• Title/Summary/Keyword: non-linear model

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Mean pressure prediction for the case of 3D unsteady turbulent flow past isolated prismatic cylinder

  • Ramesh, V.;Vengadesan, S.;Narasimhan, J.L.
    • Wind and Structures
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    • v.9 no.5
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    • pp.357-367
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    • 2006
  • Unsteady 3D Reynolds Averaged Navier-Stokes (URANS) solver is used to simulate the turbulent flow past an isolated prismatic cylinder at Re=37,400. The aspect ratio of height to base width of the body is 5. The turbulence closure is achieved through a non-linear $k-{\varepsilon}$ model. The applicability of this model to predict unsteady forces associated with this flow is examined. The study shows that the present URANS solver with standard wall functions predicts all the major unsteady phenomena showing closer agreement with experiment. This investigation concludes that URANS simulations with the non-linear $k-{\varepsilon}$ model as a turbulence closure provides a promising alternative to LES with view to study flows having complex features.

A Non-linear Model for Dynamic Analysis of Reactor Internals (원자로내부구조물의 동적해석을 위한 비선형모델)

  • Myung-J.Jhun;Sang-G.Chang;Song, Heuy-G.
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 1993.04a
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    • pp.165-172
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    • 1993
  • A non-linear mathematical model has been developed for the dynamic analysis of the reactor internals. The model includes a lumped mass and stiffness with non-linear members such as gap-spring. As hydrodynamic couplings have also been considered in the model, the effect of fluid/structure interaction between internals components due to their immersion in a confining fluid can be studied for the dynamic response analysis. The reactor internals responses for seismic and pipe break excitations have been calculated for the case of with-and without-hydrodynamic couplings.

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Bayes Prediction Density in Linear Models

  • Kim, S.H.
    • Communications for Statistical Applications and Methods
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    • v.8 no.3
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    • pp.797-803
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    • 2001
  • This paper obtained Bayes prediction density for the spatial linear model with non-informative prior. It showed the results that predictive inferences is completely unaffected by departures from the normality assumption in the direction of the elliptical family and the structure of prediction density is unchanged by more than one additional future observations.

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Estimates the Non-Stationary Probable Precipitation Using a Power Model (Power 모형을 이용한 비정상성 확률강수량 산정)

  • Kim, Gwangseob;Lee, Gichun;Kim, Beungkown
    • Journal of The Korean Society of Agricultural Engineers
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    • v.56 no.4
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    • pp.29-39
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    • 2014
  • In this study, we performed a non-stationary frequency analysis using a power model and the model was applied for Seoul, Daegu, Daejeon, Mokpo sites in Korea to estimate the probable precipitation amount at the target years (2020, 2050, 2080). We used the annual maximum precipitation of 24 hours duration of precipitation using data from 1973 to 2009. We compared results to that of non-stationary analyses using the linear and logistic regression. The probable precipitation amounts using linear regression showed very large increase in the long term projection, while the logistic regression resulted in similar amounts for different target years because the logistic function converges before 2020. But the probable precipitation amount for the target years using a power model showed reasonable results suggesting that power model be able to reflect the increase of hydrologic extremes reasonably well.

Curve Estimation among Citation and Centrality Measures in Article-level Citation Networks (문헌 단위 인용 네트워크 내 인용과 중심성 지수 간 관계 추정에 관한 연구)

  • Yu, So-Young
    • Journal of the Korean Society for information Management
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    • v.29 no.2
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    • pp.193-204
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    • 2012
  • The characteristics of citation and centrality measures in citation networks can be identified using multiple linear regression analyses. In this study, we examine the relationships between bibliometric indices and centrality measures in an article-level co-citation network to determine whether the linear model is the best fitting model and to suggest the necessity of data transformation in the analysis. 703 highly cited articles in Physics published in 2004 were sampled, and four indicators were developed as variables in this study: citation counts, degree centrality, closeness centrality, and betweenness centrality in the co-citation network. As a result, the relationship pattern between citation counts and degree centrality in a co-citation network fits a non-linear rather than linear model. Also, the relationship between degree and closeness centrality measures, or that between degree and betweenness centrality measures, can be better explained by non-linear models than by a linear model. It may be controversial, however, to choose non-linear models as the best-fitting for the relationship between closeness and betweenness centrality measures, as this result implies that data transformation may be a necessary step for inferential statistics.

