• Journal of the Korean Society of Propulsion Engineers
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• v.9 no.2
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• pp.97-104
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• 2005

A simple but efficient grid generation technique by using the modified compressible form of stream function has been formulated. Transformation of a physical plane to a streamline plane, the Von Mises Transformation, has been widely used to solve the differential equations governing flow phenomena, however, limitation arises in low velocity region of boundary layer, mixing layer and wake region where the relatively large grid spacing is inevitable. Modified Von Mises Transformation with simple mathematical adjustment for the stream function is suggested and applied to solve the confined coaxial turbulent jet mixing with simple $\kappa-\epsilon$ turbulence model. Comparison with several experimental data of axial mean velocity, turbulent kinetic energy, and Reynolds shear stress distribution shows quite good agreement in the mixing layer except in the centerline where the turbulent kinetic energy distributions were somewhat under estimated. This formulation is strongly suggested to be utilized specially for free turbulent mixing layers in axisymmetric flow conditions such as the investigation of mixing behavior, jet noise production and reduction for Turbofan engines.

• Communications for Statistical Applications and Methods
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• v.21 no.6
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• pp.539-559
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• 2014

We derive the exact distribution of summation for random samples from uniform distribution and then compare the exact distribution with the approximated normal distribution obtained by the central limit theorem. To check the similarity between two distributions, we consider five existing normality tests based on the difference between the target normal distribution and empirical distribution: Anderson-Darling test, Kolmogorov-Smirnov test, Cramer-von Mises test, Shapiro-Wilk test and Shaprio-Francia test. For the purpose of comparison, those normality tests are applied to the simulated data. It can sometimes be difficult to derive an exact distribution. Thus, we try two different transformations to find out which transform is easier to get the exact distribution in terms of calculation complexity. We compare two transformations and comment on the advantages and disadvantages for each transformation.

• Nuclear Engineering and Technology
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• v.16 no.1
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• pp.29-35
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• 1984

Filtration efficiency tests were conducted on a fiberglass mat filter with DOP aerosol having a diameter from 0.l${\mu}{\textrm}{m}$ to 0.45${\mu}{\textrm}{m}$ in the face velocity range of 1cm/sec to 10cm/sec. Filtration of submicron particles by a fibrous filter is characterized by a face velocity. The size of DOP aerosol which has a minimum removal efficiency decreases with increasing the velocity. A numerical solution of the diffusion equation is obtained for a fiberglass mat filter by using "Kuwabara's cell model" for the flow field and Von Mises Transformation for the actual flow around a fiberglass. The present theoretical results agree quite well with the experimentals for fiberglass mat. This result could be contributed to predict the removal efficiency on an air filter and to optimize the operating condition of an air purification system with a filter. a filter.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.797-810
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• 2017

The Korean peninsula is exposed to typhoons every year. Typhoons cause huge socioeconomic damage because tropical cyclones tend to occur with strong winds and heavy precipitation. In order to understand the complex dependence structure between strong winds and heavy precipitation, the copula links a set of univariate distributions to a multivariate distribution and has been actively studied in the field of hydrology. In this study, we carried out analysis using data of wind speed and precipitation collected from the weather stations in Busan and Jeju. Log-Normal, Gamma, and Weibull distributions were considered to explain marginal distributions of the copula. Kolmogorov-Smirnov, Cramer-von-Mises, and Anderson-Darling test statistics were employed for testing the goodness-of-fit of marginal distribution. Observed pseudo data were calculated through inverse transformation method for establishing the copula. Elliptical, archimedean, and extreme copula were considered to explain the dependence structure between strong winds and heavy precipitation. In selecting the best copula, we employed the Cramer-von-Mises test and cross-validation. In Busan, precipitation according to average wind speed followed t copula and precipitation just as maximum wind speed adopted Clayton copula. In Jeju, precipitation according to maximum wind speed complied Normal copula and average wind speed as stated in precipitation followed Frank copula and maximum wind speed according to precipitation observed Husler-Reiss copula.

• International Journal of Precision Engineering and Manufacturing
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• v.4 no.6
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• pp.12-19
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• 2003

This paper presents the effect of boundary conditions in various failure pressure models published for the estimation of failure pressure. Furthermore, this approach is extended to the failure prediction with the aid of a failure probability model. The first order Taylor series expansion of the limit state function is used in order to estimate the probability of failure associated with each corrosion defect in buried pipelines for long exposure period with unit of years. A failure probability model based on the von-Mises failure criterion is adapted. The log-normal and standard normal probability functions for varying random variables are adapted. The effects of random variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress, material ultimate tensile strength and pipe thickness on the failure probability of the buried pipelines are systematically investigated for the corrosion pipeline by using an adapted failure probability model and varying failure pressure model.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.191-213
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• 2023

Unit distributions are frequently used in probability theory and statistics to depict meaningful variables having values between zero and one. Using convenient transformation, the unit inverse exponentiated weibull (UIEW) distribution, which is equally useful for modelling data on the unit interval, is proposed in this study. Quantile function, moments, incomplete moments, uncertainty measures, stochastic ordering, and stress-strength reliability are among the statistical properties provided for this distribution. To estimate the parameters associated to the recommended distribution, well-known estimation techniques including maximum likelihood, maximum product of spacings, least squares, weighted least squares, Cramer von Mises, Anderson-Darling, and Bayesian are utilised. Using simulated data, we compare how well the various estimators perform. According to the simulated outputs, the maximum product of spacing estimates has lower values of accuracy measures than alternative estimates in majority of situations. For two real datasets, the proposed model outperforms the beta, Kumaraswamy, unit Gompartz, unit Lomax and complementary unit weibull distributions based on various comparative indicators.