• 한국연소학회:학술대회논문집
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• 2000.05a
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• pp.67-75
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• 2000

Computational fluid dynamics(CFD) analysis of the thermal flow in a municipal solid waste(MSW) incinerator combustion chamber provides crucial insight on the incinerator performance. However, the combustion of the waste bed is typically treated as an arbitrarily selected profile of combustion gas. A strategy for simultaneous simulation of the waste bed combustion and the thermal flow fields in the furnace chamber was introduced to substitute the simple inlet condition. A waste bed combustion model was constructed to predict the progress of combustion in the bed and corresponding generation of the gas phase species, which assumes the moving bed as a packed bed of homogeneous fuel particles. When coupled with CFD, it provides boundary conditions such as gas temperature and species distribution over the grate, and receives radiative heat flux from CFD. The combined simulation successfully predicted the physical processes of the waste bed combustion and its interaction with the flow fields for various design and operating parameters, which was limited in the previous CFD simulations.

• Structural Engineering and Mechanics
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• v.52 no.1
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• pp.97-113
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• 2014

In the present work appropriate concrete material models have been proposed to predict drying shrinkage and specific creep of High-performance concrete (HPC) using Artificial Neural Network (ANN). The ANN models are trained, tested and validated using 106 different experimental measured set of data collected from different literatures. The developed models consist of 12 input parameters which include quantities of ingredients namely ordinary Portland cement, fly ash, silica fume, ground granulated blast-furnace slag, water, and other aggregate to cement ratio, volume to surface area ratio, compressive strength at age of loading, relative humidity, age of drying commencement and age of concrete. The Feed-forward backpropagation networks with Levenberg-Marquardt training function are chosen for proposed ANN models and same implemented on MATLAB platform. The results shows that the proposed ANN models are more rational as well as computationally more efficient to predict time-dependent properties of drying shrinkage and specific creep of HPC with high level accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1892-1896
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• 2005

In this paper, an effective method for selecting significant input variables in building ANFIS (Adaptive Neuro-Fuzzy Inference System) for nonlinear system modeling is proposed. Dominant inputs in a nonlinear system identification process are extracted by evaluating the performance index and they are applied to ANFIS. The availability of our proposed model is verified with the Box and Jenkins gas furnace data. The comparisons with other methods are also given in this paper to show our proposed method is superior to other models.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.283-285
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• 2002

Boiler combustion systems are large, non-linear systems with numerous interactions between its component parts. In the analysis of such complex systems. dynamic simulation is recognized as a powerful method of keeping track of the myriad of interactions. This paper shows and discusses the developed analysis model, such as the forced draft fan the primary air fan, the furnace and burner system, pulverizer, air preheater and induced draft fan, etc. in accordance with BMCR condition of boiler using the Modular Modeling System(MMS) software.

• Proceedings of the KIEE Conference
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• 1999.07f
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• pp.2575-2577
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• 1999

This paper describes the engineering process for analyzing the simulation result and deciding the site in which Distribution STATCON operates more effectively. For this purpose the modeling method of industrial loads, equipments and STATCON was represented. Models of motor, furnace and so on are presented for the modeling of industrial loads. The distribution system models include the parameters of the distribution line and transformer. The models of PESS(Power Electronics Subsystem), controllers and maginetics are consist of STATCON model.

• Journal of Mechanical Science and Technology
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• v.19 no.4
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• pp.1006-1017
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• 2005

In this work, we developed an accurate and efficient radiative finite volume method applicable for the complex 2D planar and 3D geometries using an unstructured-grid finite volume method. The present numerical model has fully been validated by several benchmark cases including the radiative heat transfer in quadrilateral enclosure with isothermal medium, tetrahedral enclosure, a three-dimensional idealized furnace, as well as convection-coupled radiative heat transfer in a square enclosure. The numerical results for all cases are well agreed with the previous results. Special emphasis is given to the parallelization of the unstructured-grid radiative FVM using the domain decomposition approach. Numerical results indicate that the present parallel unstruc­tured-grid FVM has the good performance in terms of accuracy, geometric flexibility, and computational efficiency.

• Elastomers and Composites
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• v.52 no.2
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• pp.131-135
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• 2017

Zinc nitrate hexahydrate [$Zn(NO_3){\cdot}6H_2O$] and sodium hydroxide [NaOH] were used as source reagents in the preparation of ZnO nanoparticles in an aqueous solution containing deionized water and ethanol in a ratio of 2:5 (v/v). ZnO nanoparticles were heated in an electric furnace at $700^{\circ}C$ for 2 h under an atmosphere of inert argon gas. The morphological and structural properties of the nanoparticles were characterized by scanning electron microscopy (SEM) and powder X-ray diffractometry (XRD). UV-vis spectrophotometry was used to analyze the photocatalytic degradation of 3-nitrophenol with ZnO nanoparticles as photocatalyst under ultraviolet irradiation at 254 nm. Evaluation of the kinetic of the photo-catalytic degradation of 3-nitrophenol indicated that the degradation of 3-nitrophenol with ZnO nanoparticles obeyed the pseudo-first order reaction rate model.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.4
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• pp.61-68
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• 2009

This paper presents the numerical analysis of process of sludge drying to know the characteristics of design parameters and develop the new process plant. Finite volume method and $k-{\varepsilon}$ turbulence model were used to analogy the sludge drying furnace. It has been attempted to perform the disposal of sewage sludge such as simple reclaiming and dumping in sea and incineration. Currently, these methods are restricted by national or international government regulations. The drying process is adopted as an effective method for sewage sludge treatment. However sewage sludge makes it difficult to treat with a large volume at the real drying process plant because of its own complicated physical, chemical, and thermal properties. The final design value of moisture content with 10% of the dried sludge can be obtained through the simulated outputs in this study.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.05a
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• pp.152-153
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• 2017

In the construction site, it is necessary to estimate the compressive strength of concrete in order to adjust the demolding time of the form, and establish and adjust the construction schedule. The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, six influential factors (Water, Cement, Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at three conferences in order to know the various correlations among data and the tendency of data. After using algorithm of various methods of machine learning techniques, we selected the most suitable regression analysis model for estimating the compressive strength.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.15-15
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• 2000

In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fused models have been proposed to implement different types of fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problem. To overcome the problem, we propose the self-organizing networks with activation nodes based on fuzzy inference and polynomial function. The proposed model consists of two parts, one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules, and its fuzzy system operates with Gaussian or triangular MF in Premise part and constant or regression polynomials in consequence part. the other is polynomial nodes which several types of high-order polynomials such as linear, quadratic, and cubic form are used and are connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method, time series data for gas furnace process has been applied.