• Wind and Structures
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• v.34 no.6
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• pp.499-510
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• 2022

Computational Wind Engineering has rapidly grown in the last decades and it is currently reaching a relatively mature state. The prediction of wind loading by means of numerical simulations has been proved effective in many research studies and applications to design practice are rapidly spreading. Despite such success, caution in the use of simulations for wind loading assessment is still advisable and, indeed, required. The computational burden and the know-how needed to run high-fidelity simulations is often unavailable and the possibility to use simplified models extremely attractive. In this paper, the applicability of some well-known 2D unsteady RANS models, particularly the k-ω SST, in the aerodynamic characterization of extruded bodies with bluff sections is investigated. The main focus of this paper is on the drag coefficient prediction. The topic is not new, but, in the authors' opinion, worth a careful revisitation. In fact, despite their great technical relevance, a systematic study focussing on sections which manifest a fully detached flow configuration has been overlooked. It is here shown that the considered 2D RANS exhibit a pathological behaviour, failing to reproduce the transition between reattached and fully detached flow regime.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.290-292
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• 2008

we applied Wind Field Module of PHRLM so that disaster prevention agency concerned can effectively estimate the possible strong wind damages by typhoon. In this study, therefore, we estimated wind speed at 300m level using 700hPa wind according to the research method by Franklin(2003), PHRLM(2003), and Vickery and Skerlj(2005). Then we calculated wind speed at 10m level using the estimated wind speed at 300m level, and finally, peak 3.second gust on surface. The case period is from 18LST August 31 to 03LST September 1, 2002, when the typhoon Rusa in 2002 was the most intense. Among disaster prediction models in the US, Wind Field Module of PHRLM in Florida was used for the 2002 typhoon Rusa case. As a result, peak 3.second gust on the surface increased $10\sim20%$ in the typhoon's 700hPa wind speed.

• KIEAE Journal
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• v.12 no.1
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• pp.127-132
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• 2012

The energy consumption in the world is growing rapidly. And the environmental issues of climate become a important task. The interest in renewable energy like wind and solar is increasing now. Especially, by reducing power transmission loss, a small wind power is getting attention at the residential areas and campus of university. In this study, we attempted to estimate and compare the wind energy density using wind data of AWS (Automatic Weather Station) of H University. In this case of a campus, the weibull distribution parameter C is 2.27, and K is 0.88. According to the data, the energy density of the small wind power is 12.7 W/m2. We did CFD(Computational Fluid Dynamics) simulations at H University campus by 7 wind directions(ENE, ESE, SE, NW, WNW, W, WSW). In the results, we suggest 4 small wind powers. The small wind power generating system can produce 4,514kWh annually.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.32 no.7
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• pp.549-557
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• 2008

Despite of the laminar-turbulent transition region co-exist with fully turbulence region around the leading edge of an airfoil, still lots of researchers apply to fully turbulence models to predict aerodynamic characteristics. It is well known that fully turbulent model such as standard k-model couldn't predict the complex stall and the separation behavior on an airfoil accurately, it usually leads to over prediction of the aerodynamic characteristics such as lift and drag forces. So, we apply correlation based transition model to predict aerodynamic performance of the NREL (National Renewable Energy Laboratory) Phase IV wind turbine. And also, compare the computed results from transition model with experimental measurement and fully turbulence results. Results are presented for a range of wind speed, for a NREL Phase IV wind turbine rotor. Low speed shaft torque, power, root bending moment, aerodynamic coefficients of 2D airfoil and several flow field figures results included in this study. As a result, the low speed shaft torque predicted by transitional turbulence model is very good agree with the experimental measurement in whole operating conditions but fully turbulent model(${\kappa}-\;{\varepsilon}$) over predict the shaft torque after 7m/s. Root bending moment is also good agreement between the prediction and experiments for most of the operating conditions, especially with the transition model.

