D. Rochman

Efficient use of Monte Carlo : Uncertainty Propagation

Abstract A new and faster Total Monte Carlo (TMC) method for the propagation of nuclear data uncertainties in Monte Carlo nuclear simulations is presented (the fast TMC method). It addresses the main drawback of the original TMC method, namely, the necessary large time multiplication factor compared to a single calculation. With this new method, Monte Carlo simulations can now be accompanied with an uncertainty propagation (other than statistical), with small additional calculation time. The fast TMC method is presented and compared with the TMC and fast GRS methods for criticality and shielding benchmarks and burnup calculations. Finally, to demonstrate the efficiency of the method, uncertainties due to uncertainties in 235,238U, 239Pu, and thermal scattering nuclear data, for the local deposited power in 12.7 million cells, are calculated for a full-size reactor core.

How to Randomly Evaluate Nuclear Data: A New Data Adjustment Method Applied to 239Pu

Abstract This paper presents a novel approach to combine Monte Carlo optimization and nuclear data to produce an optimal adjusted nuclear data file. We first introduce the methodology, which is based on the so-called “Total Monte Carlo” and the TALYS system. As an original procedure, not only a single nuclear data file is produced for a given isotope but virtually an infinite number, defining probability distributions for each nuclear quantity. Then, each of these random nuclear data libraries is used in a series of benchmark calculations. With a goodness-of-fit estimator, a best evaluation for that benchmark set can be selected. To apply the proposed method, the neutron-induced reactions on 239Pu are chosen. More than 600 random files of 239Pu are presented, and each of them is tested with 120 criticality benchmarks. From this, the best performing random file is chosen and proposed as the optimum choice among the studied random set.

UO2 versus MOX: Propagated Nuclear Data Uncertainty for keff, with Burnup

Abstract Precise assessment of propagated nuclear data uncertainties in integral reactor quantities is necessary for the development of new reactors as well as for modified use, e.g., when replacing UO2 fuel by mixed-oxide (MOX) fuel in conventional thermal reactors. This paper compares UO2 fuel to two types of MOX fuel with respect to propagated nuclear data uncertainty, primarily in keff, by applying the Fast Total Monte Carlo method (Fast TMC) to a typical pressurized water reactor pin cell model in Serpent, including burnup. An extensive amount of nuclear data is taken into account, including transport and activation data for 105 nuclides, fission yields for 13 actinides, and thermal scattering data for H in H2O. There is indeed a significant difference in propagated nuclear data uncertainty in keff; at zero burnup, the uncertainty is 0.6% for UO2 and ˜ 1% for the MOX fuels. The difference decreases with burnup. Uncertainties in fissile fuel nuclides and thermal scattering are the most important for the difference, and the reasons for this are understood and explained. This work thus suggests that there can be an important difference between UO2 and MOX for the determination of uncertainty margins. However, it is difficult to estimate the effects of the simplified model; uncertainties should be propagated in more complicated models of any considered system. Fast TMC, however, allows for this without adding much computational time.

Propagation of 235,236,238 U and 239 Pu Nuclear Data Uncertainties for a Typical PWR Fuel Element

The effects of nuclear data uncertainties (cross sections, neutron emissions, fission yields, and decay data) on the burnup of a typical pressurized water reactor fuel element are presented in this paper. The uncertainties on reactivity swing, inventory, and radiotoxicity are obtained using a Monte Carlo method for nuclear data uncertainty propagation and the Monte Carlo transport code SERPENT. The impact of the nuclear data uncertainties for the two main actinide isotopes at the beginning of irradiation (235U and 238U) with the third and fourth most abundant actinide isotopes at the end of irradiation (236U and 239Pu) are calculated, showing the importance of fission yield data relative to transport data.

Evaluation and Adjustment of the Neutron-Induced Reactions of 63,65Cu

Abstract This paper presents new evaluations for the two natural isotopes of copper from thermal neutron energy up to 200 MeV, including covariances. The evaluation and adjustment method consists of applying a Monte Carlo method to select the model parameters to obtain better agreement with differential data, criticality safety, and fusion benchmarks. In the resonance range, the latest resonance parameters and uncertainties are adopted. In the fast neutron range, the TALYS reaction code is used to calculate all nuclear data quantities and covariances. The proposed evaluations present important improvements for fusion benchmarks compared to the current libraries. These new evaluations of 63Cu and 65Cu are proposed for the JEFF-3.2 European nuclear data library. As a spinoff, correlations between cross sections and benchmarks can be obtained.

