• Wind and Structures
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• v.18 no.6
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• pp.693-715
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• 2014

Stochastic processes are used to represent phenomena in many diverse fields. Numerical simulation method is widely applied for the solution to stochastic problems of complex structures when alternative analytical methods are not applicable. In some practical applications the stochastic processes show non-Gaussian properties. When the stochastic processes deviate significantly from Gaussian, techniques for their accurate simulation must be available. The various existing simulation methods of non-Gaussian stochastic processes generally can only simulate super-Gaussian stochastic processes with the high-peak characteristics. And these methodologies are usually complicated and time consuming, not sufficiently intuitive. By revealing the inherent coupling effect of the phase and amplitude part of discrete Fourier representation of random time series on the non-Gaussian features (such as skewness and kurtosis) through theoretical analysis and simulation experiments, this paper presents a novel approach for the simulation of non-Gaussian stochastic processes with the prescribed amplitude probability density function (PDF) and power spectral density (PSD) by amplitude modulation and phase reconstruction. As compared to previous spectral representation method using phase modulation to obtain a non-Gaussian amplitude distribution, this non-Gaussian phase reconstruction strategy is more straightforward and efficient, capable of simulating both super-Gaussian and sub-Gaussian stochastic processes. Another attractive feature of the method is that the whole process can be implemented efficiently using the Fast Fourier Transform. Cases studies demonstrate the efficiency and accuracy of the proposed algorithm.

• Korean Journal of Construction Engineering and Management
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• v.6 no.1 s.23
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• pp.73-79
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• 2005

This paper, introduces a Stochastic Project Scheduling Simulation system (SPSS III) developed by the author to predict a project completion probability in a certain time. The system integrates deterministic CPM, probabilistic PERT, and stochastic Discrete Event Simulation (DES) scheduling methods into one system. It implements automated statistical analysis methods for computing the minimum number of simulation runs, the significance of the difference between independent simulations, and the confidence interval for the mean project duration as well as sensitivity analysis method in What-if analyzer component. The SPSS 111 gives the several benefits to researchers in that it (1) complements PERT and Monte Carlo simulation by using stochastic activity durations via a web based JAVA simulation over the Internet, (2) provides a way to model a project network having different probability distribution functions, (3) implements statistical analyses method which enable to produce a reliable prediction of the probability of completing a project in a specified time, and (4) allows researchers to compare the outcome of CPM, PERT and DES under different variability or skewness in the activity duration data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2A
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• pp.383-390
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• 2006

In this paper, a stochastic field that is compatible with Monte Carlo simulation is suggested for an expansion-based stochastic analysis scheme of weighted integral method. Through investigation on the way of affection of stochastic field function on the displacement vector in the series expansion scheme, it is noticed that the stochastic field adopted in the weighted integral method is not compatible with that appears in the Monte Carlo simulation. As generally recognized in the field of stochastic mechanics, the response variability is not a linear function of the coefficient of variation of stochastic field but a nonlinear function with increasing variability as the intensity of uncertainty is increased. Employing the stochastic field suggested in this study, the response variability evaluated by means of the weighted integral scheme is reproduced with high precision even for uncertain fields with moderately large coefficient of variation. Besides, despite the fact that only the first-order expansion is employed, an outstanding agreement between the results of expansion-based weighted integral method and Monte Carlo simulation is achieved.

• Journal of the Korea Society for Simulation
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• v.22 no.4
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• pp.21-28
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• 2013

This paper describes the method to solve the optimization problems for stochastic simulation which is represented by military simulations. For this reason, the test fitness function reflecting the characteristics of military simulations, complex and stochastic results, is defined and PSO is used to solve the test fitness function. To control the known weak point of PSO for stochastic simulations, this paper proposes a technique which reevaluates the value of global optimum. By using the technique, the result shows notable improvements. From the simulation results, interactions among the calculation conditions which affect the accuracy and speed of optimization are analyzed. And the strategy for the optimization of stochastic simulations is proposed.

