Traditional thermal-neutron multiplicity coincidence counting relies on 3 He detectors and the thermalneutron (n,p) reaction. Due to the scarcity of 3 He, both homeland security and nuclear safeguards applications are actively seeking neutron detectors that can replace 3 He tubes. Liquid scintillation detectors offer excellent (n,γ) discrimination capability, and they do not require neutron moderation; instead, they can directly detect fission neutrons emitted from nuclear materials. Fast-neutron detection provides significant advantages because it preserves and measures the intrinsic nanosecond time scale of spontaneous and induced fission neutrons, rather than the microsecond scale that results from neutron thermalization in thermal-neutron detectors. The neutron lifetime is shortened by up to three orders of magnitude, which offers a substantial advantage when measuring plutonium materials with large masses and high (α,n) neutron yields. In this work, a fast-neutron multiplicity counter is studied using the RMC (Reactor Monte Carlo code), independently developed at Tsinghua University. First, a function for outputting elastic-scattering timestamps was added to RMC, enabling the generation of complete time-pulse sequences. Then, the neutron multipliciy counting singles, doubles, and triples can be obtained. Second, the system of equations for fast-neutron multiplicity counting was investigated. Considering the differences between spontaneousfission spectra and (α,n) spectra in PuO 2 materials, an improved set of fast-neutron multiplicity equations is proposed. Validation tests show that the improved equations enhance the accuracy of mass inversion to some extent, demonstrating the correctness and effectiveness of the method.

The modelling of particle transport through stochastic geometry such as dispersion fuel is a fundamental part of nuclear reactor simulations. The Poisson Box Sampling (PBS) methods are a new family of algorithms aimed at accurately modelling particle transport through binary Markov mixtures through approximating ensemble-averaged observables for particle transport. Due to the partial memoryless nature of the method, where previously generated boxes are discarded as new boxes are sampled, PBS partially neglects memory effects and spatial correlations between boxes, ultimately resulting in discrepancies with reference solutions. In this work, we explore methods to account for memory effects in PBS for dispersion fuel analysis, using ensemble-averaged results from quenched-disorder modelling methods as benchmark solutions. We propose the Semi-Implicit Poisson Box Sampling (SIPBS) method, using an Dynamic Inclusion Sphere to retain only boxes within a certain range of the particle's position, with the goal of accounting for the most relevant spatial correlations whilst retaining computational efficiency. The results show that SIPBS is able to produce results that are significantly closer to the benchmark results compared to PBS, being able to reproduce results with higher accuracy at a reasonable increase in computational expense.

The neutron multiplicity counting method is a non-destructive testing (NDT) technique that analyzes the properties of materials by measuring neutron emission events from nuclear materials. This method does not require the destruction or alteration of the material itself, making it widely used in the detection and safety management of nuclear materials. This article is based on the self-developed Reactor Monte Carlo code RMC to perform calculations and analysis of the UNCL(uranium neutron coincidence collar) series devices. First, calculations were performed on the UNCL device to compute neutron multiplicity countings under Californium 252 and AmLi neutron sources, and the results were compared with the simulation results of MCNP. Next, modeling calculations were conducted on the UNCL-II device, computing the neutron multiplicity countings under the same neutron sources, while considering the fuel assemblies containing Gd in the UNCL-II. The results were also compared with experimental results and the simulation results of MCNP. The neutron multiplicity count rate calculated by RMC is in good agreement with the experimental values and MCNP results, which proves the correctness of the calculated results by RMC. In addition, the effects of different fitting curves on the mass inversion results and their uncertainties are compared. At the same time, the impact of uncertainties in the polyethylene density on the results was also analyzed. The calculations show that the uncertainty in the polyethylene density has a non-negligible effect on the neutron multiplicity count rate and mass inversion results.

Based on academic research and industrial applications over more than 20 years, the Reactor Monte Carlo code (RMC) developed by the REAL (Reactor Engineering Analysis Laboratory) team at Tsinghua University since 2000 has become a powerful, innovative, and versatile simulation platform for nuclear reactor analysis, shielding simulations, criticality safety calculations, fusion neutronics analysis and beyond. Utilizing collaborative and agile development technology, advanced methods and the most cutting-edge algorithms can be tested and implemented in RMC quickly and efficiently. RMC has been deployed on many world-class supercomputers in China and played an irreplaceable role in the design and analysis of commercial nuclear power plants and newly designed types of advanced nuclear reactors. This paper reviews the state-of-the-art technologies developed in RMC in recent years, such as stochastic and continuous-varying media modeling, advanced transient simulation capability, more accurate energy deposition model, etc. Parallel acceleration on heterogeneous architecture supercomputers and machine learning algorithms would be incorporated in ongoing research and future development plans.

