• Journal of Biosystems Engineering
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• 제21권4호
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• pp.429-435
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• 1996

A wind tunnel consisting of two air flow conditioners with polycarbonate pipes, a plant growth room, a suction fan and fan controller, and fluorescent lamps, was designed to investigate the interactions between the growth of plug seedlings under artificial light and their Physical environments. Light transmissivities in the plant growth room based on the photosynthetic photon flux density and photosynthetically active radiation was appeared to be 96.3% and 96.8%, respectively. Measurement showed a uniformity in the vertical profiles of air current speed at the middle and rear regions of plug trays in wind tunnel. This result indicated that the development of a wind tunnel based on the design criteria of the American Society of Mechanical Engineers was adequate. Air current speed inside the plug stand was significantly decreased due to the resistance by the leaves of plug seedlings and boundary layer developed over and below the plug stand. Driving force to facilitate the diffusion of gas inside the plug stand might be regarded as extremely low. Aerodynamic characteristics above the plug stand under artificial light were investigated. As the air current speed increased, zero plane displacement decreased but roughness length and frictional velocity increased. Zero plane displacement linearly increased with the average height of plug seedlings. The wind tunnel developed in this study would be useful to investigate the effects of air current speed on the microclimate over and inside the plug stand and to collect basic data for a large-scale plug production under artificial light in a semi-closed ecosystem.

• 한국공간구조학회논문집
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• 제18권4호
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• pp.97-104
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• 2018

A connected control method for the adjacent buildings has been studied to reduce dynamic responses. In these studies, seismic loads were generally used as an excitation. Recently, multi-hazards loads including earthquake and strong wind loads are employed to investigate control performance of various control systems. Accordingly, strong wind load as well as earthquake load was adopted to evaluate control performance of adaptive smart coupling control system against multi-hazard. To this end, an artificial seismic load in the region of strong seismicity and an artificial wind load in the region of strong winds were generated for control performance evaluation of the coupling control system. Artificial seismic and wind excitations were made by SIMQKE and Kaimal spectrum based on ASCE 7-10. As example buildings, two 20-story and 12-story adjacent buildings were used. An MR (magnetorheological) damper was used as an adaptive smart control device to connect adjacent two buildings. In oder to present nonlinear dynamic behavior of MR damper, Bouc-Wen model was employed in this study. After parametric studies on MR damper capacity, optimal command voltages for MR damper on each seismic and wind loads were investigated. Based on numerical analyses, it was shown that the adaptive smart coupling control system proposed in this study can provide very good control performance for Multi-hazards.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2011년 춘계학술대회논문집
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• pp.286-290
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• 2011

This paper introduces the optimum macro-siting of a potential site for an offshore wind farm around Jeju Island using the RDAPS sea wind model. The statistical model was developed by analyzing the sea wind data from RDAPS model, and the meso-scale digital wind map was prepared. To develop the high resolution spatial calibration model, Artificial Neural Network(ANN) models were used to construct the wind and bathymetric maps. Accuracy and consistency of wind/bathymetric spatial calibration models were obtained using analysis of variance. The optimization problem was defined to maximize the energy density satisfying the criteria of maximum water depth and maximum distance from the coastline. The candidate site was selected through Genetic Algorithm(GA). From the results, it is possible to predict roughly a candidate site location for the installation of the offshore wind jam, and to evaluate the wind resources of the proposed site.

• Wind and Structures
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• 제38권1호
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• Structural Monitoring and Maintenance
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• 제5권4호
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• pp.507-519
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• 2018

In this paper, damage assessment in wind-turbine towers using vibration-based artificial neural networks (ANNs) is numerically investigated. At first, a vibration-based ANNs algorithm is designed for damage detection in a wind turbine tower. The ANNs architecture consists of an input, an output, and hidden layers. Modal parameters of the wind turbine tower such as mode shapes and frequencies are utilized as the input and the output layer composes of element stiffness indices. Next, the finite element model of a real wind-turbine tower is established as the test structure. The natural frequencies and mode shapes of the test structure are computed under various damage cases of single and multiple damages to generate training patterns. Finally, the ANNs are trained using the generated training patterns and employed to detect damaged elements and severities in the test structure.

• 정보처리학회논문지A
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• 제13A권7호
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• pp.633-638
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• 2006

본 논문에서는 자연풍에 비해 상대적으로 작은 영역에 영향을 주는 인공풍을 시뮬레이션하는 방법을 제안한다. 이를 위해 인공풍의 진행 형태를 모델링하는 방법을 제안하고, 제안하는 바람 모델이 시뮬레이션 환경에 미치는 영향을 계산하는 효율적인 방법도 제안한다. 제안하는 방법에서는 인공풍의 영향을 계산하는 수식이 기존의 조명 모델(Illumination Model)에서의 조도 계산식(Intensity Equation)과 유사함을 보이고, 이를 이용하여 바람에 의한 영향을 직접 수식으로 계산하지 않고 집중광선(Spot Light)에 대한 조도 계산식을 사용하여 효과적으로 인공풍의 힘을 계산한다. 제안하는 방법은 실시간 처리가 가능하며, 컴퓨터 게임이나 가상 현실과 같은 다양한 분야에 적용할 수 있다.

