• 한국전산유체공학회:학술대회논문집
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• 2008.03a
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• pp.79-83
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• 2008

An approach to CFD code validation is developed that gives proper consideration to experimental and simulation uncertainties. The comparison errors include the difference between the data, simulation values and represents the combination of all errors. The uncertainties of modeling and numerical analysis in the CFD prediction were estimated by a Coleman's theory. In this paper, the numerical solutions are calculated by A-type standard uncertainty and Richardson extrapolation Method.

• 한국전산유체공학회:학술대회논문집
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• 2008.10a
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• pp.79-83
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• 2008

An approach to CFD code validation is developed that gives proper consideration to experimental and simulation uncertainties. The comparison errors include the difference between the data, simulation values and represents the combination of all errors. The uncertainties of modeling and numerical analysis in the CFD prediction were estimated by a Coleman's theory. In this paper, the numerical solutions are calculated by A-type standard uncertainty and Richardson extrapolation Method.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.462-465
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• 2006

This paper is aimed at proposing the sampling method to reduce variance of statistical parameters in uncertainty analysis of concrete structures. The proposed method is a modification of Latin Hypercube sampling method. This uses specially modified tables of random permutations of rank number. Also, the Spearman coefficient is used to make modified tables. Numerical analysis is carried out to predict the uncertainty of axial shortening in prestressed concrete bridge. The numerical results show that the method is efficient for uncertainty analysis of complex structural system such as prestressed concrete bridges.

• Structural Engineering and Mechanics
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• v.29 no.5
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• pp.469-488
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• 2008

Finite element methods have often been used for structural analyses of various mechanical problems. When finite element analyses are utilized to resolve mechanical systems, numerical uncertainties in the initial data such as structural parameters and loading conditions may result in uncertainties in the structural responses. Therefore the initial data have to be as accurate as possible in order to obtain reliable structural analysis results. The typical finite element method may not properly represent discrete systems when using uncertain data, since all input data of material properties and applied loads are defined by nominal values. An interval finite element analysis, which uses the interval arithmetic as introduced by Moore (1966) is proposed as a non-stochastic method in this study and serves a new numerical tool for evaluating the uncertainties of the initial data in structural analyses. According to this method, the element stiffness matrix includes interval terms of the lower and upper bounds of the structural parameters, and interval change functions are devised. Numerical uncertainties in the initial data are described as a tolerance error and tree graphs of uncertain data are constructed by numerical uncertainty combinations of each parameter. The structural responses calculated by all uncertainty cases can be easily estimated so that structural safety can be included in the design. Numerical applications of truss and frame structures demonstrate the efficiency of the present method with respect to numerical analyses of structural uncertainties.

• Industrial Engineering and Management Systems
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• v.12 no.1
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• pp.46-50
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• 2013

In insurance statistics, the sum of homogeneous insurance is often needed. The sum is called individual risk model which is a fundamental model in risk analysis for insurance. This paper first presents an individual risk model based on the uncertainty theory. Then its uncertainty distribution is provided. Finally, its arithmetic is shown by a numerical example.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2084-2093
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• 2022

Probabilistic safety assessment is widely used to quantify the risks of nuclear power plants and their uncertainties. When the lognormal distribution describes the uncertainties of basic events, the uncertainty of the top event in a fault tree is approximated with the sum of lognormal random variables after minimal cutsets are obtained, and rare-event approximation is applied. As handling complicated analytic expressions for the sum of lognormal random variables is challenging, several approximation methods, especially Monte Carlo simulation, are widely used in practice for uncertainty analysis. In this study, a theoretical approach for analyzing the sum of lognormal random variables using an efficient numerical integration method is proposed for uncertainty analysis in probability safety assessments. The change of variables from correlated random variables with a complicated region of integration to independent random variables with a unit hypercube region of integration is applied to obtain an efficient numerical integration. The theoretical advantages of the proposed method over other approximation methods are shown through a benchmark problem. The proposed method provides an accurate and efficient approach to calculate the uncertainty of the top event in probabilistic safety assessment when the uncertainties of basic events are described with lognormal random variables.

• Journal of the military operations research society of Korea
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• v.23 no.2
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• pp.45-71
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• 1997

There has been considerable research recently on uncertainty handling in the fields of artificial intelligence and knowledge-based system. Various numerical and non-numerical methods have been proposed for representing and propagating uncertainty in knowledge-based system. The Bayesian method, the Dempster-Shafer's Evidence Theory, the Certainty Factor model and the Fuzzy Set Theory are most frequently appeared in the knowledge-based system. Each of these four methods views uncertainty from a different perspective and propagates it differently. There is no single method which can handle uncertainty properly in all kinds of knowledge-based systems' domain. Therefore a knowledge-based system will work more effectively when the uncertainty handling method in the system fits to the system's environment. This paper proposed a framework for selecting proper uncertainty handling methods in knowledge-based system with respect to characteristics of problem domain and cognitive styles of experts. A schema with strategic/operational and unstructured/structured classification is employed to differenciate domain. And a schema with systematic/intuitive and preceptive/receptive classification is employed to differenciate experts' cognitive style. The characteristics of uncertainty handling methods are compared with characteristics of problem domains and cognitive styles respectively. Then a proper uncertainty handling method is proposed for each category.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.4
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• pp.321-327
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• 2014

A wide scope of numerical simulations was performed to predict model-ship resistances by using STAR-CCM+ and OpenFOAM. The numerical results were compared with experimental measurements in towing tank to analyze statistical reliability of the present simulations. Based on the normal distribution of resistance errors in 113 cases of container carriers, tankers and very large crude-oil carriers, the confidence intervals of numerical error were estimated as [-2.64%,+2.32%] and [-1.82%, +1.87%] with 95% confidence in STAR-CCM+ and OpenFOAM, respectively. The resistance errors of liquefied natural gas carriers with single- and twin-skeg were confident in the ranges of [-2.51%,+2.64%] and [-2.29%, +1.46%], respectively. The grid uncertainty of resistance coefficients for KCS was also quantitatively analyzed by using a grid verification procedure. The grid uncertainty of OpenFOAM (5.1%) was larger than 4.4% uncertainty of STAR-CCM+ although OpenFOAM provided statistically more confident results than those of STAR-CCM+. It means that a grid system verified under a specific condition does not automatically lead to statistical reliability in general cases.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.251-257
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• 2007

For the modern aircraft, uncertainty has bee an important issue to its aeroelastic stability. Therefore, many researches have been conducted regarding this topic. The uncertainties in the aeroelastic system amy consist of the structural and aerodynamic uncertainty. In this paper, we suggest a parametric uncertainty modeling and conduct the aeroelastic stability analysis of a typical wing including the uncertainty.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.3
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• pp.81-93
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• 2005

This paper models supply chain uncertainties in the dynamic Newsboy Problem context. The system consists of one supplier and one retailer who place an order to the supplier every period. Demand uncertainty is modeled as stochastic period demand, and supply uncertainty as the uncertainty in quantities delivered by the supplier. The supplier delivers exactly the amount ordered by the retailer with probability of $\beta$ and the amount minus K with probability of $(1-\beta)$ We formulate the problem as a dynamic programming problem and derive the first-order optimality condition. Through a numerical study, we measure the extent to which the cost decrease due to the reduction in supply uncertainty depends on the level of demand uncertainty. One of the most important findings In this paper is that this cost decrease is relatively small if demand uncertainty is kept high, and vice versa. We also backup this numerical result by analyzing the distribution of ending Inventory under the supply and demand uncertainties.