• Journal of Radiation Protection and Research
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• v.47 no.1
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• pp.50-57
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• 2022

Background: There are several proton therapy facilities in operation or planned in Taiwan, and these facilities are anticipated to not only treat cancer but also provide beam services to the industry or academia. The simplified approach based on the Monte Carlo-based data sets (source terms and attenuation lengths) with the point-source line-of-sight approximation is friendly in the design stage of the proton therapy facilities because it is intuitive and easy to use. The purpose of this study is to expand the Monte Carlo-based data sets to allow the simplified approach to cover the application of proton beams more widely. Materials and Methods: In this work, the MCNP6 Monte Carlo code was used in three simulations to achieve the purpose, including the neutron yield calculation, Monte Carlo-based data sets generation, and dose assessment in simple cases to demonstrate the effectiveness of the generated data sets. Results and Discussion: The consistent comparison of the simplified approach and Monte Carlo simulation results show the effectiveness and advantage of applying the data set to a quick shielding design and conservative dose assessment for proton therapy facilities. Conclusion: This study has expanded the existing Monte Carlo-based data set to allow the simplified approach method to be used for dose assessment or shielding design for beam services in proton therapy facilities. It should be noted that the default model of the MCNP6 is no longer the Bertini model but the CEM (cascade-exciton model), therefore, the results of the simplified approach will be more conservative when it was used to do the double confirmation of the final shielding design.

• Nuclear Engineering and Technology
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• v.50 no.6
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• pp.854-859
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• 2018

Calculating minimal cut sets is a typical quantification method used to evaluate the top event probability for a fault tree. If minimal cut sets cannot be calculated or if the accuracy of the quantification result is in doubt, the Monte Carlo method can provide an alternative for fault tree quantification. The Monte Carlo method for fault tree quantification tends to take a long time because it repeats the calculation for a large number of samples. Herein, proposal is made to improve the quantification algorithm of a fault tree with circular logic. We developed a top-down iteration algorithm that combines the characteristics of the top-down approach and the iteration approach, thereby reducing the computation time of the Monte Carlo method.

• Economic and Environmental Geology
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• v.50 no.3
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• pp.239-250
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• 2017

Physically based landslide susceptibility analysis has been recognized as an effective analysis method because it can consider the mechanism of landslide occurrence. The physically based analysis used the slope geometry and geotechnical properties of slope materials as input. However, when the physically based approach is adopted in regional scale area, the uncertainties were involved in the analysis procedure due to spatial variation and complex geological conditions, which causes inaccurate analysis results. Therefore, probabilistic method have been used to quantify these uncertainties. However, the uncertainties caused by lack of information are not dealt with the probabilistic analysis. Therefore, fuzzy set theory was adopted in this study because the fuzzy set theory is more effective to deal with uncertainties caused by lack of information. In addition, the vertex method and Monte Carlo simulation are coupled with the fuzzy approach. The proposed approach was used to evaluate the landslide susceptibility for a regional study area. In order to compare the analysis results of the proposed approach, Monte Carlo simulation as the probabilistic analysis and the deterministic analysis are used to analyze the landslide susceptibility for same study area. We found that Fuzzy Monte Carlo simulation showed the better prediction accuracy than the probabilistic analysis and the deterministic analysis.

• Nuclear Engineering and Technology
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• v.42 no.3
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• pp.271-278
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• 2010

While the deterministic lattice physics/depletion codes use leakage-corrected critical spectrum (although approximate due to the B1 buckling search employed), Monte Carlo depletion codes currently in use do not have such a feature in spite of their heterogeneity and continuous-energy modeling capability. This paper describes an approach to Monte Carlo depletion with leakage-corrected critical spectrum derived from first principles. This is based on the concept of albedo eigenvalue treated as weight of the reflected neutron in Monte Carlo simulation.

• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.103-115
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• 2022

The prediction error variances for frequencies are usually considered as unknown in the Bayesian system identification process. However, the error variances for mode shapes are taken as known to reduce the dimension of an identification problem. The present study attempts to explore the effectiveness of Bayesian approach of model parameters updating using Markov Chain Monte Carlo (MCMC) technique considering the prediction error variances for both the frequencies and mode shapes. To remove the ergodicity of Markov Chain, the posterior distribution is obtained by Gaussian Random walk over the proposal distribution. The prior distributions of prediction error variances of modal evidences are implemented through inverse gamma distribution to assess the effectiveness of estimation of posterior values of model parameters. The issue of incomplete data that makes the problem ill-conditioned and the associated singularity problem is prudently dealt in by adopting a regularization technique. The proposed approach is demonstrated numerically by considering an eight-storey frame model with both complete and incomplete modal data sets. Further, to study the effectiveness of the proposed approach, a comparative study with regard to accuracy and computational efficacy of the proposed approach is made with the Sequential Monte Carlo approach of model parameter updating.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.6
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• pp.1341-1345
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• 2005

Sequential Monte Carlo techniques are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. We can use Monte Carlo particle filters adaptively, i.e. so that they simultaneously estimate the parameters and the signal. However, Sequential Monte Carlo techniques require the use of special panicle filtering techniques which suffer from several drawbacks. We consider here an alternative approach combining particle filtering and Sequential Hybrid Monte Carlo. We give some examples of applications in fisheries(luespotted mud hopper data).

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1181-1188
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• 2017

This paper proposes the adoption of Monte Carlo perturbation theory to approximate the Jacobian matrix of coupled neutronics/thermal-hydraulics problems. The projected Jacobian is obtained from the eigenvalue decomposition of the fission matrix, and it is adopted to solve the coupled problem via the Newton method. This avoids numerical differentiations commonly adopted in Jacobian-free Newton-Krylov methods that tend to become expensive and inaccurate in the presence of Monte Carlo statistical errors in the residual. The proposed approach is presented and preliminarily demonstrated for a simple two-dimensional pressurized water reactor case study.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.790-802
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• 2022

Large-scale reactor simulation often requires the use of Monte Carlo calculation techniques to estimate important reactor parameters. One drawback of these Monte Carlo calculation techniques is they inevitably result in some uncertainty in calculated quantities. The present study includes parametric uncertainty quantification (UQ) and sensitivity analysis (SA) on the Advanced Test Reactor Critical (ATRC) facility housed at Idaho National Laboratory (INL) and addresses some complications due to Monte Carlo uncertainty when performing these analyses. This approach for UQ/SA includes consideration of Monte Carlo code uncertainty in computed sensitivities, consideration of uncertainty from directly measured parameters and a comparison of results obtained from brute-force Monte Carlo UQ versus UQ obtained from a surrogate model. These methodologies are applied to the uncertainty and sensitivity of k_eff for two sets of uncertain parameters involving fuel plate geometry and fuel plate composition. Results indicate that the less computationally-expensive method for uncertainty quantification involving a linear surrogate model provides accurate estimations for k_eff uncertainty and the Monte Carlo uncertainty in calculated k_eff values can have a large effect on computed linear model parameters for parameters with low influence on k_eff.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.3044-3050
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• 2021

This study offers a Monte Carlo alternative for computing mass energy absorption coefficients of any material through calculation of photon energy deposited per mass of the sample and the energy flux obtained inside a sample volume. This approach is applied in this study to evaluate mass energy absorption coefficients of some amino acids found in human body at twenty-eight different photon energies between 10 keV and 20 MeV. The simulations involved a pencil beam source modeled to emit a parallel beam of mono-energetic photons toward a 1 mean free path thick sample of rectangular parallelepiped geometry. All the components in the problem geometry were surrounded by a 100 cm vacuum sphere to avoid any interactions in materials other than the absorber itself. The results computed using the Monte Carlo radiation transport packages MCNP6.2 and GAMOS5.1 were checked against the theoretical values available from the tables of XMUDAT database. These comparisons indicate very good agreement and support the conclusion that Monte Carlo technique utilized in this fashion may be used as a computational tool for determining the mass energy absorption coefficients of any material whose data are not available in the literature.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2165-2170
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• 2007

At nanoscales, the Boltzmann transport equation (BTE) can best describe the behavior of phonons which are energy carriers in crystalline materials. Through this study, the phonon transport in some micro/nanoscale problems was simulated with the Monte Carlo method which is a kind of the stochastic approach to the BTE. In the Monte Carlo method, the superparticles of which the number is the weighted value to the actual number of phonons are allowed to drift and be scattered by other ones based on the scattering probability. Accounting for the phonon dispersion relation and polarizations, we have confirmed the one-dimensional transient phonon transport in ballistic and diffusion limits, respectively. The thermal conductivity for GaAs was also calculated from the kinetic theory by using the proposed model. Besides, we simulated the electrostatic discharge event in the NMOS transistor as a two-dimensional problem by applying the Monte Carlo method.