• Journal of the Korean Society of Combustion
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• v.15 no.4
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• pp.22-28
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• 2010

The Direct Quadrature Method of Moments (DQMOM) has been presented for the solution of population balance equation in the wide range of the multi-phase flows. This method has the inherently interesting features which can be easily applied to the multi-inner variable equation. In addition, DQMOM is capable of easily coupling the gas phase with the discrete phases while it requires the relatively low computational cost. Soot inception, subsequent aggregation, surface growth and oxidation are described through a population balance model solved with the DQMOM for soot formation. This approach is also able to represent the evolution of the soot particle size distribution. The turbulence-chemistry interaction is represented by the laminar flamelet model together with the presumed PDF approach and the spherical harmonic P-1 approximation is adopted to account for the radiative heat transfer.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.427-432
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• 2005

This paper is concerned with a simultaneous design of TSK (Takagi-Sugeno-Kang)-linguistic fuzzy models with uncertain model output and the computationally efficient representation. For this purpose, we use the fundamental idea of linguistic models introduced by Pedrycz and develop their comprehensive design framework. The design process consists of several main phases such as (a) the automatic generation of the linguistic contexts by probabilistic distribution using CDF (conditional density function) and PDF (probability density function) (b) performing context-based fuzzy clustering preserving homogeneity based on the concept of fuzzy granulation (c) augment of bias term to compensate bias error (d) combination of TSK and linguistic context in the consequent part. Finally, we contrast the performance of the enhanced models with other fuzzy models for automobile MPG predication data and coagulant dosing process in a water purification plant.

• Proceedings of the KSME Conference
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• 2001.11b
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• pp.511-516
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• 2001

The flow characteristics in a hot mill reheating furnace is numerically simulated in this study. Navier-Stokes equations for conservation of mass, momentum, energy are solved and the standard $k-\varepsilon$ model, mixture fraction/PDF model are used for the turbulent reacting flow in the furnace. Radiation heat transfer is incorporated by the P-1 method with the absorption coefficient evaluated using WSGGM. First, simulation results are obtained for the total furnace region with existing protective dam, and then the calculations are carried out only for the preheating zone in the furnace. In that zone, additional center darn is built in order to control the flow behavior of the inlet air and the combustion gas.

• MALSORI
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• no.67
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• pp.167-180
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• 2008

This paper describes a novel spectral envelope conversion method based on Gaussian mixture model (GMM). The core of this paper is rearranging source feature vectors in input space to the transformed feature vectors in feature space for the better modeling of GMM of source and target features. The quality of statistical modeling is dependent on the distribution and the dimension of data. The proposed method transforms both of the distribution and dimension of data and gives us the chance to model the same data with different configuration. Because the converted feature vectors should be on the input space, only source feature vectors are rearranged in the feature space and target feature vectors remain unchanged for the joint pdf of source and target features using KPCA. The experimental result shows that the proposed method outperforms the conventional GMM-based conversion method in various training environment.

• Journal of the Korean Society of Combustion
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• v.5 no.2
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• pp.9-17
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• 2000

A brief introduction is given on the conditional moment closure model for turbulent nonpremixed combustion. It is based on the transport equations derived through a rigorous mathematical procedure for the conditionally averaged quantities and appropriate modeling forms for conditional scalar dissipation rate, conditional mean velocity and reaction rate. Examples are given for prediction of NO and OR in bluffbody flames, soot distribution in jet flames and autoignition of a methane/ethane jet to predict the ignition delay with respect to initial temperature, pressure and fuel composition. Conditional averaging may also be a powerful modeling concept in other approaches involved in turbulent combustion problems in various different regimes.

• Journal of Mechanical Science and Technology
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• v.16 no.7
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• pp.1009-1018
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• 2002

The Representative Interactive Flamelet (RIF) concept has been applied to numerically simulate the combustion processes and pollutant formation in the direct injection diesel engine. Due to the ability for interactively describing the transient behaviors of local flame structures with CFD solver, the RIF concept has the capabilities to predict the auto-ignition and subsequent flame propagation in the diesel engine combustion chamber as well as to effectively account for the detailed mechanisms of soot formation, NOx formation including thermal NO path, prompt and nitrous 70x formation, and reburning process. Special emphasis is given to the turbulent combustion model which properly accounts for vaporization effects on the mixture fraction fluctuations and the pdf model. The results of numerical modeling using the RIF concept are compared with experimental data and with numerical results of the commonly applied procedure which the low-temperature and high-temperature oxidation processes are represented by the Shell ignition model and the eddy dissipation model, respectively. Numerical results indicate that the RIF approach including the vaporization effect on turbulent spray combustion process successfully predicts the ignition delay time and location as well as the pollutant formation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.3
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• pp.486-495
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• 2009

This paper proposes a new methodology on reliability evaluation of a power system including solar cell generators (SCG). The SCGs using renewable energy resource such as solar radiation(SR) should be modeled as multi-state operational model because the uncertainty of the resource supply may occur an effect as same as the forced outage of generator in viewpoint of adequacy reliability of system. While a two-state model is well suited for modeling conventional generators, a multi-state model is needed to model the SCGs due to the random variation of solar radiation. This makes the method of calculating reliability evaluation indices of the SCG different from the conventional generator. After identifying the typical pattern of the SR probability distribution function(pdf) from SR actual data, this paper describes modelling, methodology and details process for reliability evaluation of the solar cell generators integrated with power system. Two test results indicate the viability of the proposed method.

• Smart Structures and Systems
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• v.21 no.5
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• Atmosphere
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• v.22 no.3
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• pp.287-298
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• 2012

The predictability of heavy precipitation over the Korean Peninsula is studied using THORPEX Interactive Grand Global Ensemble (TIGGE) data. The performance of the six ensemble models is compared through the inconsistency (or jumpiness) and Root Mean Square Error (RMSE) for MSLP, T850 and H500. Grand Ensemble (GE) of the three best ensemble models (ECMWF, UKMO and CMA) with equal weight and without bias correction is consisted. The jumpiness calculated in this study indicates that the GE is more consistent than each single ensemble model. Brier Score (BS) of precipitation also shows that the GE outperforms. The GE is used for a case study of a heavy rainfall event in Korean Peninsula on 9 July 2009. The probability forecast of precipitation using 90 members of the GE and the percentage of 90 members exceeding 90 percentile in climatological Probability Density Function (PDF) of observed precipitation are calculated. As the GE is excellent in possibility of potential detection of heavy rainfall, GE is more skillful than the single ensemble model and can lead to a heavy rainfall warning in medium-range. If the performance of each single ensemble model is also improved, GE can provide better performance.

• Journal of Korean Society on Water Environment
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• v.25 no.4
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• pp.507-514
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• 2009

In this study, a dynamic modeling scheme is presented to describe the probabilistic structure of soil water and plant water stress index under stochastic precipitation conditions. The proposed model has the form of the Fokker-Planck equation, and its applicability as a model for the probabilistic evolution of the soil water and plant water stress index is investigated under a climate change scenario. The simulation results of soil water confirm that the proposed soil water model can properly reproduce the observations and show that the soil water behaves with consistent cycle based on the precipitation pattern. The simulation results of plant water stress index show two different PDF patterns according to the precipitation. The simple impact assessment of climate change to soil water and plant water stress is discussed with Korean Meteorological Administration regional climate model.