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Highlights • A cheaper way to validate the reactor-physics code SARAX for application in CFR is introduced. • The nuclear-data adjustment to extrapolate measurements to CFR is calculated and analyzed. • Different correlation coefficients between measurements are compared. Abstract The validation works have been implemented to a newly-developed code SARAX for China Fast Reactor (CFR) in this paper. Different with the conventional way to validate the code using the dedicated experimental data, a theoretical approach was proposed and implemented by using the existing similar experimental data. This theoretical approach is based on the technology of sensitivity/uncertainty analysis, similarity analysis and nuclear-data adjustment. In our previous works, detailed introduction towards sensitivity/uncertainty analysis and similarity analysis have been implemented to distinguish the existing experiments which are 'similar' to the CFR's criticality characteristics. This paper focus on the method to extrapolate the 'similar' experimental data to predict the best-estimate measurements of CFR with application of nuclear-data adjustment, providing reference values to validate the SARAX code. From the numerical results, it can be observed that through nuclear-data adjustment, the simulation results of the existing experiments can agree well with corresponding measurements, with the bias reduced notably to be within 25 pcm. Moreover, the nuclear-data adjustment can also improve the nuclear-data uncertainties notably, with the relative uncertainties of the reactor k eff due to nuclear data having been reduced from the value exceeding 1.0% to the values about 0.15%. [ABSTRACT FROM AUTHOR]

• A optimization method for PWR fuel-reloading pattern has been proposed. • The improved CNN was applied to evaluate fuel-reloading pattern efficiently. • The optimization method has been applied to CNP1000 PWR operated in China. In this paper, the optimization method of fuel-reloading pattern for PWR has been studied based on the improved convolutional neural network (CNN) and genetic algorithm (GA). It is very important to search out the optimized fuel-reloading pattern to guarantee the safety and economy of the nuclear power plants. During the optimization, large number of fuel-reloading patterns should be evaluated, providing the core parameters (including the cycle length, power-peak factors and so on) to the optimization algorithm to search for the optimized pattern. In our study, the CNN was improved with the advanced Inception-ResNet structure and applied to train the rapid-evaluation model, which can receive the fuel-reloading patterns and feedback corresponding core parameters with sufficient accuracy and very-high efficiency. The GA was applied as the optimization algorithm to search for the optimized fuel-reloading pattern. This proposed optimization method has been applied to the optimization of fuel-reloading pattern for the CNP1000-type PWR reactor operated in China. It can be observed that the CNN can evaluated the core parameters of one-single fuel-reloading pattern in about 0.0005 s and the averaged evaluation errors smaller than 0.6%; the GA can search the optimized fuel-reloading pattern in about 20 min. The study in this paper indicated that the combination of CNN and GA can provide the optimization of fuel-reloading pattern for PWR in very-short time, which can be applied to improve the safety and economy of the nuclear power plants. [ABSTRACT FROM AUTHOR]

• Nuclear-data adjustment based on GLLSM has been implemented. • The adjusted continue-energy cross-section library was generated for Monte-Carlo code. • Nuclear-data adjustment has been implemented to the fast-reactor experimental facilities. In this study, the nuclear-data adjustment method based on the continue-energy cross sections has been implemented for the fast reactor. The technique of nuclear-data adjustment based on the sensitivity/uncertainty analysis has been widely applied for the fast reactor to improve the nuclear-data accuracy. The previous researches towards nuclear-data adjustment were implemented to the multigroup cross-section libraries. As improvement, the nuclear-data adjustment based on the continue-energy cross-section library and corresponding Monte-Carlo code has been implemented. The Generalized Linear Least Square method was used to determine the relative adjustments for different kinds of isotope and cross sections within different energy ranges. For adjusting the continue-energy cross-section library, corresponding continue-energy cross-section perturbation model has been proposed and applied in this study. As application, the experiment measurements provided on the JOYO and ZPPR facilities have been used for the nuclear-data adjustment. Observed from the numerical results, very efficient improvement of the nuclear-data accuracy can be satisfied through the nuclear-data adjustment for the continue-energy cross sections. [ABSTRACT FROM AUTHOR]

