• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.2
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• pp.193-200
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• 2008

Cohesive and frictional materials such as concrete and soil are pressure dependent. In general, failure criterion for such materials inclined with respect to positive hydrostatic axis in Haigh-Westergaard stress space. Consequently, inelastic volumetric strain always positive with associated flow rule. In this study, to overcome this shortcoming, non-associated flow rule which controls volumetric component of plastic flow is adopted. Numerical analysis based on a constitutive model using nonuniform hardening plasticity with five parameter failure criterion and non-associated flow rule has conducted to predict concrete behavior under multi-axial stress state and verified with experimental result.

• Proceedings of the Korean Vacuum Society Conference
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• 2000.02a
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• pp.179-179
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• 2000

음극의 크기에 따라 발생된 전자빔 전류가 도파관 영역에서 공간 전하 한계 전류를 초과할 경우 형성되는 가상 음극 (Virtual Cathode)을 이용한 축 방향으로의 고출력 마이크로파 발생 및 진단에 관한 연구를 수행하였다. 먼저 실험에 앞서 전산모사를 통해 결과를 예측하고 실험을 통해 확인하는 순으로 하였다. 전산 모사는 2-1/2차원 Partical-In-Cell(PIC) 코드인 "MAGIC"을 사용하여 축 방향으로 진행하는 새로운 개념의 가상 음극발진기를 모사하고, 정확한 경과를 얻기 위해 강렬한 상대론적 전자빔 발생 장치인 "천둥"( 최대 전압 600kV, 최대 전류 70KA, 60ns)을 사용하여 전산 모사에 넣어줄 전류값을 얻었다. 음극의 반지름이 2.5cm 일 때 전파되는 최대 출력이 약 800MW인 마이크로파가 발생되었고, 이때 출력변환 효율이 약 30%임을 전산모사를 통하여 알 수 있었다. 또한 전파하는 전기장의 축방향 성분(Ez)의 반지름 방향에 대한 분포 특성을 통하여 주된 전파 모드가 TM01와 그 상위모드의 조합으로 이루어졌음을 알았고 이때 기대되는 동작 진동수는 5~7 GHz임을 전산 모사 결과로부터 알 수 있었다. 실험을 통해서도 음극의 크기가 2.5cm 때, 최대 출력이 약 520MW인 마이크로파를 발생하였고, 이 때 출력 변환 효율은 약 8%이고, 방전 사진을 통해서 주된 동작 모드가 TM01와 그 상위모드의 조합으로 이루어졌음을 알았고, 이때 주된 출력 진동수는 5~6 GHz임을 알 수 있었다.는 5~6 GHz임을 알 수 있었다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.8
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• pp.1599-1607
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• 2002

The purpose of this work is to suggest a new non-contact damage detection method for rotating ferromagnetic shafts. The presence and the location of a damage in rotating shafts are assessed by means of longitudinal elastic waves propagating along the shafts. These waves are measured by non-contact magnetostrictive sensors consisting of a coil and bias magnets. This paper shows the effectiveness of the sensors in the damage detection of rotating shafts. Several issues occurring in the application of the sensors to rotating shafts are carefully investigated.

• The Korea Swine Journal
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• v.11 no.9 s.121
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• pp.56-61
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• 1989

농림수산부는 지난 8월 3일 돼지오제스키병 방역실시 요령을 개정, 수의사가 돼지오제스키병의 검사를 할 수 있게 하고 관리수싀사의 경우에는 관리하는 양돈장에 입식하는 돼지에 한해 검사토록 했다. 이번에 개정된 돼지오제스키병 방역실시 요령은 검사기관으로 시.도가축위생시험소장,가축위생연구소장,동물병원을 개업한 수의사 또는 신규 입식하는 돼지는 음성으로 확인된 돼지에 한해 입식해야 하며, 양성축 발견시에는 가축전염벼예방법 제4조의 규정에 의거 선고토록 하고 신고를 받은 도지사는 이에 대한 역학조사를 실시토록 했다. 따라서 본지는 돼지오제스키병 박멸을 위해 학계.연구기관.업계.농장관계자 등을 대상으로 간이진단키트확대 보급방안에 대한 의견을 듣고, 이에 따른 찬반론을 지상 소개한다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.97-98
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• 2014

Vibration signal has been widely utilized in the diagnostics of rotating mechanical system. Diagnostics systems in rotating machinery are depends on the vibration data which are acquired from the system. However, the characteristics of acquired data can be vary according to the position of anomaly installed or the position of data acquired. In this research, the coordinate transform of journal bearing vibration signal was proposed to overcome the limitation mentioned above. Journal bearing systems are equipped two gap sensors with ninety degree angles, and it can enable to generate coordinate transformed signals in arbitrary angle domain. More abundant information for each normal or anomaly conditions are obtained from coordinate transformation than only the data of the existing measuring position, then we have developed a reliable and robust diagnosis algorithm for journal bearing system.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.281-284
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). The measurement parameters of Smart UAV propulsion system are gas generator rotational speed, power turbine rotational speed, exhaust gas temperature and torque. But two measurement such as compressor exit pressure and compressor turbine exit temperature were added because they were difficult each component diagnostics using the default measurement parameter. The performance parameters for the estimate of component performance degradation degree are flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network learning and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2005.11a
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• pp.289-292
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• 2005

Because of accumulation of operation time, the performance of main components(compressor, combustor, turbine, etc.) come to be deteriorated in gas-turbine engine. So, high reliability and minimun of expense are important problem for engine manufacturer and user in operation of gas-turbine engine. In this study, the diagnostic code of the engine performance using the thermodynamic sensitivity between the sensed parameters and the health parameters has been developed without an application of the commercial program. The single performance deterioration of the turbo-shaft engine has been estimated with this code.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.3
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• pp.82-89
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• 2006

Recently operation and maintenance cost of gas turbine engines has been issued as a major parameter in terms of designing and manufacturing. Accordingly, the conception that the maintenance and repair of an engine has to be conducted in assembled condition has been spreaded out. However, it is possible only if the prediction of the engine performance is clearly identified. In this study, therefore, a diagnostic code of the engine performance has been developed by using GPA(Gas Path Analysis) and Fuzzy Logic which can analyze the engine performance and estimate the health parameters. The prediction of the quantitative performance deterioration of the established model of the turbo-shaft engine for SUAV has been achieved in a satisfied level compared to that obtained by GSP code.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.213-217
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). For teaming the NN, a BPN with one hidden, one input and one output layer was used. The input layer had seven neurons of variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer used 6 neurons of degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network teaming and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.15-22
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle) which is being developed by KARI (Korea Aerospace Research Institute). For teeming the NN(Neural Network), a BPN(Back Propagation Network) with one hidden, one input and one output layer was used. The input layer has seven neurons: variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer uses 6 neurons: degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine, respectively, Database for network teaming and test was constructed using a gas turbine performance simulation program. From application of the learned networks to diagnostics of the PW206C turboshaft engine, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.