• Wind and Structures
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• v.13 no.3
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• pp.293-307
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• 2010

This paper describes a new method for the estimation of six complex aerodynamic admittance functions. The aerodynamic admittance functions relate buffeting forces to the incoming wind turbulent components, of which the estimation accuracy affects the prediction accuracy of the buffeting response of long-span bridges. There should be two aerodynamic admittance functions corresponding to the longitudinal and vertical turbulent components, respectively, for each gust buffeting force. Therefore, there are six aerodynamic admittance functions in all for the three buffeting forces. Sears function is a complex theoretical expression for the aerodynamic admittance function for a thin airfoil. Similarly, the aerodynamic admittance functions for a bridge deck should also be complex functions. This paper presents a separated frequency-by-frequency method for estimating the six complex aerodynamic admittance functions. A new experimental methodology using an active turbulence generator is developed to measure simultaneously all the six complex aerodynamic admittance functions. Wind tunnel tests of a thin plate model and a streamlined bridge section model are conducted in turbulent flow. The six complex aerodynamic admittance functions, determined by the developed methodology are compared with the Sears functions and Davenport's formula.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• Wind and Structures
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• v.20 no.5
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• pp.609-622
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• 2015

This study presents a dynamic response analysis of operational and parked wind turbines in order to gain better understanding of the roles of wind loads on turbine blades and tower in the generation of turbine response. The results show that the wind load on the tower has a negligible effect on the blade responses of both operational and parked turbines. Its effect on the tower response is also negligible for operational turbine, but is significant for parked turbine. The tower extreme responses due to the wind loads on blades and tower of parked turbine can be estimated separately and then combined for the estimation of total tower extreme response. In current wind turbine design practice, the tower extreme response due to the wind loads on blades is often represented as a static response under an equivalent static load in terms of a concentrated force and a moment at the tower top. This study presents an improved equivalent static load model with additional distributed inertial force on tower, and introduces the square-root-of-sum-square combination rule, which is shown to provide a better prediction of tower extreme response.

• Wind and Structures
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• v.38 no.1
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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.

• Journal of Ocean Engineering and Technology
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• v.19 no.2 s.63
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• pp.12-18
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• 2005

Typhoon Maemi landed on the southeast coast of Korea and caused a severe storm surge in Jinhae Bay and Masan Bay. The tide gage in Masan Port recorded the storm surge of a maximum of more than 2m and the area of more than 700m from the Seo Hang Wharf was flooded by the storm surge. They had not met such an extremely severe storm surge since the opening of the port. Then storm surge was hindcasted with a numerical model. The typhoon pressure was approximated by Myers' empirical model and super gradient wind around the typhoon eye wall was considered in the wind estimation. The land topography surrounding Jinhae Bay and Masan Bay is so complex that the computed wind field was modified with the 3D-MASCON model. The motion of seawater due to the atmospheric forces was simulated using a one-layer model based on non-linear long wave approximation. The Janssen's wave age dependent drag coefficient on the sea surface was calculated in the wave prediction model WAM cycle 4 and the coefficient was inputted to the storm surge model. The result shows that the storm surge hindcasted by the numerical model was in good agreement with the observed one.

• Journal of Animal Environmental Science
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• v.21 no.1
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• pp.1-8
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• 2015

This study was conducted to investigate the dispersion prediction of odor from Hanwoo and dairy farms. Gaussian Plume model used in considering of farm size, wind velocity, atmospheric stability and threshold odor unit to prediction of odor dispersion based on the survey on current state of odor emission and control from 9 site of Hanwoo and 9 site of dairy farms. Farm size, wind velocity and atmospheric stability were affected the distance of odor dispersion, showed longer distance in cases of large farm, low wind velocity and stable atmospheric condition. We will suggestion the adjusted distance of odor dispersion according to farm size was estimated to 50~100 m in Hanwoo farm and 50~150 m in dairy farm when apply the 3OU, 5 m/s wind velocity and stable atmospheric condition.

• Journal of Animal Environmental Science
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• v.20 no.2
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• pp.41-48
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• 2014

This study was conducted to investigate the dispersion prediction of odor from swine farms in Korea. Gaussian Plume model used in considering of farm size, wind velocity, atmospheric stability and threshold odor unit to prediction of odor dispersion based on the survey on current state of odor emission and control from 48 site of swine farms. Farm size, wind velocity and atmospheric stability were affected the distance of odor dispersion, showed longer distance in cases of large farm, low wind velocity and stable atmospheric condition. We will suggestion the adjusted distance of odor dispersion according to farm size was estimated to 180 m in small farm and 320 m in large farm when apply the 3 OU, 5 m/s wind velocity and stable atmospheric condition.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.7
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• pp.20-28
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• 2002

In this paper, the aerodynamic performance prediction of a 30kW counter-rotating (C/R) wind turbine system has been made by using the momentum theory as well as the two-dimensional quasi-steady strip theory with special care on the wake and the post-stall effects. In order to take into account the wake effects in the performance analysis, the wind tunnel test data obtained for a scaled blade are used. Both the axial and rotational inductions behind the auxiliary rotors are determined through the wake model. In addition, the optimum chord and twist distributions along the blades are obtained from the Glauert's optimum actuator disk model considering the Prandtl's tip loss effect. The performance results of the counter-rotating wind turbine system are compared with those of the conventional single rotor system and demonstrated the effectiveness of the counter-rotating wind turbine system.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.1
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• pp.59-65
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• 2002

The application of local air circulation models in the field of air pollution research has become more and more popular with increasing demands of detailed wind data for obtaining precise information on spatial and temporal variations. However the prediction of air circulation near the surface is generally not a simple task because of intricate interactions between surface and air. Particularly in Korea, many areas are mountainous with a complicated shoreline. Because considerable errors could be introduced into the model predictions, it is necessary to confirm their feasibility by comparing model predictions with observations. In this paper, the results from the evaluation of model predictions in selected publications in Korea as well as their procedures were reviewed. Various aspects of errors in the model predictions. such as possible sources, vulnerable conditions, and reduction methods, were discussed.

• Wind and Structures
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• v.28 no.1
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• pp.31-47
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• 2019

The accurate prediction of snow distributions under the wind action on roofs plays an important role in designing structures in civil engineering in regions with heavy snowfall. Affected by some factors such as building shapes, sizes and layouts, the snow drifting on roofs shows more three-dimensional characteristics. Thus, the research on three-dimensional snow distribution is needed. Firstly, four groups of stepped flat roofs are designed, of which the width-height ratio is 3, 4, 5 and 6. Silica sand with average radius of 0.1 mm is used to model the snow particles and then the wind tunnel test of snow drifting on stepped flat roofs is carried out. 3D scanning is used to obtain the snow distribution after the test is finished and the mean mass transport rate is calculated. Next, the wind velocity and duration is determined for numerical simulations based on similarity criteria. The adaptive-mesh method based on radial basis function (RBF) interpolation is used to simulate the dynamic change of snow phase boundary on lower roofs and then a time-marching analysis of steady snow drifting is conducted. The overall trend of numerical results are generally consistent with the wind tunnel tests and field measurements, which validate the accuracy of the numerical simulation. The combination between the wind tunnel test and CFD simulation for three-dimensional typical roofs can provide certain reference to the prediction of the distribution of snow loads on typical roofs.