• Nuclear Engineering and Technology
• /
• v.55 no.7
• /
• pp.2474-2482
• /
• 2023

The development of fast reactors with complex designs and operation status requires more accurate and effective simulation. The Monte-Carlo method can generate multi-group cross-sections in arbitrary geometry without approximation on resonances treatment and leads to good results in combination with diffusion codes. However, in previous studies, the coupling of Monte-Carlo generated multi-group cross-sections (MC-MGXS) and transport solvers has shown relatively large biases in fast reactor problems. In this paper, the main contribution to the biases is proved to be the neglect of the angle-dependence of the total cross-sections. The flux-moment homogenization technique (MHT) is proposed to take into account this dependence. In this method, the angular dependence is attributed to the transfer cross-sections, keeping an independent form for the total sections. For the MET-1000 benchmark, the multi-group transport simulation results with MC-MGXS generated with MHT are improved by 700 pcm and an additional 120 pcm with higher order scattering. The factors that cause the residual bias are discussed. The core power distribution bias is also significantly reduced when MHT is used. It proves that the MCMGXS with MHT can be applicable with transport solvers in fast reactor analysis.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.24 no.1
• /
• pp.79-84
• /
• 2020

In this article, we implement a recently developed fast Monte Carlo simulation (MCS) for pricing equity-linked securities (ELS), which is most commonly issued autocallable structured financial derivative in South Korea, on the mobile platform. The fast MCS is based on Brownian bridge technique. Although mobile platform devices are easy to carry around, mobile platform devices are slow in computation compared to desktop computers. Therefore, it is essential to use a fast algorithm for pricing ELS on the mobile platform. The computational results demonstrate the practicability of Android application implementation for pricing ELS.

• Nuclear Engineering and Technology
• /
• v.46 no.3
• /
• pp.353-362
• /
• 2014

The effects of nuclear data uncertainties are studied on a typical PWR fuel assembly model in the framework of the OECD Nuclear Energy Agency UAM (Uncertainty Analysis in Modeling) expert working group. The "Fast Total Monte Carlo" method is applied on a model for the Monte Carlo transport and burnup code SERPENT. Uncertainties on $k_{\infty}$, reaction rates, two-group cross sections, inventory and local pin power density during burnup are obtained, due to transport cross sections for the actinides and fission products, fission yields and thermal scattering data.

• Nuclear Engineering and Technology
• /
• v.46 no.3
• /
• pp.407-412
• /
• 2014

In this research, we investigated the burnup characteristics and the conversion of fertile $^{232}Th$ into fissile $^{233}U$ in the core of a Sodium-Cooled Fast Reactor (SFR). The SFR fuel assemblies were designed for burning $^{232}Th$ fuel (fuel pin 1) and $^{233}U$ fuel (fuel pin 2) and include mixed minor actinide compositions. Monte Carlo simulations were performed using Serpent Code1.1.19 to compare with CRAM (Chebyshev Rational Approximation Method) and TTA (Transmutation Trajectory Analysis) method in the burnup calculation mode. The total heating power generated in the system was assumed to be 2000 MWth. During the reactor operation period of 600 days, the effective multiplication factor (keff) was between 0.964 and 0.954 and peaking factor is 1.88867.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.15 no.1
• /
• pp.101-113
• /
• 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
• /
• v.53 no.9
• /
• pp.2788-2802
• /
• 2021

Multigroup cross section (MG XS) generation by the UNIST in-house Monte Carlo (MC) code MCS for fast reactor analysis using nodal diffusion codes is reported. The feasibility of the approach is quantified for two sodium fast reactors (SFRs) specified in the OECD/NEA SFR benchmark: a 1000 MW_th metal-fueled SFR (MET-1000) and a 3600 MW_th oxide-fueled SFR (MOX-3600). The accuracy of a few-group XSs generated by MCS is verified using another MC code, Serpent 2. The neutronic steady-state whole-core problem is analyzed using MCS/RAST-K with a 24-group XS set. Various core parameters of interest (core k_eff, power profiles, and reactivity feedback coefficients) are obtained using both MCS/RAST-K and MCS. A code-to-code comparison indicates excellent agreement between the nodal diffusion solution and stochastic solution; the error in the core k_eff is less than 110 pcm, the root-mean-square error of the power profiles is within 1.0%, and the error of the reactivity feedback coefficients is within three standard deviations. Furthermore, using the super-homogenization-corrected XSs improves the prediction accuracy of the control rod worth and power profiles with all rods in. Therefore, the results demonstrate that employing the MCS MG XSs for the nodal diffusion code is feasible for high-fidelity analyses of fast reactors.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.20 no.4
• /
• pp.277-293
• /
• 2016