This study developed a new structured nuclear database to improve the readability and extensibility of the ACE (A Compact ENDF) nuclear database, save disk space, reduce memory usage, and enhance the computational efficiency of the Reactor Monte Carlo (RMC) code. A Python package was developed to store nuclear data in HDF5 format. Compared to the ACE database, the HDF5-format database shows significant improvements: an 80% reduction in disk space for continuous energy neutron data and a 60% reduction for neutron thermal scattering data. The HDF5-format database was implemented in RMC’s criticality calculation mode and validated through VERA benchmark problem 2B, demonstrating perfect agreement with the ACE results in keff and neutron flux counts. The Computational results indicate a 7.7% reduction in memory usage and a 20.1% improvement in computational efficiency with the HDF5-format database. Additional tests show that using the database at a single temperature point reduces memory usage by 5.4% and running time by 13.2%. At two temperature points, memory usage decreases by 35.2% and running time by 18.0%. The new data structure reduces temperature-independent redundant data and improves indexing efficiency, leading to greater savings with more temperature points. This development enhances performance of criticality calculation of RMC and addresses ACE database limitations.

Reactor startup without external neutron source is beneficial to improve the economics of nuclear energy, especially in a more and more competitive energy market with the rapid development and deployment of renewable energy. This paper conducts the research on the connection calculation of Monte Carlo Direct Kinetics Simulation (DKS) method and Predictor-Corrector Quasi-Static (PCQS) method during the weak source start-up process of the reactor based on the Monte Carlo code RMC, addressing the calculation methods for time absorption source, delayed neutron source, reactivity, effective delayed neutron fraction and neutron generation time. It achieves advantages in the calculation efficiency in three-dimensional simulations of the connection calculation between DKS in weak neutron field based on stochastic neutron transport equation and PCQS in strong neutron field based on time-dependent Boltzmann neutron transport equation.

Calculating or predicting the energy deposition in the reactor is an important task for reactor design and safety analysis. Rigorous energy deposition estimation is a difficult task because of the energy dependence, spatial dependence, and time dependence of in-core nuclear heating. This paper develops a time-dependent energy deposition model in the RMC kinetics calculation to explicitly account for the time-delay effect of delayed in-core nuclear heating. An explicit sampling method (ESM) is proposed to sample the decay time of the delayed photon and beta precursors based on a new precursor library processed from the ENDF/B-VIII.0 library. The new model is verified by comparing it with the typical equilibrium model using the Godiva experiment and the C5G7- TD benchmark. Maximum 2.5% and 5.4% relative deviations are observed in GODIVA and C5G7-TD cases respectively, which indicates that the equilibrium model underestimates the energy deposition over the transient time. The analysis of the energy deposition results demonstrates that the new model can provide more precise energy deposition estimation in the transient calculation.

Neutron multiplicity measurement technology utilizes the correlation of neutron emission times during fission in nuclear materials. By employing mathematical tools to interpret processes such as neutron generation, multiplication, and detection in nuclear materials, a point model equation can be derived through this process. Simultaneously, nuclear material mass can be inverted based on neutron multiplicity counting. Due to the different spontaneous fission neutron yields of uranium and plutonium, neutron multiplicity analysis methods are divided into active and passive methods. This article is based on the independently developed Monte Carlo program RMC, which models and analyzes the AWCC device, and studies the neutron multiplicity counting in both active and passive methods. At the same time, the parameters of the point model equations are calculated, and the influence of different gate widths on the results is analyzed, demonstrating the capability of RMC in solving this problem. This expands the application of RMC in this field and provides reference for the design of subsequent experimental instruments.

Monte Carlo simulation has become a crucial method internationally for simulating neutron multiplicity counting devices. This method requires sampling of fission neutron multiplicities, energies, and directions, making accurate simulation of fission events highly important. To meet this demand, the Fission Reaction Event Yield Algorithm (FREYA) and the Cascading Gammaray Multiplicity with Fission (CGMF) models have been integrated into RMC. These models can simulate individual fission events, preserving momentum, energy, and angular momentum, and then emit correlated particles. In addition to simulating fission neutrons, CGMF and FREYA models have been employed to compute fission photons, laying the foundation for subsequent photon multiplicity counting. Simultaneously, neutron multiplicity counting has been calculated using CGMF and FREYA, and point model equations have been used to invert Pu material, yielding favorable results.

Neutron multiplicity pertains to the probability distribution of the quantity of neutrons released during induced or spontaneous fission processes within fissile materials. The technology for neutron multiplicity measurement leverages temporal correlations in the emission of fission neutrons from nuclear materials. It employs mathematical tools to elucidate the processes of neutron generation, multiplication within the nuclear material, and detection of outside nuclear materials. In this paper, two multiplicity counting methods are devised building on the RMC (Reactor Monte Carlo) code. The results obtained from both methods, including singles, doubles, and triples counting rates, exhibit good agreement with MCNP. Additionally, parameters associated with the detection efficiency and decay time of the apparatus are computed. By amalgamating the acquired singles, doubles, and triples counting rates, the mass of fissile material within the sample is inversely determined using a passive method with the point model equation. Notably, the point model equation reveals that spontaneous fission neutrons and induced neutrons possess distinct energy spectra, challenging the validity of the assumption that the probability of neutrons being captured without causing fission can be disregarded. In light of these considerations, the neutron multiplicity counting equation was rederived. The accuracy of the Monte Carlo simulation results is improved using the new method.

