MCNPX Monte Carlo burnup simulations of the isotope correlation experiments in the NPP Obrigheim (original) (raw)
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Annals of Nuclear Energy, 2021
The Monte Carlo N-particle radiation transport code (MCNP) is used widely in nuclear fuel burnup simulation (FBS). In the FBS, MCNP predicts various neutron reaction rates and the associated stochastic relative error (SRE). The SRE is due to the nature of the statistical methods used in MCNP. These SREs for neutron reaction rates and fluxes are reported in the MCNP output for each FBS time-step. However, MCNP does not report SREs for its predictions of isotope concentrations. A new methodology to compute the SRE for the MCNP-predicted isotope concentrations is developed, which uses MCNP-computed neutron reaction rates and the associated SREs. The effectiveness of the methodology is verified for a pressurized water reactor FBS and is found to be satisfactory. The SRE computation for isotope concentrations in irradiated fuel has applications in nuclear science and engineering.
Journal of Nuclear Science and Technology, 2004
We performed a numerical comparative analysis of the burnup capability of the Gas Turbine-Modular Helium Reactor (GT-MHR) by the Monte Carlo Continuous Energy Burnup Code (MCB). The MCB code is an extension of MCNP that includes the burnup implementation; it adopts continuous energy cross sections and it evaluates the transmutation trajectories for over 2,400 decaying nuclides. We equipped the MCB code with three different nuclear data libraries: JENDL-3.2, JEF-2.2 and ENDF/B-6.8 processed for temperatures from 300 to 1,800 K. The GT-MHR model studied in this paper is fueled by actinides coming from the Light Water Reactors waste, converted into two different types of fuel: Driver Fuel and Transmutation Fuel. The Driver Fuel supplies the fissile nuclides needed to maintain the criticality of the reactor, whereas the Transmutation Fuel depletes non-fissile isotopes and controls reactivity excess. We set the refueling and shuffling period to one year and the in-core fuel residency time to three years. The comparative analysis of the MCB code consists of accuracy and precision studies. In the accuracy studies, we performed the burnup calculation with different nuclear data libraries during the year at which the refueling and shuffling schedule set the equilibrium of the fuel composition. In the precision studies, we repeated the same simulations 20 times with a different pseudorandom number stride and the same nuclear data library.
Nuclear Engineering and Technology, 2021
This work deals with the assessment of the burnup capabilities of the Serpent Monte Carlo code to predict spent nuclear fuel (SNF) isotopic concentrations for low-enriched uranium (LEU) fuel at different burnup levels up to 47 MWd/kgU. The irradiation of six UO 2 experimental samples in three different VVER-1000 reactor units has been simulated and the predicted concentrations of actinides up to 244 Cm have been compared with the corresponding measured values. The results show a global good agreement between calculated and experimental concentrations, in several cases within the margins of the nuclear data uncertainties and in a few cases even within the reported experimental uncertainties. The differences in the performances of the JEFF3.1.1, ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear data libraries (NDLs) have also been assessed and the use of the newly released ENDF/B-VIII.0 library has shown an increased accuracy in the prediction of the C/E's for some of the actinides considered, particularly for the plutonium isotopes. This work represents a step forward towards the validation of advanced simulation tools against post irradiation experimental data and the obtained results provide an evidence of the capabilities of the Serpent Monte-Carlo code with the associated modern NDLs to accurately compute SNF nuclide inventory concentrations for VVER-1000 type reactors.
Progress in Nuclear Energy, 2006
The System for Analysis of Reactor Core (SARC) has been developed for burnup analysis of nuclear reactors using whole core modeling and simulation by coupling the WIMS-D4S and CITATION. The system has the capability to use 1981, 1986 and other WIMS-D libraries recently released by International Atomic Energy Agency, based on the latest cross section data. The SARC was used for the analysis of the first equilibrium core of Pakistan Research Reactor-1 using newly released JENDL3.2 based WIMS-D library. The calculated cycle length and other core related parameters were found in good agreement with the experimental results. Changes in several core parameters such as core reactivity, flux, element-wise power densities, depletion of 235 U and production of 239 Pu were also analysed during the cycle. It was found that all these parameters vary linearly with burnup of the core. Moreover, the actinide and fission product inventories in the discharged fuel were also computed.
Burnup simulations of different fuel grades using the MCNPX Monte Carlo code
Nuclear Technology and Radiation Protection, 2014
Global energy problems range from the increasing cost of fuel to the unequal distribution of energy resources and the potential climate change resulting from the burning of fossil fuels. A sustainable nuclear energy would augment the current world energy supply and serve as a reliable future energy source. This research focuses on Monte Carlo simulations of pressurized water reactor systems. Three different fuel grades - mixed oxide fuel (MOX), uranium oxide fuel (UOX), and commercially enriched uranium or uranium metal (CEU) - are used in this simulation and their impact on the effective multiplication factor (Keff) and, hence, criticality and total radioactivity of the reactor core after fuel burnup analyzed. The effect of different clad materials on Keff is also studied. Burnup calculation results indicate a buildup of plutonium isotopes in UOX and CEU, as opposed to a decline in plutonium radioisotopes for MOX fuel burnup time. For MOX fuel, a decrease of 31.9% of the fissile pl...
