• 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.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.109-118
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• 2004

Life cycle assessment (LCA) usually involves some uncertainty. These uncertainties are generally divided in two categories such lack of data and data inaccuracy in life cycle inventory (LCI). This paper explo.es a methodology on dealing with uncertainty due to lack of data in LCI. In order to treat uncertainty of LCI data, a model for data uncertainty is proposed. The model works with probabilistic curves as inputs and with Monte Carlo Simulation techniques to propagate uncertainty. The probabilistic curves were derived from the results of survey in expert network and Monte Carlo Simulation was performed using the derived probabilistic curves. The results of Monte Carlo Simulation were verified by statistical test. The proposed approach should serve as a guide to improve data quality and deal with uncertainty of LCI data in LCA projects.

• Electrical & Electronic Materials
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• v.10 no.9
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• pp.922-929
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• 1997

As the size of semiconductor devices shrinks in the horizontal as well as vertical dimension it is difficult to estimate the transport-velocity of electron because they drift in non-equilibrium with a few scattering. In this paper HYbrid Monte Carlo simulator which employs the drift-diffusion model for hole-transport and Monte Carlo model for electron-transport in order to reduce the simulation time and increase the accuracy as well has been developed and applied to analyze the electron-transport in InAlAs/InGaAs HBT which is attractive for an ultra high speed active device in high speed optical fiber transmission systems in terms of the velocity and energy distribution as well as cutoff frequency.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1902-1908
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• 2022

In many of the complex systems modeled in the field of nuclear engineering, it is often useful to use linear regression-based analyses to analyze relationships between model parameters and responses of interests. In cases where the response of interest is calculated by a simulation which uses Monte Carlo methods, there will be some uncertainty in the responses. Further, the reduction of this uncertainty increases the time necessary to run each calculation. This paper presents some discussion on how the Monte Carlo error in the response of interest influences the error in computed linear regression coefficients. A mathematical justification is given that shows that when performing linear regression in these scenarios, the error in regression coefficients can be largely independent of the Monte Carlo error in each individual calculation. This condition is only true if the total number of calculations are scaled to have a constant total time, or amount of work, for all calculations. An application with a simple pin cell model is used to demonstrate these observations in a practical problem.

• Journal of Korea Water Resources Association
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• v.31 no.4
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• pp.429-437
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• 1998

The numerical model named "DWOPER-LEV" for the uncertainty analysis of flood inundation is developed. DWOPER model is expanded to compute overtopping risks of levee and to predict the range of the possible flood extent. Monte-Carlo simulation is applied to examine the uncertainties in cross section geometry and Manning's roughness coefficient. The model is applied to an actual levee break of the South Han River. The risks of overtopping are computed and the possible range of inundated area and inundated depth are estimated.

• 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 Welding and Joining
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• v.18 no.3
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• pp.122-130
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• 2000

Due to the inherent dimensional uncertainty, the tolerances accumulate in the assembly of plasma cutting torch. Tolerance accumulation has serious effect on the performance of the plasma torch. This study proposes a statistical tolerance propagation model, which is based on matrix transform. This model can predict the final tolerance distributions of the completed plasma torch assembly with the prescribed statistical tolerance distribution of each part to be assembled. Verification of the proposed model was performed by making use of Monte Carlo simulation. Monte Carlo simulation generates a large number of discrete plasma torch assembly instances and randomly selects a point within the tolerance region with the prescribed statistical distribution. Monte Carlo simulation results show good agreement with that of the proposed model. This results are promising in that we can predict the final tolerance distributions in advance before assembly process of plasma torch thus provide great benefit at the assembly design stage of plasma torch.

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

• JSTS:Journal of Semiconductor Technology and Science
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• v.9 no.1
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• pp.67-74
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• 2009

Monte Carlo simulation is widely used for predicting ion implantation profiles in amorphous targets. Here, we compared Monte Carlo simulation results with a vast database of ion implantation secondary ion mass spectrometry (SIMS), and showed that the Monte Carlo data sometimes deviated from the experimental data. We modified the electron stopping power model, calibrated its parameters, and reproduced most of the database. We also demonstrated that Monte Carlo simulation can accurately predict profiles in a low energy range of around 1keV once it is calibrated in the higher energy region.

• Journal of Environmental Science International
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• v.15 no.4
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• pp.319-324
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• 2006

Release rate is one of the important items for the environmental impact assessment caused by radioactive materials in case of an accidental release from the nuclear facilities. In this study, the uncertainty of the estimated release rate is evaluated using Monte Carlo method. Gaussian plume model and linear programming are used for estimating the release rate of a source material. Tracer experiment is performed at the Yeoung-Kwang nuclear site to understand the dispersion characteristics. The optimized release rate was 1.56 times rather than the released source as a result of the linear programming to minimize the sum of square errors between the observed concentrations of the experiment and the calculated ones using Gaussian plume model. In the mean time, 95% confidence interval of the estimated release rate was from 1.41 to 2.53 times compared with the released rate as a result of the Monte Carlo simulation considering input variations of the Gaussian plume model. We confirm that this kind of the uncertainty evaluation for the source rate can support decision making appropriately in case of the radiological emergencies.