Highlights • It shows almost linear propagation in the uncertainty quantification of criticality during the process neutronics calculation in k inf and k eff. • The most important contributors is the inelastic scattering reaction of U-238. • The relative uncertainties of reactivity are bigger than that of criticality in neutronics calculation. • The relative uncertainties of parameters in the MOX-3600 core are always bigger than the ones in the MET-1000 core. Abstract To quantify the uncertainties in the neutronics calculations of sodium-cooled fast reactor (SFR), a series of sensitivity and uncertainty (S&U) analysis were performed on two typical SFRs, which were a large-size MOX-fueled core and a medium-size metallic fueled core. The analysis was done from the pin-cell level to full core level to investigate the uncertainty propagation in the two-step neutronics calculation scheme. The responses analyzed contained the k inf , homogenized cross-sections, sodium void reactivity (SVR), Doppler constant (K D) and control rod worth (CRW) at the sub-assembly level, and the k eff and power distribution at the full core level. The direct numerical perturbation method was used to get the relative sensitivity coefficients and the 'Sandwich Rule' was used to get the relative uncertainties. The results showed that the relative uncertainties propagated linearly from pin-cell to full core for the k inf and k eff. Relative uncertainties of reactivity like SVR were much bigger than the ones of k inf and k eff. However, the relative uncertainty of power distribution was smaller than other responses. Differences were observed in the S&U analysis between the MOX fueled and metallic fueled SFRs. Softer spectrum of the large-size MOX fueled core caused bigger uncertainty compared to the metallic fueled core. [ABSTRACT FROM AUTHOR]

Highlights • The sensitivity of k eff of parts of existing evaluated experiments is analyzed. • Different methods to distinguish the similar experiments are compared. • Similarity characteristics of sodium-cooled fast reactor are discussed. Abstract We conducted a validation of the newly developed SARAX code for the China Fast Reactor (CFR). Unlike the usual way to validate code using dedicated experimental data, we proposed and implemented a theoretical way using the existing experimental data. This paper introduced a method to select the suitable existing experiments, which are "similar" to the CFR's criticality characteristics. We based our method on the sensitivity and uncertainty (S&U) analysis and the similarity analysis. We analyzed different experiments and discussed different ways to distinguish these experiments. Our results showed that a large-size mixed-oxide-fueled experimental core was necessary to represent the critical characteristics of CFR for the code validation. The method using both sensitivity and covariance was better able to distinguish the difference between the objective core and the other cores. [ABSTRACT FROM AUTHOR]

Highlights • A new code SARAX has been developed for fast reactor neutronics analysis. • A new hybrid method has been proposed to generate the few-group cross section. • The core calculation is based on the transport theory with two-level coarse mesh acceleration. • A new combined S&U analysis method has been proposed with implicit sensitivity. Abstract A renaissance in fast reactor research and development and the rapid development of computing technologies have encouraged improvements in fast reactor modeling and calculation. A new code named SARAX has been developed. This paper reviewed current fast reactor neutronics analysis codes and provided an overview of SARAX developments. We introduced the following three methods: a hybrid method for cross-section generation, a two-level coarse mesh method for transport calculation acceleration, and a combined sensitivity and uncertainty analysis method. We discussed preliminary tests to show the benefits of these methods from two perspectives: (1) replacing the flux solver from neutron diffusion to neutron transport and (2) using a hybrid method rather than using the all Monte Carlo method to generate the cross-sections. We discussed an error cancelation phenomenon based on the numerical benchmarks. [ABSTRACT FROM AUTHOR]

This paper presents a strategy for processing the covariance matrix of the prompt fission neutron spectrum (PFNS) and propagating the PFNS uncertainties to the neutronic calculation of fast and thermal reactors. Due to their great significance to the accurate prediction of nuclear criticality, the sensitivity and uncertainty of core parameters for PFNS are analyzed in this study. The propagation of PFNS uncertainties from the evaluated nuclear data files (ENDF) to the core parameters is divided into two phases. In the first phase, the covariance data in the ENDF is transformed into the multi-group form, in which the "zero-sum" constraint is considered and a corresponding functional module is developed in the updated NECP-Atlas nuclear data processing code. In the second phase, the neutron transport code is coupled as a "black box" to propagate the uncertainties in the multi-group form to the core parameters. Sensitivity and uncertainty (S&U) analysis function is implemented in the NECP-UNICORN uncertainty quantification code. As for its application, S&U analysis for PFNS has been performed to the fast and thermal reactors. The numerical results indicate that the influence of PFNS uncertainties to the confidence interval of reactivity prediction and power distribution should be considered in the nuclear reactor analyses. [ABSTRACT FROM AUTHOR]