Propagation of nuclear data uncertainties for ELECTRA burn-up calculations
2014
The European Lead-Cooled Training Reactor (ELECTRA) has been proposed as a training reactor for fast systems within the Swedish nuclear program. It is a low-power fast reactor cooled by pure liquid lead. In this work, we propagate the uncertainties in 239 P u transport data to uncertainties in the fuel inventory of ELECTRA during the reactor life using the Total Monte Carlo approach (TMC). Within the TENDL project the nuclear models input parameters were randomized within their uncertainties and 740 239 P u nuclear data libraries were generated. These libraries are used as inputs to reactor codes, in our case SERPENT, to perform uncertainty analysis of nuclear reactor inventory during burn-up. The uncertainty in the inventory determines uncertainties in: the longterm radio-toxicity, the decay heat, the evolution of reactivity parameters, gas pressure and volatile fission product content. In this work, a methodology called fast TMC is utilized, which reduces the overall calculation time. The uncertainty in the long-term radiotoxicity, decay heat, gas pressure and volatile fission products were found to be insignificant. However, the uncertainty of some minor actinides were observed to be rather large and therefore their impact on multiple recycling should be investigated further. It was also found that, criticality benchmarks can be used to reduce inventory uncertainties due to nuclear data. Further studies are needed to include fission yield uncertainties, more isotopes, and a larger set of benchmarks.
Validation of spent nuclear fuel decay heat calculations using Polaris, ORIGEN and CASMO5
Annals of Nuclear Energy, 2022
In this paper, we validate the decay heat calculation capability via a two-step method to analyze spent nuclear fuel (SNF) discharged from pressurized water reactors (PWRs). The calculation method is implemented with a lattice code STREAM and a nodal diffusion code RAST-K. One of the features of this method is the direct consideration of three-dimensional (3D) core simulation conditions with the advantage of a short simulation time. Other features include the prediction of the isotope inventory by Lagrange non-linear interpolation and the use of power history correction factors. The validation is performed with 58 decay heat measurements of 48 fuel assemblies (FAs) discharged from five PWRs operated in Sweden and the United States. These realistic benchmarks cover the discharge burnup range up to 51 GWd/MTU, 23.2 years of cooling time, and spanning an initial uranium enrichment range of 2.100e4.005 wt percent. The SNF analysis capability of STREAM is also employed in the code-to-code comparison. Compared to the measurements, the validation results of the FA calculation with RAST-K are within ±4%, and the pin-wise results are within ±4.3%. This paper successfully demonstrates that the developed decay heat calculation method can perform SNF back-end cycle analyses.
International Journal of Engineering Research and Advanced Technology (IJERAT), 2020
The computational benchmarks performed on the TVEL and the Westinghouse fuel assemblies for the VVER-1000 nuclear power reactor have been calculated by the Monte Carlo code (version MCNPX 2.7). The calculations were performed on models of the fuel assemblies of the VVER-1000 reactor. The basis was taken of a typical fuel assembly of the Russian TVEL suppliers and the new fuel assemblies of the American company Westinghouse. The aim of this work was to analyze the changes in the isotopic composition of spent nuclear fuel of VVER-1000 due to various operational conditions. The variations of keff and assembly's average isotopic composition vs. burnup were calculated. Fission and activation products and actinide daughter nuclides selected for calculation e.g.235U, 236U, 239Pu, 154Eu, 134Cs, were those important for the assessment of nuclear safety in the management and storage of spent fuel. The variations of isotopic composition vs. time during operation and cooling were calculated. Most of the results agreed excellently with those calculated by the SERPENT code.
The joint evaluated fission and fusion nuclear data library, JEFF-3.3
The European Physical Journal A
The joint evaluated fission and fusion nuclear data library 3.3 is described. New evaluations for neutron-induced interactions with the major actinides ^{235}\hbox {U}235U,235 U ,235U,^{238}\hbox {U}238Uand238 U and238Uand^{239}\hbox {Pu}239Pu,on239 Pu , on239Pu,on^{241}\hbox {Am}241Amand241 Am and241Amand^{23}\hbox {Na}23Na,23 Na ,23Na,^{59}\hbox {Ni}$$ 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 u...
On the estimation of nuclide inventory and decay heat: a review from the EURAD European project
EPJ Nuclear Sciences & Technologies
In this work, a study dedicated to the characterization of the neutronics aspect of the Spent Nuclear Fuel (SNF), as part of the European project EURAD (Work Package 8), is presented. Both measured nuclide concentrations from Post Irradiation Examination samples and decay heat from calorimetric measurements are compared to simulations performed by different partners of the project. Based on these detailed studies and data from the published literature, recommendations are proposed with respect to best practices for SNF modelling, as well as biases and uncertainties for a number of important nuclides and the SNF decay heat for a cooling period from 1 to 1000 years. Finally, specific needs are presented for the improvement of current code prediction capabilities.