Uncertainty Propagation in Monte Carlo Depletion Analysis

Park, Ho Jin; Shim, Hyung Jin; Kim, Chang Hyo

doi:10.13182/nse09-106

Cited by 28 publications

(15 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Statistical uncertainty occurs in the Monte-Carlo calculation, and the uncertainty is propagated by depletion calculation. However, it is generally known that uncertainty propagation has little impact on the core simulation result [13]. Section 2 of this paper describes MSBR.…”

Section: Introductionmentioning

confidence: 99%

Whole core analysis of molten salt breeder reactor with online fuel reprocessing

Park

Jeong

Lee

2015

Int. J. Energy Res.

View full text Add to dashboard Cite

SUMMARYThe simulation of whole core depletion and continuous reprocessing of a molten salt breeder reactor (MSBR) was performed. The MSBR model was built using MCNP6, and the depletion and reprocessing simulations were modeled using CINDER90 and a Python script. The Python script was introduced to implement online reprocessing of molten salt fuel and the feeding of new fertile material with 3-day depletion intervals during the simulations. The simulation started with the reference composition from the original Oak Ridge National Laboratory MSBR design. Equilibrium compositions were obtained from depletion and reprocessing simulations 7000 days worth of data. The MSBR whole core analysis was performed at the initial and equilibrium core conditions, for various reactor design parameters such as multiplication factors, neutron flux distributions, temperature coefficients, rod worths, and power distributions. The neutronic core characteristics were analyzed using a four-factor formula applied to the two zones of the core separately.

show abstract

Section: Introductionmentioning

confidence: 99%

Whole core analysis of molten salt breeder reactor with online fuel reprocessing

Park

Jeong

Lee

2015

Int. J. Energy Res.

View full text Add to dashboard Cite

show abstract

“…The superhistory algorithm applied for the collision history-based method is effective in reducing memory consumption. Future work will focus on extending the capabilities of computing sensitivity coefficients to bilinear ratios (Aufiero et al, 2015) and apply the generalized sensitivity coefficients to uncertainty propagation in depletion calculations (Park et al, 2011). …”

Section: Discussionmentioning

confidence: 99%

“…For example, setting R 2 ¼ 1 allows to calculate sensitivity coefficients of one-group cross section obtained by Monte Carlo transport calculations, and these coefficients can be further used to study uncertainty propagation (Park et al, 2011) in depletion calculations. In this work, methods are restricted to first order perturbation theory; therefore, the perturbation of generalized response, dR, caused by perturbations of cross sections can be expressed as…”

Section: Generalized Sensitivity Coefficientsmentioning

confidence: 99%

Development of sensitivity analysis capabilities of generalized responses to nuclear data in Monte Carlo code RMC

Qiu

Aufiero

Wang

et al. 2016

Annals of Nuclear Energy

View full text Add to dashboard Cite

a b s t r a c tCapabilities for nuclear data sensitivity and uncertainty analysis have been recently implemented in numerous continuous-energy Monte Carlo codes, including the Reactor Monte Carlo (RMC) code. Previous work developed the capability for RMC of computing sensitivity coefficients of the effective multiplication factor and related uncertainties, due to nuclear data. In this work, such capability was extended to generalized responses in the form of ratios of linear response functions of the forward flux based on the collision history-based approach as implemented in SERPENT2. The superhistory algorithm was also adopted in RMC to reduce memory consumption for generalized sensitivity calculations. These new capabilities of RMC were verified by comparing results of TSUNAMI-1D in SCALE6.1 code package, and SERPENT2 through Jezebel, Flattop and the UAM TMI PWR pin cell benchmark problems.

show abstract

“…A group out of Seoul National University very thoroughly worked out the theory for propagating explicit sensitivities through a time-dependent Monte Carlo burnup analysis [Park et al, 2011]. The extension of this from explicit to implicit sensitivities would be of enormous benefit.…”

Section: Literature Reviewmentioning

confidence: 99%

Adjoint-Based Uncertainty Quantification with MCNP

Seifried¹

2011

View full text Add to dashboard Cite

This work serves to quantify the instantaneous uncertainties in neutron transport simulations born from nuclear data and statistical counting uncertainties. Perturbation and adjoint theories are used to derive implicit sensitivity expressions. These expressions are transformed into forms that are convenient for construction with MCNP6, creating the ability to perform adjoint-based uncertainty quantification with MCNP6. These new tools are exercised on the depleted-uranium hybrid LIFE blanket, quantifying its sensitivities and uncertainties to important figures of merit. Overall, these uncertainty estimates are small (< 2%). Having quantified the sensitivities and uncertainties, physical understanding of the system is gained and some confidence in the simulation is acquired.Professor Per F Peterson, Chair Date 1 AcknowledgementsLila Chase for assisting in compiling CRSRD, providing MCNP6 source code and compile advice, and squashing a strange bug in MAKXSF. John Wagner for providing the source for his code CRSRD.

show abstract

Uncertainty Propagation in Monte Carlo Depletion Analysis

Cited by 28 publications

References 6 publications

Whole core analysis of molten salt breeder reactor with online fuel reprocessing

Whole core analysis of molten salt breeder reactor with online fuel reprocessing

Development of sensitivity analysis capabilities of generalized responses to nuclear data in Monte Carlo code RMC

Adjoint-Based Uncertainty Quantification with MCNP

Contact Info

Product

Resources

About