• Wind and Structures
• /
• 제18권6호
• /
• pp.693-715
• /
• 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.

• 한국건설관리학회논문집
• /
• 제6권1호
• /
• pp.73-79
• /
• 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.

• 대한토목학회논문집
• /
• 제26권2A호
• /
• pp.383-390
• /
• 2006

본 연구에서는 급수전개를 이용한 추계론적 유한요소해석법의 개선을 위한 등가몬테카를로 추계장함수를 제안하고 1차 Taylor전개를 이용한 추계론적 유한요소해석법인 가중적분법에 적용하였다. 일반적으로 1차 Taylor전개를 이용하는 수치해석법에서의 응답변화도는 고려하고 있는 추계장의 분산계수에 대하여 선형거동을 보인다. 그러나 몬테카를로 해석의 경우 추계장 분산계수에 대하여 비선형 거동을 나타낸다. 이는 급수전개법의 1차 Taylor전개에 따른 선형특성에 기인한다. 따라서, 가중적분법에서 사용되는 Taylor전개된 변위벡터와 몬테카를로 해석에서의 변위벡터를 비교하고 이들 두 변위벡터 사이에 상호 불일치 하는 점을 고찰하여 몬테카를로 해석에서의 변위벡터와 등가의 변위벡터를 구성하고 이를 가중적분법에 적용하였다. 제안한 등가몬테카를로 추계장은 본래의 추계장 함수에 대한 고차함수로 주어진다. 평면구조에 대한 수치해석을 통하여 제안한 등가몬테카를로 추계장을 이용한 정식화의 타당성을 고찰하였다 새로운 정식화는 기존의 l차 가중적분법을 위한 정식화 과정과 유사하게 수행할 수 있었다.

• 한국시뮬레이션학회논문지
• /
• 제22권4호
• /
• pp.21-28
• /
• 2013

일반적으로 최적화 문제에서 군사 시뮬레이션과 같이 결과가 확률적으로 나타나는 경우를 계산할 때에는 문제를 모델링 하여 일반적인 최적화 기법을 적용하는 것에 어려움이 있다. 본 논문에서는 이러한 군사 시뮬레이션의 특징을 반영하는 복잡한 반응표면을 가진 확률적 평가 함수를 정의하였다. 그리고 이러한 확률적 시뮬레이션에 대해 기존의 PSO법이 가진 약점을 보완하는 기법을 제안하였다. 제안한 기법을 이용해 평가 함수에 대한 최적화를 시행하였으며 최적화의 속도와 정확도에 영향을 미치는 계산 조건들의 상호작용을 분석하였다. 이를 통해 본 논문에서 제안한 확률적 시뮬레이션의 최적화 전략을 제시하였다.

• Korea-Australia Rheology Journal
• /
• 제14권4호
• /
• pp.161-174
• /
• 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
• /
• 한국전산유체공학회 2008년도 학술대회
• /
• pp.204-207
• /
• 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
• /
• 한국전산유체공학회 2008년 추계학술대회논문집
• /
• pp.204-207
• /
• 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.

• 한국CDE학회논문집
• /
• 제2권4호
• /
• pp.215-221
• /
• 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.

• 대한기계학회논문집
• /
• 제14권6호
• /
• pp.1426-1435
• /
• 1990

본 연구에서는 확률론적 등가선형화 기법을 사용하여 비선형 랜덤 시스템을 선형화하였다.또 이 선형화된 시스템을 최근에 새로이 제안된 방법을 적용하여 비 백색잡음형태의 랜덤 가진을 받을 때 그 거동을 구하였다.

• 한국시뮬레이션학회:학술대회논문집
• /
• 한국시뮬레이션학회 1994년도 추계학술발표회 및 정기총회
• /
• pp.1-2
• /
• 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.