• 한국해양공학회지
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• 제36권3호
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• pp.194-210
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• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.

• 한국해안해양공학회지
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• 제6권3호
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• pp.245-259
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• 1994

연직 확산계수의 산정에 k-I 난류방정식을 도입하여 예측성을 향상시킨 치안 해수유동 및 온배수 확산에 관한 3차원 수치모형을 개발하였다. 모형은 1차원 수로에서 취송류의 연직분포, 정지수역으로의 온배수 젯트에 대하여 수리실험자료와의 비교검증을 실시하고 고리해역에서 조류 및 온배수 확산을 해석하여 현장 적용성을 검토하였다. 계산결과는 검증에 사용된 자료와 대체로 일치하는 양호한 결과를 보였으며, 취송류, 밀도류, 조류에 대하여 동일한 모형상수를 사용하여 연직 확산계수를 계산할 수 있어 난류모형의 상수가 보편성이 있음을 확인하였다. 따라서, 본 연구에서 $textsc{k}$-ι 난류방정식을 도입하여 개발된 수치모형은 연안 해수유동을 대표하는 취송류, 밀도류, 조류에 대하여 난류상수의 보편성과 모형의 예측성을 갖고 있어 연안 해수유동 및 확산을 연구하는 데 효율적으로 활용될 수 있을 것으로 사료된다.

• Wind and Structures
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• 제15권3호
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• pp.247-261
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• 2012

Some numerical investigations are presented concerning the response of a given plate under turbulence driven excitations. Three different input loads are simulated according to the wall pressure distributions derived from the models proposed by Corcos, Efimtsov and Chase, respectively. Modal solutions (finite element based) are used for building the modal stochastic responses in the sub-critical aerodynamic frequency range. The parametric investigations concern two different values of the structural damping and three values of the boundary layer thickness. A final comparison with available experimental data is also discussed. The results demonstrate that the selection of the adequate TBL input model is still the most critical step in order to get a good prediction.

• Geomechanics and Engineering
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• 제29권2호
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• pp.171-182
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• 2022

Reusing the used pile has not yet been implemented due to the unpredictability of the bearing capacity evolution. This paper presents an analytic approach to estimate the sides shear setup after the dissipation of pore pressure. Long-term evolution of adjacent soil is simulated by viscoelastic-plastic constitutive model. Then, an innovative concept of quasi-overconsolidation is proposed to estimate the strength changes of surrounding soil. Total stress method (α method) is employed to evaluate the long term bearing capacity. Measured data of test piles in Louisiana and semi-logarithmic time function are cited to validate the effectiveness of the presented method. Comparisons illustrate that the presented approach gives a reasonably prediction of the side shear setup. Both the presented method and experiment show the shaft resistance increase by 30%-50%, and this highlight the potential benefit of piles reutilization.

• 대기
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• 제22권2호
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• pp.267-277
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• 2012

The objectives of this study are to develop a real-time drought monitoring and prediction system on the East Asia domain and to evaluate the performance of the system by using past historical drought records. The system is mainly composed of two parts: drought monitoring for providing current drought indices with meteorological and hydrological conditions; drought outlooks for suggesting future drought indices and future hydrometeorological conditions. Both parts represent the drought conditions on the East Asia domain (latitude $21.15{\sim}50.15^{\circ}$, longitude $104.40{\sim}149.65^{\circ}$), Korea domain (latitude $30.40{\sim}43.15^{\circ}$, longitude $118.65{\sim}135.65^{\circ}$) and South Korea domain (latitude $30.40{\sim}43.15^{\circ}$, longitude $118.65{\sim}135.65^{\circ}$), respectively. The observed meteorological data from ASOS (Automated Surface Observing System) and AWS (Automatic Weather System) of KMA (Korean Meteorological Administration) and model-driven hydrological data from LSM (Land Surface model) are used for the real-time drought monitoring, while the monthly and seasonal weather forecast information from UM (Unified Model) of KMA are utilized for drought outlooks. For the evaluation of the system, past historical drought records occurred in Korea are surveyed and are compared with the application results of the system. The results demonstrated that the selected drought indices such as KMA drought index, SPI (3), SPI (6), PDSI, SRI and SSI are reasonable, especially, the performance of SRI and SSI provides higher accuracy that the others.

• Nuclear Engineering and Technology
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• 제53권10호
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• 한국가스학회지
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• 제24권6호
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• pp.91-97
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• 2020

리튬이온 배터리(LIB)는 다른 배터리에 비해 수명이 길고, 에너지 밀도가 높으며, 자체 방전율이 낮아, 에너지 저장장치(ESS)로 선호되고 있다. 하지만, 2017~2019년 기간 동안 국내에서만도 28건의 화재사고가 발생하였으며, LIB의 운영 중 안전성 및 신뢰성을 보장하기 위해 LIB의 정확한 용량추정은 필수요소이다. 본 연구에서는 LIB의 충방전 cycle에 따른 용량변화를 예측하는 기계학습 기반 모델의 설계에 있어 중요한 요소인 최적 머신러닝 모델의 선정을 위해, Decision Tree, 앙상블학습법, Support Vector Regression, Gaussian Process Regression (GPR) 각각을 이용한 예측모델을 구현하고 성능비교를 실시하였다. 학습을 위해 NASA에서 제공하는 시험데이터를 사용하였으며, GPR이 가장 좋은 예측성능을 보였다. 이를 바탕으로 추가 시험데이터 학습을 통해 개선된 LIB 용량예측과 잔여 수명추정 모델을 개발하여, 운영 중 이상 감지 및 모니터링 성능을 높여, 보다 안전하고 안정된 ESS 운용에 활용하고자 한다.