• 생물환경조절학회지
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• 제5권2호
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• pp.152-159
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• 1996

Experiment was performed in a newly developed wind tunnel with light system to determine the aerodynamic resistance and eddy diffusivity above the plug stand under artificial light. Maximum air temperature appeared near the top of the plug stand under artificial light. Since Richardson number was ranged from -0.07 to +0.01, the atmosphere above the plug stand in wind tunnel was in an unstable or near- neutral stability state. The average aerodynamic resistance at rear region of plug stand was 25 % higher than that at middle region. Eddy diffusivity($K_{M}$) linearly increased with the increasing air current speed. $K_{M}$ at air current speed of 0.9 m.$s^{-1}$ was about two times as many as that at air current speed of 0.3 m.$s^{-1}$. And average $K_{M}$ at the rear region was 15% lower than that at the middle region. These results indicated that the diffusion of heat and mass along the direction of air current inside the plug stand was different. It might cause the lack of uniformity in the growth and quality of plug seedlings. The wind tunnel developed in this study would be useful to investigate the effects of air current speed on microclimates and the growth of plug seedlings under artificial light in a semi- closed ecosystem.

• Wind and Structures
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• 제32권2호
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• pp.127-142
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• 2021

Wind load acting on a standalone structure is different from that acting on a similar structure which is surrounded by other structures in close proximity. The presence of other structures in the surrounding can change the wind flow regime around the principal structure and thus causing variation in wind loads compared to a standalone case. This variation on wind loads termed as interference effect depends on several factors like terrain category, geometry of the structure, orientation, wind incident angle, interfering distances etc., In the present study, a three building configuration is considered and the mean pressure coefficients on each face of principle building are determined in presence of two interfering buildings. Generally, wind loads on interfering buildings are determined from wind tunnel experiments. Computational fluid dynamic studies are being increasingly used to determine the wind loads recently. Whereas, wind tunnel tests are very expensive, the CFD simulation requires high computational cost and time. In this scenario, Artificial Neural Network (ANN) technique and Support Vector Regression (SVR) can be explored as alternative tools to study wind loads on structures. The present study uses these data-driven approaches to predict mean pressure coefficients on each face of principle building. Three typical arrangements of three building configuration viz. L shape, V shape and mirror of L shape arrangement are considered with varying interfering distances and wind incidence angles. Mean pressure coefficients (Cp mean) are predicted for 45 degrees wind incidence angle through ANN and SVR. Further, the critical faces of principal building, critical interfering distances and building arrangement which are more prone to wind loads are identified through this study. Among three types of building arrangements considered, a maximum of 3.9 times reduction in Cp mean values are noticed under Case B (V shape) building arrangement with 2.5B interfering distance. Effect of interfering distance and building arrangement on suction pressure on building faces has also been studied. Accordingly, Case C (mirror of L shape) building arrangement at a wind angle of 45º shows less suction pressure. Through this study, it was also observed that the increase of interfering distance may increase the suction pressure for all the cases of building configurations considered.

• Wind and Structures
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• 제36권6호
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• pp.423-434
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• 2023

Aerodynamic force coefficients are generally obtained from traditional wind tunnel tests or computational fluid dynamics (CFD). Unfortunately, the techniques mentioned above can sometimes be cumbersome because of the cost involved, such as the computational cost and the use of heavy equipment, to name only two examples. This study proposed to build a deep neural network model to predict the aerodynamic force coefficients based on data collected from CFD simulations to overcome these drawbacks. Therefore, a series of CFD simulations were conducted using different geometric parameters to obtain the aerodynamic force coefficients, validated with wind tunnel tests. The results obtained from CFD simulations were used to create a dataset to train a multilayer perceptron artificial neural network (ANN) model. The models were obtained using three optimization algorithms: scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg-Marquardt algorithms (LM). Furthermore, the performance of each neural network was verified using two performance metrics, including the mean square error and the R-squared coefficient of determination. Finally, the ANN model proved to be highly accurate in predicting the force coefficients of similar bridge sections, thus circumventing the computational burden associated with CFD simulation and the cost of traditional wind tunnel tests.

• 한국지능시스템학회논문지
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• 제20권5호
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• pp.710-715
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• 2010

최근 풍력발전 시스템은 가장 빨리 발전하고 있는 신재생 에너지원중 하나로 각광을 받고 있으며, 풍력발전 시스템의 주된 관심사는 어떻게 광범위한 풍속의 변화에서도 효율적으로 시스템을 동작시키는 가에 있다. 일반적으로 풍속은 풍력발전시스템의 동특성에 큰 영향을 미치는 요소이다. 따라서 많은 풍력발전 제어 알고리듬은 성능향상을 위해 풍속의 측정을 요구하게 된다. 그러나 불행히도 풍속계와 같은 센서에 의한 실효 풍속의 정확한 측정은 어려운 실정이며 따라서 제어 시스템의 동작을 위해 풍속은 여러 가지 기법을 통해 추정되고 있는 실정이다. 이에 본 연구에서는 칼만 필터 및 신경망에 기반한 새로운 형태의 풍속 추정 기법을 제안하고 제안된 기법의 유용성 확인을 위해 다양한 형태의 시뮬레이션을 수행하고자 한다.