• Journal of the Korean Society for Marine Environment & Energy
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• v.17 no.3
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• pp.167-173
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• 2014

In this work, based on the blade element-momentum theory (BEMT), we proposed the geometry of a lab-scale horizontal axis tidal turbine with a diameter of 80cm, which can demonstrate the maximum power coefficient, and investigated the effect of blade pitch angle increase on the power coefficient. For validation of the computed power coefficients by the BEMT, we also computed the power coefficient using the computational fluid dynamics (CFD) for each case. For the CFD, 15 times of the turbine radius was used for the length and diameter of the computational domain, and the open boundary condition was prescribed at the boundary of the computational domain. The maximum power coefficients of the turbine acquired by the BEMT and CFD were about 48%, showing a good agreement. Both of the power coefficients computed by the BEMT and CFD tended to decrease when the blade pitch angle increases. The two power coefficients for a given tip-speed ratio were in good agreement. Through the present study, we have confirmed that we can trust the proposed geometry and the computed power coefficients based on the BEMT.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.131-139
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• 2002

This study introduces a developed artificial neural networks(ANN) model as a more efficient and reliable prediction model in operating speed Prediction with the 85th percentile horizontal curve of two-way rural highway in the aspect of evaluating highway design consistency. On the assumption that the speed is decided by highway geometry features, total 30 survey sites were selected. Data include currie radius, curve length, intersection angle, sight distance, lane width, and lane of those sites and were used as input layer data of the ANN. The optimized model structure was drawn by number of unit of hidden layer, learning coefficient, momentum coefficient, and change in learning frequency in multi-layer a ANN model. To verify learning Performance of ANN, 30 survey sites were selected while data in obtained from the 20 cites were used as learning data and those from the remaining 10 sites were used as predictive data. As a result of statistical verification, the model D of 4 types of ANN was evaluated as the most similar model to the actual operating speed value: R2 was 85% and %RMSE was 0.0204.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.828-834
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• 1997

The knowledge of material stress-strain behavior is an essential requirement for design and analysis of deformation processes. Empirical stress-strain relationship and constitutive equations describing material behavior during deformation are being widely used, despite suffering some drawbacks in terms of ease of development, accuracy and speed. In the present study, back-propagation neural networks are used to model and predict the flow stresses of a HSLA steel under conditions of constant strain, strain rate and temperature. The performance of the network model is comparedto those of statistical models on rate equations. Well-trained network model provides fast and accurate results, making it superior to statistical models.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.05a
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• pp.177-180
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• 2010

It has been studied the effect of mixture ratio and chamber pressure on variations of discharge coefficients. Combustion experiments of bi-liquid swirl coaxial injectors were conducted at fuel-rich conditions with liquid oxygen and kerosene. Using two types of injectors for the experiments, characteristics of the discharge coefficient have been identified from variations in a diameter of the fuel nozzle and a momentum ratio along with the change of a LOx spray angle. It is concluded that discharge coefficients do not vary because of no change of flame structures from the fact that the fuel swirl chamber is completely filled up with fuel flow.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1901_1902
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• 2009

본 논문에서는 Interval Type-2 퍼지 집합을 이용한 퍼지집합 기반 퍼지뉴럴네트워크를 설계하고 최적화한다. Interval Type-2 퍼지뉴럴네트워크는 각 입력 변수에 따른 서로 분리된 입력 공간을 분할함으로서 네트워크 및 규칙을 구성한다. 규칙의 전반부는 퍼지 입력 공간을 개별적으로 분할하여 표현하고, 각 공간은 Interval Type-2 퍼지 집합으로 구성된다. 규칙의 후반부는 Interval 집합을 이용하여 다항식으로서 표현되며, 오류역전파 알고리즘을 이용하여 연결가중치인 후반부 다항식을 학습한다. 또한, 각 입력에 대한 전반부 멤버쉽함수의 정점과 불확실성 계수 그리고 학습률 및 모멘텀 계수를 유전자 알고리즘을 이용하여 최적 동조한다. 제안된 네트워크는 표준 모델로서 널리 사용되는 수치적인 예를 통하여 평가한다.

