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COMPARITIVE STUDY OF PERFORMANCE CHARACTERSISTICS OF IGCC AND IGFC
최정일(J. I. Choi),아가라자(A. R. Abid),손정락(J. L. Sohn),송성진(S. J. Song) 한국유체기계학회 2010 유체기계 연구개발 발표회 논문집 Vol.2010 No.12
An IGCC (Integrated Gasification Combined Cycle) system is the system that utilizes coal in the form of Syngas as fuel in a combined energy cycle. An IGFC (Integrated Gasification Fuel Cell) system is a high efficiency power generation system that is composed of IGCC and SOFC (Solid Oxide Fuel Cell). The first part of this study is the modeling of commercially available IGCC. The performance results thus acquired are verified with the Tampa IGCC model. Secondly, this study investigates the integration of SOFC into IGCC and the operating condition for higher performance of the system. This is done by first developing a model for SOFC and then integrating it with the IGCC model. A new IGFC system is suggested to satisfy the constraints of both IGCC and SOFC. A comparative study of performance characteristics of the two systems IGCC and IGFC is done. The results acquired from the IGFC are compared with the commercial IGCC results. When the same amount of coal is used in the IGFC, the efficiency is 46.9% which is 10.2% higher than the IGCC. The appropriate amount of coal for system performance by maximizing SOFC performance is investigated and the IGFC with additional fuel has the efficiency of 55.8%.
전기자동차 모터 유냉식 방열성능 향상을 위한 열전달 해석 및 최적화
장준호(J.H. Jang),오근우(G.W. Oh),Xiang Sun,김종수(J.S. Kim),곽태희(T.H. Kwak),최정일(J.-I. Choi) 한국전산유체공학회 2020 한국전산유체공학회지 Vol.25 No.4
This paper presents the heat transfer analysis of the electric vehicle motor and optimization of an operating parameter for improving the performance of the motor with an oil cooling system. The homogeneous multi-phase flow model in commercial software, ANSYS CFX, is applied to solve the flow of mixture, including oil and air. We mainly focus on the influence of operating (oil level) and driving conditions (roll and pitch angles) on the cooling system. Several CFD simulations are performed with various input parameters (oil level, roll, and pitch angles) generated from the Latin Hypercube Sampling method. Based on the CFD results, we built a Polynomial Chaos Expansions based surrogate model to predict the temperature distribution, maximum temperature, and torque loss, which respond to the input parameters. Sensitivity analysis indicates that the oil level significantly affects system performance (maximum temperature and torque loss). Finally, the optimal range of oil level is estimated, considering the maximum allowable temperature of the motor.