The ability to calculate the material density sensitivity coefficients of power with respect to the material density has broad application prospects for accelerating Monte Carlo-Thermal Hydraulics iterations. The second-order material density sensitivity coefficients for the general Monte Carlo score have been derived based on the differential operator sampling method in this paper, and the calculation of the sensitivity coefficients of cell power scores with respect to the material density has been realized in continuous-energy Monte Carlo code RMC. Based on the power-density sensitivity coefficients, the sensitivity coefficients of power scores to some other physical quantities, such as power-boron concentration coefficients and power-temperature coefficients considering only the thermal expansion, were subsequently calculated. The effectiveness of the proposed method is demonstrated in the power-density coefficients problems of the pressurized water reactor (PWR) moderator and the heat pipe reactor (HPR) reflectors. The calculations were carried out using RMC and the ENDF/B-VII.1 neutron nuclear data. It is shown that the calculated sensitivity coefficients can be used to predict the power scores accurately over a wide range of boron concentration of the PWR moderator and a wide range of temperature of HPR reflectors.

Due to the generality and flexibility of Monte Carlo methods in geometric modeling and the challenge for traditional deterministic transport methods in dealing with stochastic media, it is necessary to develop a cross-sectional library generation function using a Monte Carlo method as an interface for full core calculations. In this study, we proposed a cross-sectional parameterization method based on the RMC code for the generation of a few-group cross-sectional library. To perform realistic core calculations, a fewgroup neutron cross-sectional library of functions of burnup and thermal-hydraulic parameters was prepared in advance. The results of nodal diffusion code CORCA3D full core calculations with RMC B1 corrected cross-sectional sets agree well with those of RMC continuous-energy calculations. Meanwhile, with the consideration of T-H feedback, the results of the CORCA3D full core calculations show good agreement with RMC full core calculations, with a maximum difference in the critical boron concentration of 17 ppm.

In a weak neutron source system, the neutron chains rarely overlap, and hence the neutrons should be described by stochastic kinetics. The probability of survival (POS) is a quantity that is often calculated in the field of stochastic kinetics and is also a simple index to test the correctness of the method of describing weak source systems. The general Monte Carlo method for calculating POS needs to control the number of simulated particles, which introduces deviations. In this work, an innovative single neutron tracking method for calculating POS based on the Reactor Monte Carlo (RMC) code is proposed, and it does not need to control the number of particles. The relative difference between the POS calculated by RMC and those calculated by MCATK are less than 6%. The new method is more accurate and user-friendly than the traditional method. (c) 2021 Elsevier Ltd. All rights reserved.

With the continuous development of supercomputing, a direct kinetic simulation based on Monte Carlo method has received more attention owing to its high fidelity. In this paper, an improved direct kinetic simulation capability is developed based on the Reactor Monte Carlo (RMC) code. Several new techniques that are different from other Monte Carlo codes have been proposed, including a new method of obtain-ing the neutron distribution at the initial moment, and a theoretically more accurate time-dependent tally method. The newly-developed kinetic capability was tested and verified using a point reactor bench-mark and a complex three-dimensional C5G7 kinetic benchmark. The numerical results demonstrate that the relative differences of the relative power at all time points between such direct kinetic simulation capability and the reference in the C5G7 benchmark are within 5.15%.(c) 2022 Elsevier Ltd. All rights reserved.

The capability of performing nuclear data sensitivity and uncertainty analysis of reaction rates has been developed in continuous-energy Reactor Monte Carlo (RMC) code. The sensitivity coefficients of reaction rates are calculated by using new generalized perturbation theory (GPT) formulation which is also implemented in McCARD. The superhistory based generalized perturbation theory (SH-GPT) formulation is developed in this paper to overcome the huge memory consumption problem encountered by the new GPT formulation. This newly developed capability is tested on several benchmark problems. The sensitivity coefficients are shown to agree within 5% of reference results for most cases. Moreover, the region-specified sensitivity analysis capability enables RMC to be the first continuous-energy Monte Carlo code that can use the first-order uncertainty quantification method to perform uncertainty analysis of the power distribution. The uncertainty results calculated by first-order uncertainty quantification method also agree well with those obtained by using stochastic sampling method. (C) 2020 Elsevier Ltd. All rights reserved.

