simulations and real data, with validation by uncertainty quantification tasks and facilitation by machine and deep learning methods. The hybrid framework is built on the basis of a wide range of physical models, real data, mathematical and statistical methods, and artificial intelligence techniques. The framework is demonstrated in different applications, including quantifying uncertainties in computer simulations, multiphysics coupling, analysis of variance using machine learning surrogate models, deep learning of time series phenomena, and propagating parametric uncertainties of nuclear data. The applications demonstrated are oriented to nuclear engineering simulations, even though majority of the methods are applicable to other energy sources (eg, renewable). Efficient utilization of this framework is expected to yield a much better understanding of the physical phenomena analyzed as well as an improvement in the performance of the energy design/model under construction.

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Inventory calculation and nuclear data uncertainty propagation on light water reactor fuel using ALEPH-2 and SCALE 6.2

Fiorito

Piedra

Cabellos

et al. 2015

Annals of Nuclear Energy

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Nuclide Composition Benchmark Data Set for Verifying Burnup Codes on Spent Light Water Reactor Fuels

Cited by 44 publications

References 1 publication

Nuclear Analysis of PIE Data of Irradiated BWR 8×8-2 and 8×8-4 UO₂Fuel Assemblies

Nuclear Analysis of PIE Data of Irradiated BWR 8×8-2 and 8×8-4 UO₂Fuel Assemblies

Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling

Inventory calculation and nuclear data uncertainty propagation on light water reactor fuel using ALEPH-2 and SCALE 6.2

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Nuclide Composition Benchmark Data Set for Verifying Burnup Codes on Spent Light Water Reactor Fuels

Cited by 44 publications

References 1 publication

Nuclear Analysis of PIE Data of Irradiated BWR 8×8-2 and 8×8-4 UO2Fuel Assemblies

Nuclear Analysis of PIE Data of Irradiated BWR 8×8-2 and 8×8-4 UO2Fuel Assemblies

Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling

Inventory calculation and nuclear data uncertainty propagation on light water reactor fuel using ALEPH-2 and SCALE 6.2

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Product

Resources

About

Nuclear Analysis of PIE Data of Irradiated BWR 8×8-2 and 8×8-4 UO₂Fuel Assemblies

Nuclear Analysis of PIE Data of Irradiated BWR 8×8-2 and 8×8-4 UO₂Fuel Assemblies