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In the framework of the FP7 Euratom project FREYA, the Serpent Monte Code was used to characterize a number of critical VENUS-F core configurations. Several neutronic parameters calculated by Serpent were compared to the available experimental data and reported in a previous study. Although a generally reasonable agreement between the calculated and experimental values was obtained, the study also revealed some important issues related to the numerical results such as a systematic overestimation of reactivity and a systematic underestimation of U238 to U235 fission rate ratio (designated as F28/F25 spectral index). The objective of the current follow-up study is to quantify the effect of nuclear data uncertainties on the Monte Carlo estimates of reactivity and spectral indices. The analysis is carried out for two critical VENUS-F cores using a recently upgraded version of Serpent which is capable to perform sensitivity analysis based on the collision history method and to propagate and quantify nuclear data uncertainties using multi-group covariance libraries in an automated way. The criticality calculations were performed using the ENDV/B-VII.1 based cross sections while the uncertainties due to nuclear data were obtained using 56-group neutron cross section covariance library from the SCALE-6.2 package. The estimated uncertainties due to nuclear data are of the order of 2200 pcm and 8% on k-eff and F28/F25 spectral index respectively. It was found that the major contributor to the k-eff uncertainty is capture cross section of U235. In the F28/F25 case, the total uncertainty is dominated by inelastic cross section of U238, fission spectrum of U235, and capture cross section of U235.

The XGPT method extends the Generalized Perturbation Theory capabilities of Monte Carlo codes to continuous-energy sensitivity functions. In this work, this method is proposed as a new approach to nuclear data uncertainty propagation. XGPT overcomes some of the limitations of legacy perturbation-based approaches. In particular, it allows the nuclear data uncertainty propagation to be performed adopting continuous energy covariance matrices, instead of discretized (multi-group) data. The XGPT capabilities are demonstrated in three simple fast criticality benchmarks for 239Pu and 208Pb cross section uncertainties. The new method is also applied in selected cases to estimate higher moments of the keff distribution, starting from TENDL random evaluations. The XGPT estimates, when compared against reference Total Monte Carlo (TMC) results, show a good agreement and a significant reduction in computational requirements with respect to the TMC approach. Finally, the capabilities for uncertainty propagation involving adjoint-weighted response functions are demonstrated.

The XGPT method extends the Generalized Perturbation Theory capabilities of Monte Carlo codes to continuous-energy sensitivity functions. In this work, this method is proposed as a new approach to nuclear data uncertainty propagation. XGPT overcomes some of the limitations of legacy perturbation-based approaches. In particular, it allows the nuclear data uncertainty propagation to be performed adopting continuous energy covariance matrices, instead of discretized (multi-group) data. The XGPT capabilities are demonstrated in three simple fast criticality benchmarks for 239Pu and 208Pb cross section uncertainties. The new method is also applied in selected cases to estimate higher moments of the keff distribution, starting from TENDL random evaluations. The XGPT estimates, when compared against reference Total Monte Carlo (TMC) results, show a good agreement and a significant reduction in computational requirements with respect to the TMC approach. Finally, the capabilities for uncertainty propagation involving adjoint-weighted response functions are demonstrated.

Coupled Monte Carlo (MC) methods are becoming widely used in reactor physics analysis and design. Many research groups therefore, developed their own coupled MC depletion codes. Typically, in such coupled code systems, neutron fluxes and cross sections are provided to the depletion module by solving a static neutron transport problem. These fluxes and cross sections are representative only of a specific time-point. In reality however, both quantities would change through the depletion time interval. Recently, Generalized Perturbation Theory (GPT) equivalent method that relies on collision history approach was implemented in Serpent MC code. This method was used here to calculate the sensitivity of each nuclide and reaction cross section due to the change in concentration of every isotope in the system. The coupling method proposed in this study also uses the substep approach, which incorporates these sensitivity coefficients to account for temporal changes in cross sections. As a result, a notable improvement in time dependent cross section behavior was obtained. The method was implemented in a wrapper script that couples Serpent with an external depletion solver. The performance of this method was compared with other existing methods. The results indicate that the proposed method requires substantially less MC transport solutions to achieve the same accuracy.