Sensitivity analysis is an important way for us to know how the input parameters will affect the output of a system. Therefore, recently, there is an increased interest in developing sensitivity analysis methods in continuous-energy Monte Carlo Code due to the fact that Monte Carlo method can perform high-fidelity simulations of nuclear reactor. Previous studies mainly focused on developing sensitivity analysis method suitable for analyze eigenvalue. There are relatively few researches for performing sensitivity analysis of generalized response function by using continuous-energy Monte Carlo code. So, in this work, the differential operator method (DOM) has been investigated and implemented in continuous-energy Reactor Monte Carlo code (RMC) to perform sensitivity analysis of generalized response function in the form of ratios of reaction rate. The DOM implemented in RMC is based on the analog Monte Carlo transport mode and non-analog Monte Carlo transport mode. The correctness of the newly implemented method has been verified by comparing the results with those calculated by using the collision history-based method through the Jezeble and Flattop benchmark problems. In general, the results given by the DOM agree well with those obtained by the collision history-based method with an accuracy of 5%. Moreover, it is also shown that the non-analog Monte Carlo transport mode can obtain lower relative standard deviation of the sensitivity coefficients than the analog Monte Carlo transport mode.

Sensitivity and uncertainty analyses of generalized response functions are essential for nuclear designs because they provide useful information about how changes in the input parameters influence the neutronics response. Several methods have been developed to compute generalized sensitivity coefficients for nuclear data. One method is the GEneralized Adjoint Responses in Monte Carlo (GEAR-MC) method which can be used to analyze the sensitivity coefficients of reaction rate ratios. However, this method requires the generalized adjoint function to estimate the sensitivity coefficients. The generalized adjoint function can be calculated using a method like the iterated fission probability (IFP) method which has a huge memory requirement. In this paper, the superhistory-based GEAR-MC method is developed to reduce the huge memory requirement induced by calculating the generalized adjoint function. This newly developed superhistory-based GEAR-MC method is found to be accurately predict the sensitivities of the reaction rate ratios for the Godiva, Flattop and the UAM TMI PWR pin cell benchmark problems. The calculations also show that the memory usage of the superhistory-based GEAR-MC method is 98% less than for the traditional power iteration-based GEAR-MC method. In addition, it is shown that the superhistory-based GEAR-MC method will increase the runtime and this increase is not totally linearly proportional to the number of inner generations in each superhistory for all test cases. Moreover, the equivalence of GEAR-MC method and first-order differential operator method are also proved in this paper.

In a weak neutron source system, the neutron chains rarely overlap. It is necessary to describe this system using stochastic kinetics. Quantities of interest in the field of stochastic kinetics are Probability of Initiation (POI) and Probability of Survival (POS). The RMC code has the capability of calculating the POI in a static system. In this study, the calculation function of POS in dynamic systems is developed in RMC. The Monte Carlo direct simulation method is used for neutron transport in the program. The reactivity of the dynamic system changes with time through the linear interpolation method. The total number of simulated particles was controlled by the threshold and comb method. The benchmark calculated is proposed by the LANL. The calculation results of RMC and MCATK for POI are completely consistent. The calculation results of the two programs for POS are in good agreement except for 600sh(shake). Then the reason for the inconsistent between different calculation results of POS at time 600sh was analyzed and it can be found that the threshold setting in the comb method has a more significant impact on the calculation results of POS. This research is helpful for the further study of stochastic kinetics application.

With the increase interest in nuclear data sensitivity and uncertainty analysis, Many researches have developed new methods that can be used to perform sensitivity analysis for different response functions. In previous researches, several methods have been developed in the continuous-energy Reactor Monte Carlo code (RMC) to perform sensitivity analysis of the effective multiplication factor (kef reaction rate ratios and bilinear response functions. Due to the fact that the reaction rates are also common and important generalized response functions, some methods suitable for its sensitivity and uncertainty analysis need to be developed. In this work, the differential operator method was investigated and implemented in RMC to perform sensitivity analysis of reaction rate. The new capability in RMC are tested on Godiva and Flattop benchmark problems. For the Godiva benchmark, the sensitivity coefficients of fission rate and absorption rate calculated by the newly developed differential operator method in RMC are compared with the reference results from McCARD. For the Flattop benchmark, the sensitivity coefficients of fission rate and absorption rate produced by differential operator method are compared with reference results produced by the direct perturbation method. In general, results produced by the differential operator method agree well with reference results, which verifies the correctness of newly developed differential operator method in performing sensitivity analysis of reaction rates. Moreover, based on sensitivity coefficients calculated by the differential operator method (DOM), the first-order uncertainty quantification method are also developed in RMC to perform uncertainty analysis of reaction rates. The uncertainties of fission rate and absorption rate calculated by first-order uncertainty quantification method and stochastic sampling method are compared. Fairly good agreement can be observed from these uncertainty results..