• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2712-2722
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• 2023

For an accurate and efficient time-dependent Monte Carlo (TDMC) neutron transport analysis, several advanced methods are newly developed and implemented in the Seoul National University Monte Carlo code, McCARD. For an efficient control of the neutron population, a dynamic weight window method is devised to adjust the weight bounds of the implicit capture in the time bin-by-bin TDMC simulations. A moving geometry module is developed to model a continuous insertion or withdrawal of a control rod. Especially, the history-based batch method for the TDMC calculations is developed to predict the unbiased variance of a bin-wise mean estimate. The developed methods are verified for three-dimensional problems in the C5G7-TD benchmark, showing good agreements with results from a deterministic neutron transport analysis code, nTRACER, within the statistical uncertainty bounds. In addition, the TDMC analysis capability implemented in McCARD is demonstrated to search the optimum detector positions for the pulsed-neutron-source experiments in the Kyoto University Critical Assembly and AGN201K.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.916-921
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• 2024

The control rod worth sensitivity to the neutron detector position in the rod drop experiment is studied by the time-dependent Monte Carlo (TDMC) neutron transport calculations for AGN-201K educational reactor and the Kyoto University Critical Assembly. The TDMC simulations of the rod drop experiments are conducted by the Seoul National University Monte Carlo (MC) code, McCARD, yielding time-dependent neutron densities at detector positions. The detector-position-dependent results of the total control rod worth calculated by the extrapolation, the integral counting, and the inverse methods are compared with the numerical reference using the MC eigenvalue calculations and the experimental results. From these comparisons, it is observed that the total control rod worth can be estimated with a considerable difference depending on the detector position through the rod drop experiment. The proposed TDMC simulation of the rod drop experiment can be applied for searching a better detector position or quantifying a bias for the control rod worth measurement.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.920-927
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• 2017

With ever-advancing computer technology, the Monte Carlo (MC) neutron transport calculation is expanding its application area to nuclear reactor transient analysis. Dynamic MC (DMC) neutron tracking for transient analysis requires efficient algorithms for delayed neutron generation, neutron population control, and initial condition modeling. In this paper, a new MC steady-state simulation method based on time-dependent MC neutron tracking is proposed for steady-state initial condition modeling; during this process, prompt neutron sources and delayed neutron precursors for the DMC transient simulation can easily be sampled. The DMC method, including the proposed time-dependent DMC steady-state simulation method, has been implemented in McCARD and applied for two-dimensional core kinetics problems in the time-dependent neutron transport benchmark C5G7-TD. The McCARD DMC calculation results show good agreement with results of a deterministic transport analysis code, nTRACER.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.811-816
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• 2022

This work presents the methodology used to expand the capabilities of the Monte Carlo code OpenMC for the calculation of reactor kinetic parameters: effective delayed neutron fraction β_eff and neutron generation time L. The modified code, OpenMC(Time-Dependent) or OpenMC(TD), was then used to calculate the effective delayed neutron fraction by using the prompt method, while the neutron generation time was estimated using the pulsed method, fitting Λ to the decay of the neutron population. OpenMC(TD) is intended to serve as an alternative for the estimation of kinetic parameters when licensed codes are not available. The results obtained are compared to experimental data and MCNP calculated values for 18 benchmark configurations.

• Nuclear Engineering and Technology
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• v.44 no.2
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• pp.161-176
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• 2012

McCARD is a Monte Carlo (MC) neutron-photon transport simulation code. It has been developed exclusively for the neutronics design of nuclear reactors and fuel systems. It is capable of performing the whole-core neutronics calculations, the reactor fuel burnup analysis, the few group diffusion theory constant generation, sensitivity and uncertainty (S/U) analysis, and uncertainty propagation analysis. It has some special features such as the anterior convergence diagnostics, real variance estimation, neutronics analysis with temperature feedback, $B_1$ theory-augmented few group constants generation, kinetics parameter generation and MC S/U analysis based on the use of adjoint flux. This paper describes the theoretical basis of these features and validation calculations for both neutronics benchmark problems and commercial PWR reactors in operation.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.1
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• pp.101-113
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• 2015

