• 한국가시화정보학회지
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• 제10권1호
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• pp.27-33
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• 2012

This paper describes a new design of small-scale horizontal wind blade, called spiral wind turbine blade. Theoretical and numerical approaches on the prediction of aerodynamic performance of the blade have been conducted. A theoretical equation is successfully derived using the angular momentum equation to predict aerodynamic characteristics according to the design shape parameters of spiral blade. To be compared with the theoretical value, a numerical simulation using ANSYS CFX v12.1 is performed on the same design with the theoretical one. Large scale tip vortex is captured and graphically presented in this paper. The TSR-$C_p$ diagram shows a typical parabolic relation in which the maximum efficiency of the blade approximately 25% exists at TSR=2.5. The numerical simulation agrees well with that of the theoretical result except at the low rotational speed region of 0~20 rad/s.

• 한국지능시스템학회논문지
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• 제26권4호
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• pp.286-294
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• 2016

초기설계 단계에서 시스템의 성능을 고려한 형상의 최적화가 필요하다. 하지만, 일반적으로 공학시스템의 성능예측은 많은 계산 시간이 요구되는 작업이다. 시스템 형상의 최적화를 위해서는 다양한 설계대안에 대한 성능의 평가가 요구되므로 초기 설계과정에서 많은 어려움이 있다. 이러한 문제를 해결하기 위해, 많은 연구자들은 응답표면방법을 이용한 성능예측에 관한 다양한 연구를 시도하고 있다. 하지만, 이 방법은 비선형성이 강한 문제에서 예측오차가 비교적 크게 발생하는 단점이 있다. 따라서 본 연구의 최종목표는 초기설계과정에서 성능예측을 위한 적절한 근사모델을 제시하고, 해양시스템 성능예측문제(부유식 해상발전기 하부구조물 최적화 문제, 유조선의 선저외판 최적화 문제)에 적용하여 제시된 근사모델을 검증하는 것이다.

• 대한기계학회논문집
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• 제14권5호
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• pp.1264-1271
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• 1990

본 연구에서는 회전 깃(rotor blade)을 폭 방향과 시위방향으로 많은 평면 페 널(panel)들로 나누어 이에 말굽쇠 형 화류(horseshoe vortex)를 분포시키는 양력면 (lifting surface)으로 대치하고 후류는 깃상의 순환(circulation)분포에 의해 그 크 기가 결정되는 와도(vorticity)를 와류격자로 대치하는 와류격자법(Vortex Lattice Method`VLM)을 사용하여 HAWT의 공기역학적 성능 예측을 시도하였다. 그리고 후류의 형상은 근 후류(near wake)와 원후류(far wake)로 나누어 근 후류는 깃의 후연(trail- ing edge)에서의 속도를 갖고 와선(vortex line)이 움직이게 하여 결정하였고 원 후류 는 반무한대 원형화류 실린더(semi-infinite circular vortex cylinder)로 취급하여 결정하였다.

• Ocean Systems Engineering
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• 제11권1호
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• pp.43-58
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• 2021

In the present work, a 3D numerical model is proposed to study local scouring around single vertical piers with different cross-section shapes under steady-current flow. The model solves the flow field and sediment transport processes using a coupled approach. The flow field is obtained by solving the Unsteady Reynolds Averaged Navier-Stokes (URANS) equations in combination with the k-ω SST turbulence closure model and the sediment transport is considered using both bedload and suspended load models. The proposed model is validated against the empirical measurements of local scour around single vertical piers with circular, square, and diamond cross-section shapes obtained from the literature. The measurement of scour depth in equilibrium condition for the simulations reveal the differences of 4.6%, 6.7% and 13.1% from the experimental measurements for the circular, square, and diamond pier cases, respectively. The model displayed a remarkable performance in the prediction of scour around circular and square piers where horseshoe vortices (HSVs) have a leading impact on scour progression. On the other hand, the maximum deviation was found in the case of the diamond pier where HSVs are weak and have minimum impact on the formation of local scour. Overall, the results confirm that the prediction capability of the present model is almost independent of the strength of the formed HSVs and pier cross-section shapes.

• Wind and Structures
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• 제36권4호
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• pp.237-247
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• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.

• 한국가시화정보학회지
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• 제21권2호
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• pp.83-90
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• 2023

The shape of an airfoil is a critical factor in determining aerodynamic characteristics such as lift and drag. Aerodynamic properties of an airfoil have a decisive impact on the performance of various engineering applications, including airplane wings and wind turbine blades. Therefore, it is essential to analyze the aerodynamic characteristics of airfoils. Various analytical tools such as experiments, computational fluid dynamics, and Xfoil are used to perform these analyses, but each tool has its limitation. In this study, airfoil parameterization, image recognition, and artificial intelligence are combined to overcome these limitations. Image and coordinate data are collected from the UIUC airfoil database. Airfoil parameterization is performed by recognizing images from image data to build a database for deep learning. Trained model can predict the aerodynamic characteristics not only of airfoil images but also of sketches. The mean absolute error of untrained data is 0.0091.

• 대한기계학회논문집B
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• 제39권8호
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• pp.653-660
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• 2015

본 연구에서는 CFD 해석기법을 이용하여 서로 다른 두 가지 형식의 수직축 조류발전용 터빈에 대한 출력성능 및 하중 해석을 수행하였다. ANSYS CFX를 이용하여 시간변화에 따른 해석을 수행하였으며, H 타입 로터의 정상 및 극치운전조건에서 각각 7.47kW와 67.6kW의 출력이 나타났다. 이는 초기 설계조건에 적합하지 않은 것으로 확인되었으며, helical 타입 로터의 정상 및 극치운전조건에서는 출력성능이 거의 설계 운전점에 가까운 특성을 나타내었다. 블레이드 주변에 발생하는 캐비테이션은 두 종류의 로터 블레이드 모두에서 반복적으로 발생되었으며, 조류 터빈의 순간 출력변화에 많은 영향을 미칠 수 있다. 따라서 안정적인 출력품질의 확보 및 피로파손 방지를 위해서는 캐비테이션 현상의 발생을 최소화 할 수 있는 설계가 필요하다.