The RBWR-TR is a thorium-based reduced moderation BWR (RBWR) with a high transuranic (TRU) consumption rate. It is charged with LWR TRU and thorium, and it recycles all actinides an unlimited number of times while discharging only fission products and trace amounts of actinides through reprocessing losses. This design is a variant of the Hitachi RBWR-TB2, which arranges its fuel in a hexagonal lattice, axially segregates seed and blanket regions, and fits within an existing ABWR pressure vessel. The RBWR-TR eliminates the internal axial blanket, eliminates absorbers from the upper reflector, and uses thorium rather than depleted uranium as the fertile makeup fuel. This design has been previously shown to perform comparably to the RBWR-TB2 in terms of TRU consumption rate and burnup, while providing significantly larger margin against critical heat flux. This study examines the uncertainty in key neutronics parameters due to nuclear data uncertainty. As most of the fissions are induced by epithermal neutrons and since the reactor uses higher actinides as well as thorium and 233U, the cross sections have significantly more uncertainty than in typical LWRs. The sensitivity of the multiplication factor (keff) to the cross sections of many actinides is quantified using a modified version of Serpent 2.1.19 [1]. Serpent [2] is a Monte Carlo code which uses delta tracking to speed up the simulation of reactors; in this modified version, cross sections are artificially inflated to sample more collision, and collisions are rejected to preserve a "fair game." The impact of these rejected collisions is then propagated to the multiplication factor using generalized perturbation theory [3]. Covariance matrices are retrieved for the ENDF/B-VII.1 library [4], and used to collapse the sensitivity vectors to an uncertainty on the multiplication factor. The simulation is repeated for several reactor configurations (for example, with a reduced flow rate, and with control rods inserted)

Coupled Monte Carlo (MC) methods are becoming widely used in reactor physics analysis and design. Many research groups therefore, developed their own coupled MC depletion codes. Typically, in such coupled code systems, neutron fluxes and cross sections are provided to the depletion module by solving a static neutron transport problem. These fluxes and cross sections are representative only of a specific time-point. In reality however, both quantities would change through the depletion time interval. Recently, Generalized Perturbation Theory (GPT) equivalent method that relies on collision history approach was implemented in Serpent MC code. This method was used here to calculate the sensitivity of each nuclide and reaction cross section due to the change in concentration of every isotope in the system. The coupling method proposed in this study also uses the substep approach, which incorporates these sensitivity coefficients to account for temporal changes in cross sections. As a result, a notable improvement in time dependent cross section behavior was obtained. The method was implemented in a wrapper script that couples Serpent with an external depletion solver. The performance of this method was compared with other existing methods. The results indicate that the proposed method requires substantially less MC transport solutions to achieve the same accuracy.

The RBWR-TR is a thorium-based reduced moderation BWR (RBWR) with a high transuranic (TRU) consumption rate. It is charged with LWR TRU and thorium, and it recycles all actinides an unlimited number of times while discharging only fission products and trace amounts of actinides through reprocessing losses. This design is a variant of the Hitachi RBWR-TB2, which arranges its fuel in a hexagonal lattice, axially segregates seed and blanket regions, and fits within an existing ABWR pressure vessel. The RBWR-TR eliminates the internal axial blanket, eliminates absorbers from the upper reflector, and uses thorium rather than depleted uranium as the fertile makeup fuel. This design has been previously shown to perform comparably to the RBWR-TB2 in terms of TRU consumption rate and burnup, while providing significantly larger margin against critical heat flux. This study examines the uncertainty in key neutronics parameters due to nuclear data uncertainty. As most of the fissions are induced by epithermal neutrons and since the reactor uses higher actinides as well as thorium and 233U, the cross sections have significantly more uncertainty than in typical LWRs. The sensitivity of the multiplication factor (keff) to the cross sections of many actinides is quantified using a modified version of Serpent 2.1.19 [1]. Serpent [2] is a Monte Carlo code which uses delta tracking to speed up the simulation of reactors; in this modified version, cross sections are artificially inflated to sample more collision, and collisions are rejected to preserve a "fair game." The impact of these rejected collisions is then propagated to the multiplication factor using generalized perturbation theory [3]. Covariance matrices are retrieved for the ENDF/B-VII.1 library [4], and used to collapse the sensitivity vectors to an uncertainty on the multiplication factor. The simulation is repeated for several reactor configurations (for example, with a reduced flow rate, and with control rods inserted)

This study summarizes some initial results of nuclear data uncertainty quantification for the FREYA fast critical experiments

Capabilities 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.