Uncertainties for the Kalimer Sodium Fast Reactor: Void Reactivity Coefficient, k eff, βeff, Depletion and Radiotoxicity

In this paper, we present the effect of nuclear data and its uncertainties on the core design of the KALIMER-600 Sodium Fast Reactor. The void reactivity coefficient, k eff, and βeff are calculated with uncertainties due to the nuclear data of the main components of the reactor: 238U, 239;240Pu, 23Na, 56Fe, and 90Zr. Two methods are used: the “Total Monte Carlo” method involving many identical calculations with different sets of randomized nuclear data, and the perturbation method using MCNP. In a second step, the depletion is calculated together with radiotoxicity components after irradiation. By the Total Monte Carlo method, uncertainties due to nuclear data are propagated to the fuel composition and radiotoxicity curves.

Total Monte Carlo evaluation for dose calculations

Total Monte Carlo (TMC) is a method to propagate nuclear data (ND) uncertainties in transport codes, by using a large set of ND files, which covers the ND uncertainty. The transport code is run multiple times, each time with a unique ND file, and the result is a distribution of the investigated parameter, e.g. dose, where the width of the distribution is interpreted as the uncertainty due to ND. Until recently, this was computer intensive, but with a new development, fast TMC, more applications are accessible. The aim of this work is to test the fast TMC methodology on a dosimetry application and to propagate the (56)Fe uncertainties on the predictions of the dose outside a proposed 14-MeV neutron facility. The uncertainty was found to be 4.2 %. This can be considered small; however, this cannot be generalised to all dosimetry applications and so ND uncertainties should routinely be included in most dosimetry modelling.

New Evaluation of the 99Tc Neutron-Induced Cross Sections for the ENDF/B-VII.0 Library

Abstract Neutron-induced cross sections for 99Tc were evaluated from 10−5 eV to 20 MeV for the U.S. ENDF/B-VII.0 library released in December 2006. The resonance parameters were adopted from the new Atlas of Neutron Resonances. In the fast region, all open reaction channels were evaluated with the reaction code EMPIRE-2.19. Comparison of the present evaluation with microscopic measurements and data recommended by European (JEFF-3.1) and Japanese (JENDL-3.3) libraries is presented.

Testing the Sampling-Based NUSS-RF Tool for the Nuclear Data—Related Global Sensitivity Analysis with Monte Carlo Neutronics Calculations

Abstract NUSS-RF is a tool for nuclear data uncertainty propagation through neutronics calculations with continuous-energy Monte Carlo codes and ACE-formatted nuclear data libraries. Many existing codes, including the original version of NUSS (Nuclear data Uncertainty Stochastic Sampling), are based on simple random sampling algorithms. The NUSS-RF extension now uses a frequency-based sampling algorithm, called the random balance design (RBD), to analyze individual nuclear data uncertainty contributions in regard to the total output (e.g., keff) uncertainty. The implementation of the RBD method into NUSS-RF is initially verified by comparing the computed individual input variance contributions with analytical solutions for two analytical test cases. As well, it is assessed against the alternative approach based on the use of correlation coefficients. NUSS-RF is then used for an analysis of the Jezebel and Godiva fast-spectrum criticality benchmarks: in a first step, the overall effect of the 239Pu(n,f) and 235U(n,f) cross-section uncertainties on keff is evaluated, while in a second step, the contributions from the individual energy groups are quantified. As an additional verification, the NUSS-RF results are assessed against sensitivity and uncertainty analysis based on perturbation theory, showing good agreement between the two solutions. Finally, the capability of NUSS-RF is demonstrated for ranking the input parameters with respect to their influence on the total uncertainty of the output parameters, taking into account possible correlations between input parameters. Possible future improvements for the current computational scheme are discussed in the conclusions.

Uncertainty Analysis on Reactivity and Discharged Inventory due to 235,238 U, 239,240,241 Pu, and Fission Products: Application to a Pressurized Water Reactor Fuel Assembly

Uncertainty analysis on reactivity and discharged inventory for a typical pressurized water reactor fuel element as a result of uncertainties in 235,238U, 239,240,241Pu, and fission products nuclear data was performed. A typical Westinghouse three-loop fuel assembly fueled with UO2 fuel with 4.8% enrichment was selected. The Total Monte Carlo method was applied using the deterministic transport code DRAGON. This code allows the generation of the few-groups nuclear data libraries by directly using data contained in the nuclear data evaluation files. The nuclear data used in this study are from the JEFF3.1 evaluation, with the exception of the nuclear data files for U, Pu, and fission products isotopes (randomized for the generation of the various DRAGON libraries). These are taken from the TALYS evaluated nuclear data library TENDL-2012. Results show that the calculated total uncertainty in keff (as a result of uncertainties in nuclear data of the considered isotopes) is virtually independent of fuel burnup, and amounts to 700 pcm. The uncertainties in the inventory of the discharged fuel are dependent on the element considered and lie in the range 1% to 15% for most fission products, and are <5% for the most important actinides. The total uncertainty on the reactor parameters was also split into different components (different nuclear reaction channels), and the main sources of uncertainties were identified.