• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.659-673
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• 2021

This study aims to investigate the impact of the High Pressure Selective Catalytic Reduction system (SCR-HP) on a large marine two-stroke engine performance parameters by employing thermodynamic modelling. A coupled model of the zero-dimensional type is extended to incorporate the modelling of the SCR-HP components and the Control Bypass Valve (CBV) block. This model is employed to simulate several scenarios representing the engine operation at both healthy and degraded conditions considering the compressor fouling and the SCR reactor clogging. The derived results are analysed to quantify the impact of the SCR-HP on the investigated engine performance. The SCR system pressure drop and the cylinder bypass valve flow cause an increase of the engine Specific Fuel Oil Consumption (SFOC) in the range 0.3-2.77 g/kWh. The thermal inertia of the SCR-HP is mainly attributed to the SCR reactor, which causes a delayed turbocharger response. These effects are more pronounced at low engine loads. This study supports the better understanding of the operating characteristics of marine two-stroke diesel engines equipped with the SCR-HP and quantification of the impact of the components degradation on the engine performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.205-208
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• 2008

In numerical analysis numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But most of the thermodynamic properties of the steam table are determined by experiment. Therefore they are supposed to have measurement errors. In order to make noised thermodynamic properties corresponding to errors, random numbers are generated, adjusted to appropriate magnitudes and added to original thermodynamic properties. the neural networks and quadratic spline interpolation method are introduced for function approximation of these modified thermodynamic properties in the saturated water based on pressure. It was proved that the neural networks give smaller percentage error compared with quadratic spline interpolation. From this fact it was confirmed that the neural networks trace the original values of thermodynamic properties better than the quadratic interpolation method.

• Journal of Environmental Impact Assessment
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• v.13 no.4
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• pp.213-222
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• 2004

Exergy is defined as the amount of work (entropy-free energy) a system can perform when it is brought into thermodynamic equilibrium with its environment. Exergy measures the distance from the inorganic soup in energy terms. Therefore, exergy can be considered as fuel for any system that converts energy and matter in a metabolic process. The aim of this study is to introduce structural dynamic modelling which is based on maximum exergy principle. Especially, almost ecological models couldn't explain algal succession until now. New model (structural dynamic model) is anticipated to predict or explain the succession theory. If the new concept using maximum exergy principle is used, algal succession can be explained in many actual cases. Therefore, It is estimated that structural dynamic model using maximum exergy principle might be a excellent tool to understand succession of nature from now on.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.413-418
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• 2011

The thermodynamic properties of steam table are obtained by measurement or approximate calculation under appropriate assumptions. Therefore they are supposed to have basic measurement errors. And thermodynamic properties should be modeled through function approximation for using in numerical analysis. In order to make noised thermodynamic properties corresponding to measurement errors, random numbers are generated, adjusted to appropriate magnitudes and added to original thermodynamic properties. Both neural networks and quadratic spline interpolation method are introduced for function approximation of these modified thermodynamic properties in the saturated water based on pressure and temperature. In analysis spline interpolation method gives much less relative errors than neural networks at both ends of data. Excluding the both ends of data, the relative errors of neural networks is generally within ${\pm}0.2%$ and those of spline interpolation method within ${\pm}0.5$~1.5%. This means that the neural networks give smaller relative errors compared with quadratic spline interpolation method within range of use. From this fact it was confirmed that the neural networks trace the original values better than the quadratic interpolation method and neural networks are more appropriate method in modelling the saturated steam table.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2000.04a
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• pp.196-201
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• 2000

A thermodynamic model was developed to analyze the characteristics of the heat generation during transformation of austenite in 0.186wt% and 0.458 wt%. carbon steels. The heat capacity and the heat evolved during transformation were formulated as functions of temperature and chemical composition for ferrite bainite and pearlite. in addition using the transformation dilatometer the transformation heat evolved during cooling was measured and the transformation behavior was observed. It was found that the heat capacity of ferrite was similar to those of pearlite and bainite. The heat capacity of ferrite was greater than that of bainite which was greater than that of pearlite. The molar heat of transformation to pearlite was greater than that to bainite which was greater than that to ferrite. The heats were found to be increased with decreased temperature and increasing the carbon content, It was also observed that the thermodynamic model. The heat of transformation in the higher carbon steel was greater than that in the lower carbon one. This was attributed to the lower transformation temperature and the greater amount of transformed pearlite in the higher carbon steel.