The Monte-Carlo (MC) technique is very efficient solution for statistical problem. Various MC methods can easily be applied to statistical circuit performance analysis. Recently, as the number of process parameters and their impact, has increasingly affected circuit performance, a sufficient sample size is required in order to consider high dimensionality, profound nonlinearity, and stringent accuracy requirements. Also, it is important to identify the performance of circuit as soon as possible. In this paper, Fast MC method is proposed for efficient analysis of circuit performance. The proposed method analyzes performance using enhanced-precision Latin Hypercube Sampling Monte Carlo (LHSMC). To increase the accuracy of the analysis, we calculate the effective dimension for the low discrepancy value on critical parameters. This will guarantee a robust input vector for the critical parameters. Using a 90nm process parameter and OP-AMP, we verified the accuracy and reliability of the proposed method in comparison with the standard MC, LHS and Quasi Monte Carlo (QMC).

• Nuclear Engineering and Technology
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• v.50 no.7
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• pp.1043-1050
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• 2018

For an efficient Monte Carlo (MC) burnup analysis, an accurate high-order depletion scheme to consider the nonlinear flux variation in a coarse burnup-step interval is crucial accompanied with an accurate depletion equation solver. In a Seoul National University MC code, McCARD, the high-order depletion schemes of the quadratic depletion method (QDM) and the linear extrapolation/quadratic interpolation (LEQI) method and a depletion equation solver by the Chebyshev rational approximation method (CRAM) have been newly implemented in addition to the existing constant extrapolation/backward extrapolation (CEBE) method using the matrix exponential method (MEM) solver with substeps. In this paper, the quadratic extrapolation/quadratic interpolation (QEQI) method is proposed as a new high-order depletion scheme. In order to examine the effectiveness of the newly-implemented depletion modules in McCARD, four problems in the VERA depletion benchmarks are solved by CEBE/MEM, CEBE/CRAM, LEQI/MEM, QEQI/MEM, and QDM for gadolinium isotopes. From the comparisons, it is shown that the QEQI/MEM predicts ${k_{inf}}^{\prime}s$ most accurately among the test cases. In addition, statistical uncertainty propagation analyses for a VERA pin cell problem are conducted by the sensitivity and uncertainty and the stochastic sampling methods.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.12
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• pp.1758-1765
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• 2003

Monte Carlo simulation(MC) method was used as an uncertainty assessment tool for gas flow measurement in this paper. Uncertainty sources for gas flow measurement were analyzed, and probability distribution characteristics of each source were discussed. Detailed MC methodology was described including the effect of the number of simulation. The uncertainty result was compared with that of the conventional sensitivity coefficient method, and it was revealed that the results were different from each other for this particular gas flow measurement case of which the modelling equation was nonlinear. The MC was comparatively simple, convenient and accurate as an uncertainty assessment method, especially in cases of complex, nonlinear measurement modelling equations. It was noted that the uncertainty assessment method should be selected carefully according to the mathematical characteristics of the measurement.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2004.11a
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• pp.43-46
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• 2004

The commissioning of a treatment planning system of model-based dose calculation algorithm requires a lot of parameters to be selected to fit measured data, in which process physical insights for the parameters are often forgotten. We present the photon beam commissioning of Pinnacle$^3$ with the help of Monte Carlo (MC) simulation and evaluate the parameters Pinnacle$^3$ demands. Even though the MC calculation produces reasonable values for the commissioning, the thorough physical basis of the Pinnacles$^3$'s commissioning process is needed to use the MC derived parameters directly.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1172-1180
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• 2017

The application of Monte Carlo (MC) to large-scale fixed-source problems has recently become possible with new hybrid methods that automate generation of parameters for variance reduction techniques. Two common variance reduction techniques, weight windows and source biasing, have been automated and popularized by the consistent adjoint-driven importance sampling (CADIS) method. This method uses the adjoint solution from an inexpensive deterministic calculation to define a consistent set of weight windows and source particles for a subsequent MC calculation. One of the motivations for source consistency is to avoid the splitting or rouletting of particles at birth, which requires computational resources. However, it is not always possible or desirable to implement such consistency, which results in inconsistent source biasing. This paper develops an original framework that mathematically expresses the coupling of the weight window and source biasing techniques, allowing the authors to explore the impact of inconsistent source sampling on the variance of MC results. A numerical experiment supports this new framework and suggests that certain classes of problems may be relatively insensitive to inconsistent source sampling schemes with moderate levels of splitting and rouletting.