• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.E
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• pp.59-66
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• 1990

Abstract The formulation of the probabilistic finite element method was briefly reviewed. The method was implemented into a computer program for frame analysis which has the same analogy as finite element analysis. Another program for Monte Carlo simulation of finite element analysis was written. Two sample structures were assumed and analized. The characteristics of the second moment statistics obtained by the probabilistic finite element method was examined through numerical studies. The applicability and limitation of the method were also evaluated in comparison with the data generated by Monte Carlo simulation.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.1-20
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• 2009

The present contribution addresses uncertainty quantification and uncertainty propagation in structural mechanics using stochastic analysis. Presently available procedures to describe uncertainties in load and resistance within a suitable mathematical framework are shortly addressed. Monte Carlo methods are proposed for studying the variability in the structural properties and for their propagation to the response. The general applicability and versatility of Monte Carlo Simulation is demonstrated in the context with computational models that have been developed for deterministic structural analysis. After discussing Direct Monte Carlo Simulation for the assessment of the response variability, some recently developed advanced Monte Carlo methods applied for reliability assessment are described, such as Importance Sampling for linear uncertain structures subjected to Gaussian loading, Line Sampling in linear dynamics and Subset simulation. The numerical example demonstrates the applicability of Line Sampling to general linear uncertain FE systems under Gaussian distributed excitation.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.193-196
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• 1987

We reported that hot electron phenomena in submicron nMOSFET by Monte Carlo method. In order to predict the influence of the hot electron effects on the device reliability, either simple analytical model or a complete two dimensional numerical simulation has been adopted. Results of numerical simulation, based on the static mobility model, may be inaccurate when gate length of MOSFET is scaled down to less than 1um. Most of device simulation packages utilize the static nobility model. Monte Carlo method based on stochastic analysis of carrier movement may be a powerful tool to characterize hot electrons. In this work, energy and velocity distribution of carriers were obtained to predict the relative degree of short channel effects for different device parameters. Our analysis shows a few interesting results when $V_{ds}$ is 5 volt, average electron energy does not increase with gate bias as evidenced by substrate current.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.1095-1099
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• 2017

A fission source can act as a stabilization element in coupled Monte Carlo simulations. We have observed this while studying numerical instabilities in nonlinear steady-state simulations performed by a Monte Carlo criticality solver that is coupled to a xenon feedback solver via fixed-point iteration. While fixed-point iteration is known to be numerically unstable for some problems, resulting in large spatial oscillations of the neutron flux distribution, we show that it is possible to stabilize it by reducing the number of Monte Carlo criticality cycles simulated within each iteration step. While global convergence is ensured, development of any possible numerical instability is prevented by not allowing the fission source to converge fully within a single iteration step, which is achieved by setting a small number of criticality cycles per iteration step. Moreover, under these conditions, the fission source may converge even faster than in criticality calculations with no feedback, as we demonstrate in our numerical test simulations.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.139-155
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• 2000

An exclusive simulation study is conducted in computing means for order statistics in standard normal variate. Monte Carlo moments are used in Shapiro-Francia W' statistic computation. Finally, quantiles for Shapiro-Francia W' are generated. The study shows that in computing means for order statistics in standard normal variate, complicated distributions and intensive numerical integrations can be avoided by using Monte Carlo simulation. Lack of accuracy is minimal and computation simplicity is noteworthy.

• 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.

• Journal of Ocean Engineering and Technology
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• v.28 no.6
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• pp.566-573
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• 2014

This paper presents the results of an integrated navigation performance analysis for selecting the sensor of an unmanned underwater vehicle (UUV) using Monte Carlo numerical simulation. An inertial measurement unit (IMU) and Doppler velocity log (DVL) are considered to build the integrated navigation system. The position error and price of the sensor are selected as performance indices to evaluate the volunteer integrated navigation systems. Monte-Carlo simulation is introduced to analyze the circular error probability (CEP) and its variance. Simulation results provide the proper sensor combination for integrated navigation in relation to the performance and price.

• Journal of KSNVE
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• v.10 no.6
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• pp.1041-1047
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• 2000

The main objectives of this study are to examine the random response of a vibration absorber system with autoparametric coupling in the neighborhood of internal resonance by Gaussian closure and to compare the results with those obtained by Monte Carlo simulation. The numerical simulation is found to support the main features of the nonlinear modal interaction in the neighborhood of internal resonance conditions. While the Gaussian closure exhibits regions of multiple solutions in the neighborhood of internal resonance, the numerical simulation gives only one solution depending on the assigned initial conditions. The on-off intermittency phenomena of the cantilever mode is observed in the Monte Carlo simulation over a small range of parameter.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.5
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• pp.21-28
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• 2006

Vibration of a non-linear system with multiple degrees of freedom under random parametric excitations was evaluated by probabilistic method. The non-linear characteristic terms of system structure were quasi-linearized and excitation terms were remained as they were. An analytical method where the expectation values of square mean of error was minimized was used. The numerical results were compared with those obtained by Monte Carlo simulation. A linear congruential generator and Box-Muller method were used in Monte Carlo simulation. The comparison showed the results by probabilistic method agreed well with those by Monte Carlo simulation.

• Journal of Korea Technology Innovation Society
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• v.7 no.3
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• pp.533-554
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• 2004

Real option valuation considers the managerial flexibility to make ongoing decisions regarding implementation of investment projects and deployment of real assets. The appeal of the framework is natural given the high degree of uncertainty that firms face in their technology investment decisions. This paper suggests an algorithm for estimating volatility of logarithmic cash flow returns of real asset based on Monte Carlo simulation. This research uses a binomial model to obtain point estimate of real option value with embedded expansion option case and provides also an array of numerical results to show the interval estimation of option value using Monte Carlo simulation.