• Structural Engineering and Mechanics
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• v.33 no.4
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• pp.391-406
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• 2009

Viscoelastic materials store as well as dissipate energy to the thermal domain under deformation. Two efficient modelling techniques reported in literature use coupled (thermo-mechanical) ATF (Augmenting Thermodynamic Fields) displacements and ADF (Anelastic Displacement Fields) displacements, to represent the constitutive relationship in time domain by using certain viscoelastic parameters. Viscoelastic parameters are first extracted from the storage modulus and loss factor normally reported in hand books with the help of Genetic Algorithm and then constitutive relationships are used to obtain the equations of motion of the continuum after discretizing it with finite beam elements. The equations of motion are solved to get the frequency response function and modal damping ratio. The process may be applied to study the dynamic behaviour of composite beams and rotors comprising of several viscoelastic layers. Dynamic behaviour of a composite beam, formed by concentric layers of steel and aluminium is studied as an example.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.112-119
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• 1997

The modelling of carbon substances obtaining, for instance, carbon fibers which have high fire resistance, has been realized on the example of the polyacrylonitrile pyrolysis modelling. The pyrolysis is considered as a double step process when the formation of a liquid phase and the oxidation of substance are excluded. Three main reactions are considered: a) with the evolution of ammonia; b) with the evolution of hydrogen cyanide; c) with the evolution of hydrogen. Reactions b) and c) are sequential, and a) and b) are parallel. The problem is formulated as one-dimensional. The equations of energy, masses or concentrations, porosity and thermal conductivity are proposed. The mathematical model of the carbonization process is designed using tile kinetic characteristics of the above reactions and the thermodynamic parameters of reagents and products in these reactions. The equations received are calculated by Runge-Cutta method and by Adams method of the fourth order accuracy.

• Journal of the Korean Recycled Construction Resources Institute
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• v.5 no.4
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• pp.488-495
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• 2017

In this study, we conducted the kinetic hydration modeling of OPC and the final product according to the substitution ratio of GGBS by using the geochemical code, GEMS, in order to calculate the thermodynamic equilibrium. The thermodynamic data was used by GEMS's 3rd party database, Cemdata18, and the cement hydration model, the Parrot & Killoh model was applied to simulate the hydration process. In OPC modeling, ion concentration of pore solution and hydration products by mass and volume were observed according to time. In the GGBS modeling, as the substitution rate increases, the amount of C-S-H, which contributes the long-term strength, increases, but the amount of Portlandite decreases, which leads to carbonation and steel corrosion. Therefore, it is necessary to establish prevention of some deterioration.

• Korean Journal of Soil Science and Fertilizer
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• v.47 no.6
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• pp.443-450
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• 2014

To determine Pb species in soils following the immobilization process, sequential extraction has been used despite the possibility of overestimating Pb species from unintended reactions during chemical extraction. Meanwhile, the application of extended X-ray absorption fine structure (EXAFS) has been shown to provide a more precise result than chemical extraction. In this study, the immobilization of Pb in contaminated soils treated with liming materials such as oyster shell (OS) or eggshell (ES) was evaluated with thermodynamic modelling and EXAFS analysis. Thermodynamic modelling by visual MINTEQ predicted the precipitation of $Pb(OH)_2$ in OS and ES treated soils. In particular, the values of saturation index (SI) for $Pb(OH)_2$ in OS (SI=0.286) and ES (SI=0.453) treated soils were greater than in the control soil (SI=0.281). Linear combination fitting (LCF) analysis confirmed the presence of $C_{12}H_{10}O_{14}Pb_3$ (lead citrate, 44.7%) by citric acid from plant root, Pb-gibbsite (Pb adsorbed gibbsite, 26.4%), and Pb-kaolinite (Pb adsorbed kaolinite, 20.3%) in the control soil. On the other hand, $Pb(OH)_2$ (16.8%), Pb-gibbsite (39.3%), and Pb-kaolinite (25.6%) were observed in the OS treated soil and $Pb(OH)_2$ (55.2%) and Pb-gibbsite (33.8%) were also confirmed in the ES treated soil. Our results indicate that the treatment with OS and ES immobilizes Pb by adsorption of Pb onto the soil minerals as a result of the increase in soil negative charge and the formation of stable $Pb(OH)_2$ under high pH condition of soils.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.246-249
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• 2007

In numerical analysis for phase change material, numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But the steam table or diagram itself cannot be used without modelling. In this study applicability of neural networks in modelling superheated vapor region of water was examined by comparing with the quadratic spline. neural network consists of an input layer with 2 nodes, two hidden layers and an output layer with 3 nodes. Quadratic spline interpoation method was also applied for comparison. Neural network model revealed smaller percentage error to quadratic spline interpolation. From these results, it is confirmed that the neural networks could be powerful method in modelling the superheated range of the steam table.