Bayesian Estimation Procedure in Multiprocess Non-Linear Dynamic Normal Model

  • Sohn, Joong-Kweon;Kang, Sang-Gil
    • Communications for Statistical Applications and Methods
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    • v.3 no.1
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    • pp.155-168
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    • 1996
  • In this paper we consider the multiprocess dynamic normal model with parameters having a time dependent non-linear structure. We develop and study the recursive estimation procedure for the proposed model with normality assumption. It turns out thst the proposed model has nice properties such as insensitivity to outliers and quick reaction to abrupt changes of pattern.

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Large Robust Designs for Generalized Linear Model

  • Kim, Young-Il;Kahng, Myung-Wook
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.2
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    • pp.289-298
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    • 1999
  • We consider a minimax approach to make a design robust to many types or uncertainty arising in reality when dealing with non-normal linear models. We try to build a design to protect against the worst case, i.e. to improve the "efficiency" of the worst situation that can happen. In this paper, we especially deal with the generalized linear model. It is a known fact that the generalized linear model is a universal approach, an extension of the normal linear regression model to cover other distributions. Therefore, the optimal design for the generalized linear model has very similar properties as the normal linear model except that it has some special characteristics. Uncertainties regarding the unknown parameters, link function, and the model structure are discussed. We show that the suggested approach is proven to be highly efficient and useful in practice. In the meantime, a computer algorithm is discussed and a conclusion follows.

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Significance of seabed interaction on fatigue assessment of steel catenary risers in the touchdown zone

  • Elosta, Hany;Huang, Shan;Incecik, Atilla
    • Structural Engineering and Mechanics
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    • v.57 no.3
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    • pp.403-423
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    • 2016
  • The challenges involved with fatigue damage assessment of steel catenary riser (SCR) in the touchdown zone (TDZ) are primarily due to the non-linear behaviour of the SCR-seabed interaction, considerable uncertainty in SCR-seabed interaction modelling and geotechnical parameters. The issue of fatigue damage induced by the cyclic movements of the SCR with the seabed has acquired prominence with the touch down point (TDP) interaction in the TDZ. Therefore, the SCR-seabed response is critical for reliable estimation of fatigue life in the TDZ. Various design approaches pertaining to the lateral pipe-soil resistance model are discussed. These techniques have been applied in the finite element model that can be used to analyse the lateral SCR-seabed interaction under hydrodynamic loading. This study investigates the sensitivity of fatigue performance to geotechnical parameters through a parametric study. In this study, global analyses are performed to assess the influence of vertical linear seabed springs, the lateral seabed model and the non-linear seabed model, including trench evolution into seabed, seabed normalised stiffness, re-penetration offset parameter and soil suction resistance ratio, on the fatigue life of SCRs in the TDZ.

A Note on Linear Regression Model Using Non-Symmetric Triangular Fuzzy Number Coefficients

  • Hong, Dug-Hun;Kim, Kyung-Tae
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.2
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    • pp.445-449
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    • 2005
  • Yen et al. [Fuzzy Sets and Systems 106 (1999) 167-177] calculated the fuzzy membership function for the output to find the non-symmetric triangular fuzzy number coefficients of a linear regression model for all given input-output data sets. In this note, we show that the result they obtained in their paper is invalid.

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Efficient Prediction in the Semi-parametric Non-linear Mixed effect Model

  • So, Beong-Soo
    • Journal of the Korean Statistical Society
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    • v.28 no.2
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    • pp.225-234
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    • 1999
  • We consider the following semi-parametric non-linear mixed effect regression model : y\ulcorner=f($\chi$\ulcorner;$\beta$)+$\sigma$$\mu$($\chi$\ulcorner)+$\sigma$$\varepsilon$\ulcorner,i=1,…,n,y*=f($\chi$;$\beta$)+$\sigma$$\mu$($\chi$) where y'=(y\ulcorner,…,y\ulcorner) is a vector of n observations, y* is an unobserved new random variable of interest, f($\chi$;$\beta$) represents fixed effect of known functional form containing unknown parameter vector $\beta$\ulcorner=($\beta$$_1$,…,$\beta$\ulcorner), $\mu$($\chi$) is a random function of mean zero and the known covariance function r(.,.), $\varepsilon$'=($\varepsilon$$_1$,…,$\varepsilon$\ulcorner) is the set of uncorrelated measurement errors with zero mean and unit variance and $\sigma$ is an unknown dispersion(scale) parameter. On the basis of finite-sample, small-dispersion asymptotic framework, we derive an absolute lower bound for the asymptotic mean squared errors of prediction(AMSEP) of the regular-consistent non-linear predictors of the new random variable of interest y*. Then we construct an optimal predictor of y* which attains the lower bound irrespective of types of distributions of random effect $\mu$(.) and measurement errors $\varepsilon$.

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