• Korea-Australia Rheology Journal
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• v.14 no.4
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• pp.161-174
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• 2002

A new technique for numerical calculation of viscoelastic flow based on the combination of Neural Net-works (NN) and Brownian Dynamics simulation or Stochastic Simulation Technique (SST) is presented in this paper. This method uses a "universal approximator" based on neural network methodology in combination with the kinetic theory of polymeric liquid in which the stress is computed from the molecular configuration rather than from closed form constitutive equations. Thus the new method obviates not only the need for a rheological constitutive equation to describe the fluid (as in the original Calculation Of Non-Newtonian Flows: Finite Elements St Stochastic Simulation Techniques (CONNFFESSIT) idea) but also any kind of finite element-type discretisation of the domain and its boundary for numerical solution of the governing PDE's. As an illustration of the method, the time development of the planar Couette flow is studied for two molecular kinetic models with finite extensibility, namely the Finitely Extensible Nonlinear Elastic (FENE) and FENE-Peterlin (FENE-P) models.P) models.

• 한국전산유체공학회:학술대회논문집
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• 2008.03a
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• pp.204-207
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• 2008

Stochastic molecular dynamics simulation is a variation of standard molecular dynamics simulation that basically omits water molecules. The omission of water molecules, occupying a majority of space, enables flow simulation at microscale. This study reports our stochastic molecular dynamics simulation of particles diffusing in rectangular microchannels. We interestingly found that diffusion patterns in channels with a very small aspect ratio differ by dimensions. We will also discuss the future direction of our research toward a more realistic simulation of micromixing.

• 한국전산유체공학회:학술대회논문집
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• 2008.10a
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• pp.204-207
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• 2008

Stochastic molecular dynamics simulation is a variation of standard molecular dynamics simulation that basically omits water molecules. The omission of water molecules, occupying a majority of space, enables flow simulation at microscale. This study reports our stochastic molecular dynamics simulation of particles diffusing in rectangular microchannels. We interestingly found that diffusion patterns in channels with a very small aspect ratio differ by dimensions. We will also discuss the future direction of our research toward a more realistic simulation of micromixing.

• Korean Journal of Computational Design and Engineering
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• v.2 no.4
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• pp.215-221
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• 1997

A new approach in solving design centering problem is presented. Like most stochastic optimization problems, optimal design centering problems have intrinsic difficulties in multivariate intergration of probability density functions. In order to avoid to avoid those difficulties, genetic algorithm and very coarse Monte Carlo simulation are used in this research. The new algorithm performs robustly while producing improved yields. This result implies that the combination of robust optimization methods and approximated simulation schemes would give promising ways for many stochastic optimizations which are inappropriate for mathematical programming.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.6
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• pp.1426-1435
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• 1990

Dynamic behaviour of point tracking system mounted on flying vehicle shaking in a random manner is investigated and the system dynamic is represented by nonlinear stochastic equations. 2-D.O.F. nonlinear stochastic equations are successfully transformed to linear stochastic equations via equivalent linearization procedure in stochastic sense. Newly developed hybrid technique is used to obtain response statistics of the system under non-white random excitation, which is proved to be remarkably accurate one by performing stochastic simulation.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.1-2
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• 1994

With the prevalence of computers in modern organizations, simulation is receiving more atention as an effectvie decision -making tool. Simualtion is a computer-based numerical technique which uses mathmatical and logical models to approximate the behaviror of a real-world system. However, iptimization of synamic stochastic systems often defy analytical and algorithmic soluions. Although a simulation approach is often free fo the liminting assumption s of mathematical modeling, cost and time consiceration s make simulation the henayst's last resort. Therefore, whenever possible, analytical and algorithmica solutions are favored over simulation. This paper discussed the issues and procedrues for using simulation as a tool for optimization of stochastic complex systems that are dmodeled by computer simulation . Its emphasis is mostly on issues that are speicific to simulation optimization instead of consentrating on the general optimizationand mathematical programming techniques . A simulation optimization problem is an optimization problem where the objective function. constraints, or both are response that can only be evauated by computer simulation. As such, these functions are only implicit functions of decision parameters of the system, and often stochastic in nature as well. Most of optimization techniqes can be classified as single or multiple-resoneses techniques . The optimization of single response functins has been researched extensively and consists of many techniques. In the single response category, these strategies are gradient based search techniques, stochastic approximate techniques, response surface techniques, and heuristic search techniques. In the multiple response categroy, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphica techniqes, direct search techniques, constrained optimization techniques, unconstrained optimization techniques, and goal programming techniques. The choice of theprocedreu to employ in simulation optimization depends on the analyst and the problem to be solved. For many practival and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computersimulation is one of the most effective means of studying such complex systems. In this paper, after discussion of simulation optmization techniques, the applications of above techniques will be presented in the modeling process of many flexible manufacturing systems.