We develop an efficient numerical method for pricing the Derivative Linked Securities (DLS). The payoff structure of the hybrid DLS consists with a standard 2-Star step-down type ELS and the range accrual product which depends on the number of days in the coupon period that the index stay within the pre-determined range. We assume that the 2-dimensional Geometric Brownian Motion (GBM) as the model of two equities and a no-arbitrage interest model (One-factor Hull and White interest rate model) as a model for the interest rate. In this study, we employ the Monte Carlo simulation method with the Compute Unified Device Architecture (CUDA) parallel computing as the General Purpose computing on Graphic Processing Unit (GPGPU) technology for fast and efficient numerical valuation of DLS. Comparing the Monte Carlo method with single CPU computation or MPI implementation, the result of Monte Carlo simulation with CUDA parallel computing produces higher performance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.8
• /
• pp.3889-3903
• /
• 2017

Fast and accurate localization of randomly deployed nodes is required by many applications in wireless sensor networks (WSNs). However, mobile nodes localization in WSNs is more difficult than static nodes localization since the nodes mobility brings more data. In this paper, we propose a Range-based Monte Carlo Box (RMCB) algorithm, which builds upon the Monte Carlo Localization Boxed (MCB) algorithm to improve the localization accuracy. This algorithm utilizes Received Signal Strength Indication (RSSI) ranging technique to build a sample box and adds a preset error coefficient in sampling and filtering phase to increase the success rate of sampling and accuracy of valid samples. Moreover, simplified Particle Swarm Optimization (sPSO) algorithm is introduced to generate new samples and avoid constantly repeated sampling and filtering process. Simulation results denote that our proposed RMCB algorithm can reduce the location error by 24%, 14% and 14% on average compared to MCB, Range-based Monte Carlo Localization (RMCL) and RSSI Motion Prediction MCB (RMMCB) algorithm respectively and are suitable for high precision required positioning scenes.

• Nuclear Engineering and Technology
• /
• v.56 no.2
• /
• pp.715-727
• /
• 2024

During fast neutron imaging, besides the dark current noise and readout noise of the CCD camera, the main noise in fast neutron imaging comes from high-energy gamma rays generated by neutron nuclear reactions in and around the experimental setup. These high-energy gamma rays result in the presence of high-density gamma white spots (GWS) in the fast neutron image. Due to the microscopic quantum characteristics of the neutron beam itself and environmental scattering effects, fast neutron images typically exhibit a mixture of Gaussian noise. Existing denoising methods in neutron images are difficult to handle when dealing with a mixture of GWS and Gaussian noise. Herein we put forward a deep learning approach based on the Swin Transformer UNet (SUNet) model to remove high-density GWS-Gaussian mixture noise from fast neutron images. The improved denoising model utilizes a customized loss function for training, which combines perceptual loss and mean squared error loss to avoid grid-like artifacts caused by using a single perceptual loss. To address the high cost of acquiring real fast neutron images, this study introduces Monte Carlo method to simulate noise data with GWS characteristics by computing the interaction between gamma rays and sensors based on the principle of GWS generation. Ultimately, the experimental scenarios involving simulated neutron noise images and real fast neutron images demonstrate that the proposed method not only improves the quality and signal-to-noise ratio of fast neutron images but also preserves the details of the original images during denoising.

• Nuclear Engineering and Technology
• /
• v.31 no.4
• /
• pp.408-422
• /
• 1999

To reduce the fast neutron fluence which deteriorates the RPV integrity, additional shields were assumed to be installed at the outer core structures of the Kori Unit 1 reactor, and its reduction effects were examined. Full scope Monte Carlo simulation with MCNP4A code was made to estimate the fast neutron fluence at the RPV. An optimized design option was found from various choices in geometry and material for shield structure. It was expected that magnitude of fast neutron fluence would be reduced by 39% at the circumferential weld of the RPV, resulting in extension of plant lifetime by 4.6 EFPYs based on the criterion of PTS requirement It was investigated that the nuclear characteristics and thermal hydraulic factors at the internal core were only negligibly influenced by the installation of additional shield structure.