Capabilities 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.

The coolant density coefficient represents one of the main reactivity feedback in Lead-cooled Fast Reactors (LFRs) and Sodium-cooled Fast Rectors (SFRs), and its accurate calculation is important for a correct evaluation of the dynamic of these systems. Coolant density reactivity maps have been calculated in the past adopting perturbation theory in deterministic codes. Usually, full-core simulations employed multi-group diffusion codes or 2D (r, z) geometrical approximations. Nowadays, Monte Carlo neutron transport simulations are commonly adopted for the study of LFR and SFR. Nonetheless, reactivity feedback is usually calculated via direct perturbations, i.e., comparing the effective multiplication factor of two separate Monte Carlo runs. When small effects are to be investigated via the direct perturbation approach, the adoption of either large system perturbations or a large number of simulated particles is required, in order to reduce the statistical errors. Moreover, if spatial maps of coolant density reactivity coefficient are to be generated via direct perturbation, one criticality source Monte Carlo simulation is required for each spatial region. In this view, the sensitivity/perturbation method offer the advantage of producing a large number of sensitivity coefficients is a single calculation. More important, this approach allows decomposing reactivity effects by energy and reaction for a deeper investigation of the feedback. In this work, two LFR and SFR core designs are considered, focusing on the calculation and analysis of the coolant density reactivity coefficient. The space-dependent lead and sodium density reactivity worth are calculated adopting the sensitivity/perturbation capabilities recently implemented in an extended version of the Serpent-2 code , previously adopted for the calculation of coolant void maps . The present work focuses on the validation of the sensitivity/perturbation results against direct perturbation calculations, on the anal

The coolant density coefficient represents one of the main reactivity feedback in Lead-cooled Fast Reactors (LFRs) and Sodium-cooled Fast Rectors (SFRs), and its accurate calculation is important for a correct evaluation of the dynamic of these systems. Coolant density reactivity maps have been calculated in the past adopting perturbation theory in deterministic codes. Usually, full-core simulations employed multi-group diffusion codes or 2D (r, z) geometrical approximations. Nowadays, Monte Carlo neutron transport simulations are commonly adopted for the study of LFR and SFR. Nonetheless, reactivity feedback is usually calculated via direct perturbations, i.e., comparing the effective multiplication factor of two separate Monte Carlo runs. When small effects are to be investigated via the direct perturbation approach, the adoption of either large system perturbations or a large number of simulated particles is required, in order to reduce the statistical errors. Moreover, if spatial maps of coolant density reactivity coefficient are to be generated via direct perturbation, one criticality source Monte Carlo simulation is required for each spatial region. In this view, the sensitivity/perturbation method offer the advantage of producing a large number of sensitivity coefficients is a single calculation. More important, this approach allows decomposing reactivity effects by energy and reaction for a deeper investigation of the feedback. In this work, two LFR and SFR core designs are considered, focusing on the calculation and analysis of the coolant density reactivity coefficient. The space-dependent lead and sodium density reactivity worth are calculated adopting the sensitivity/perturbation capabilities recently implemented in an extended version of the Serpent-2 code , previously adopted for the calculation of coolant void maps . The present work focuses on the validation of the sensitivity/perturbation results against direct perturbation calculations, on the anal

This paper presents the methodology developed for the Serpent 2 Monte Carlo code for the calculation of adjoint-weighted reactor point kinetics parameters: effective generation time and delayed neutron fractions. The calculation routines were implemented at the Politecnico di Milano, and they are based on the iterated fission probability (IFP) method. The developed methodology is mainly intended for the modeling of small research reactor cores, and the results are validated by comparison to experimental data and MCNP5 calculations in 31 critical configurations.