• 대한조선학회논문집
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• 제58권3호
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• pp.158-166
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• 2021

In this paper, a numerical analysis technique using a body force model is investigated to estimate the available net thrust of multi-pod-driven ice-breaking vessels under bollard pull and overload conditions. To employ the body force model in present flow simulations, drag and thrust components acting on the pod unit are calculated by using Propeller Open Water (POW) test data. The available net thrusts according to the direction of operation are evaluated in both bollard pull and overload conditions under deep water. The simulation results are compared with the model test data. The available net thrusts, calculated by the present analysis for ahead operating modes at 3~6 knots which are typical speeds of the target vessel in arctic field, are agreed well with the model test results. It is also found that the present result for astern operating mode appears approximately 6 % larger than the model test result. In addition, the available net thrusts are calculated under the both operating conditions accompanied by shallow water effects, and the main cause of the difference is studied. Based on the result of the present study, it is confirmed that the body force model can be applied to the performance evaluation of multi-pod propulsion system and the main engine selection in early design stage of the vessel.

• 지구물리와물리탐사
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• 제23권3호
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• pp.168-177
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• 2020

해저면 탄성파 겹반사는 발파점 모음자료와 겹쌓기 단면에서 모두 일차 반사파의 해석에 잘못된 결과를 초래할 수 있다. 따라서, 해저면 겹반사는 자료처리를 통해 제거해야 한다. 전통적인 자료처리 과정에서 겹반사 제거는 예측오차 곱풀기와 라돈 필터링 등과 같은 모델-기반 기법과 지표관련-겹반사제거와 같은 데이터-기반 기법에 의해 이루어져 왔다. 그러나 대다수의 자료처리 과정들은 방대한 컴퓨터 자원과 전문적인 자료처리 기법뿐만 아니라 자료처리 변수들을 테스트하고 선택하는데 많은 시간을 필요로 한다. 이 논문에서는 머신러닝 시스템을 활용한 해저면 겹반사의 제거효과를 살펴보기 위해 Marmousi2 속도모델에 대한 수치모델링으로 겹반사가 포함된 입력데이터와 겹반사가 포함되지 않은 레이블데이터를 생성하였다. 수직시간차가 보정된 공통중간점 모음자료로 훈련데이터를 구성하였으며 인공신경망은 U-Net 모델을 적용하였다. 해저면 겹반사를 제거하기 위해 훈련된 모델은 레이블데이터에 거의 근접하는 예측 결과를 만들어내며, 현장자료에 대한 예측 테스트에서 해저면 겹반사를 효과적으로 제거하는 것으로 나타났다.

• 농업과학연구
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• 제51권3호
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• pp.315-329
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• 2024

This study was conducted for simulation model development of an electric all-wheel-drive vehicle to adapt the agricultural machinery. Data measurement system was installed on a four-wheel electric driven vehicle using proximity sensor, torque-meter, global positioning system (GPS) and data acquisition (DAQ) device. Axle torque and rotational speed were measured using a torque-meter and a proximity sensor. Driving test was performed on an upland field at a speed of 7 km·h^-1. Simulation model was developed using a multi-body dynamics software, and tire properties were measured and calculated to reflect the similar road conditions. Measured and simulated data were compared to validate the developed simulation model performance, and axle rotational speed was selected as simulation input data and axle torque and power were selected as simulation output data. As a result of driving performance, an average axle rotational speed was 115 rpm for each wheel. Average axle torque and power were 4.50, 4.21, 4.04, and 3.22 Nm and 53.42, 50.56, 47.34, and 38.07 W on front left, front right, rear left, and rear right wheel, respectively. As a result of simulation driving, average axle torque and power were 4.51, 3.9, 4.16, and 3.32 Nm and 55.79, 48.11, 51.62, and 41.2 W on front left, front right, rear left, and rear right wheel, respectively. Absolute error of axle torque was calculated as 0.22, 7.36, 2.97, and 3.11% on front left, front right, rear left, rear right wheel, respectively, and absolute error of axle power was calculated as 4.44, 4.85, 9.04, and 8.22% on front left, front right, rear left, and rear right wheel, respectively. As a result of absolute error, it was shown that developed simulation model can be used for driving performance prediction of electric driven vehicle. Only straight driving was considered in this study, and various road and driving conditions would be considered in future study.