• 한국전산유체공학회:학술대회논문집
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• 2005.04a
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• pp.30-34
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• 2005

고속 선박을 추진하는 한 방법으로 널리 사용되는 물분사 추진은 물을 내부 덕트로 빨아들여 임펠러로 물을 가속시켜 노즐을 통해 분사시킴으로써 입출구의 운동량차이에 의해 추력을 얻는 추진장치이다. 선박의 목적에 따라 사용되는 다양한 형태의 물분사 추진기의 개발을 위하여 모형실험을 통하여 그 성능을 검증하는 부분에서 로터의 피치각 변화에 따른 추진기의 성능 실험을 하는 것은 많은 비용과 시간이 따른다. 따라서 이러한 문제를 해결하기 위하여 본 연구에서는 추진기 내부의 유동장을 4가지 피치각에 따라 추진력을 3차원 비압축성 Navier-Stokes 방정식을 이용하여 해석하였다. 로터의 회전을 고려하여 슬라이딩 다중 격자기법을 적용하였고 추력계수, 토크계수, 그리고 모멘텀을 해석 결과와 비교 분석을 통하여 추진기의 성능과 효율을 추정하였다.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.5
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• pp.67-72
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• 2008

The film cooling effectiveness on a flat plate measured by pressure sensitive paint technique. Six film cooling hole were fabricated on a flat plate with 30 degree angle with respect to the surface and three blowing ratios of 0.5, 1, and 2 were tested. Results showed that PSP technique successfully evaluated the distribution of film cooling effectiveness and showed similar results with references. The film cooling effectiveness near the film cooling holes was higher for lower blowing ratio case. As the blowing ratio was increased, the film cooling effectiveness near the film cooling hole decreased due to the lift off of the coolant. At far downstream, the film cooling effectiveness for higher blowing ratio was higher due to the coolant reattachment.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1859_1860
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• 2009

사회가 발전할수록 관련된 화재사고도 많이 발생하게 된다. 화재 사고로 인하여 많은 재산손실과 인명의 피해를 본다. 화재 중에서 가장 많이 발생하는 화재는 전기로 인한 화재이다. 본 논문에서는 여러 가지 전기 화재 발생원인중에서 절연 열화에 의해 일어나는 발화의 한 형태인 트래킹 신호 패턴 인식 시스템을 구현하고자 한다. 트래킹을 인식 시스템을 설계하는데 현재 제어 및 계측분야에서 사용되고 있는 LabVIEW 프로그램을 사용하여 데이터를 획득하고 획득한 데이터에서 유전자 알고리즘을 사용하여 입력 데이터 및 학습률과 모멘텀 계수와 은닉층의 수를 결정하고 결정된 데이터를 패턴인식 알고리즘의 한 형태인 신경망을 이용하여 학습시켜서 트래킹 발생 유무를 판단한다.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1815-1816
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• 2007

본 논문에서는 데이터의 특성을 이용한 정보 입자 기반 퍼지 뉴럴 네트워크의 연속적 최적화를 제안한다. 데이터들간의 거리를 중심으로 C-Means 클러스터링 알고리즘을 이용하여 멤버쉽 함수를 정의하고 각 중심의 후반부 중심값을 이용하여 후반부 학습에 적용한다. 구조/파라미터 동정에 있어서 실수 코딩 기반 유전자 알고리즘을 이용하여 입력변수의 수, 입력 변수의 선택, 멤버쉽함수의 수, 후반부 형태와 같은 시스템의 입력 구조와 전반부 멤버쉽함수의 정점 및 학습율과 모멘텀 계수와 같은 파라미터를 최적으로 동정한다. 또한, 구조 연산과 파라미터 연산의 연속적 동조 방법을 이용하여 퍼지 뉴럴 네트워크를 최적화한다. 제안된 퍼지 뉴럴 네트워크는 삼각형 멤버쉽 함수를 이용하며, 후반부 추론에는 간략, 선형, 변형된 2차식을 이용한다. 제안된 퍼지 뉴럴 네트워크는 표준 모델로서 널리 사용되는 수치적인 예를 통하여 평가한다.

• Journal of the Korean Geotechnical Society
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• v.15 no.1
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• pp.99-112
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• 1999

By using 92 values of lateral earth pressure coefficient(K) measured in Korea, the tendency of K with varying depth is analyzed and compared with the range of K defined by Hoek and Brown. The horizontal stress is generally larger than the vertical stress in Korea : About 84 % of K values are above 1. In this study, the theory of elasto-plasticity is applied to analyze the variation of K values, and the results are compared with those of numerical analysis. This reveals that the erosion, sedimentation and weathering of earth crust are important factors in the determination of K values. Surface erosion, large lateral pressure and good rock mass increase the K values, but sedimentation decreases the K values. This study enable us to analyze the effects of geological processes on the K values, especially at shallow depth where underground excavation takes place. A neural network expert system using multi-layer back-propagation algorithm is developed to predict the K values. The neural network model has a correlation coefficient above 0.996 when it is compared with measured data. The comparison with 9 measured data which are not included in the back-propagation learning has shown an average inference error of 20% and the correlation coefficient above 0.95. The expert system developed in this study can be used for reliable determination of K values.