• Atmosphere
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• v.30 no.3
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• pp.277-291
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• 2020

Low-level wind shear (LLWS) events on glide path at Jeju International Airport (CJU) are evaluated using the Aircraft Meteorological Data Relay (AMDAR) and Korea Meteorological Administration Post-Processing (KMAPP) with 100 m spatial resolution. LLWS that occurs in the complex terrains such as Mt. Halla on the Jeju Island affects directly aircraft approaching to and/or departing from the CJU. For this reason, accurate prediction of LLWS events is important in the CJU. Therefore, the use of high-resolution Numerical Weather Prediction (NWP)-based forecasts is necessary to cover and resolve these small-scale LLWS events. The LLWS forecasts based on the KMAPP along the glide paths heading to the CJU is developed and evaluated using the AMDAR observation data. The KMAPP-LLWS developed in this paper successfully detected the moderate-or-greater wind shear (strong than 5 knots per 100 feet) occurred on the glide paths at CJU. In particular, this wind shear prediction system showed better performance than conventional 1-D column-based wind shear forecast.

• Wind and Structures
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• v.15 no.1
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• pp.43-64
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• 2012

Many potential small wind turbine locations are near obstacles such as buildings and shelterbelts, which can have a significant, detrimental effect on the local wind climate. A neural network-based model has been developed which predicts mean wind speed and turbulence intensity at points in an obstacle's region of influence, relative to unsheltered conditions. The neural network was trained using measurements collected in the wakes of 18 scale building models exposed to a simulated rural atmospheric boundary layer in a wind tunnel. The model obstacles covered a range of heights, widths, depths, and roof pitches typical of rural buildings. A field experiment was conducted using three unique full scale obstacles to validate model predictions and wind tunnel measurements. The accuracy of the neural network model varies with the quantity predicted and position in the obstacle wake. In general, predictions of mean velocity deficit in the far wake region are most accurate. The overall estimated mean uncertainties associated with model predictions of normalized mean wind speed and turbulence intensity are 4.9% and 12.8%, respectively.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.222-234
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• 2021

Airborne-pests can be introduced into Korea from overseas areas by wind, which can cause considerable damage to major crops. Meteorological models have been used to estimate the wind trajectories of airborne insects. The objective of this study is to analyze the effect of input settings on the prediction of areas where airborne pests arrive by wind. The wind trajectories were predicted using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The HYSPLIT model was used to track the wind dispersal path of particles under the assumption that brown plant hopper (Nilaparvata lugens) was introduced into Korea from sites where the pest was reported in China. Meteorological input data including instantaneous and average wind speed were generated using meso-scale numerical weather model outputs for the domain where China, Korea, and Japan were included. In addition, the calculation time intervals were set to 1, 30, and 60 minutes for the wind trajectory calculation during early June in 2019 and 2020. It was found that the use of instantaneous and average wind speed data resulted in a considerably large difference between the arrival areas of airborne pests. In contrast, the spatial distribution of arrival areas had a relatively high degree of similarity when the time intervals were set to be 1 minute. Furthermore, these dispersal patterns predicted using the instantaneous wind speed were similar to the regions where the given pest was observed in Korea. These results suggest that the impact assessment of input settings on wind trajectory prediction would be needed to improve the reliability of an approach to predict regions where airborne-pest could be introduced.

• Asian Journal of Atmospheric Environment
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• v.10 no.2
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• pp.80-89
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• 2016

In the complex terrain where local wind systems are formed, accurate understanding of regional wind variability is required for wind resource assessment. In this paper, cluster analysis based on the similarity of time-series wind vector was applied to classify wind regions with similar wind characteristics and the meteorological validity of regionalization method was evaluated. Wind regions in Jeju Island and Busan were classified using the wind resource map of Korea created by a mesoscale numerical weather prediction modeling. The evaluation was performed by comparing wind speed, wind direction, and wind variability of each wind region. Wind characteristics, such as mean wind speed and prevailing wind direction, in the same wind region were similar and wind characteristics in different wind regions were meteor-statistically distinct. It was able to identify a singular wind region at the top area of Mt. Halla using the inconsistency of wind direction variability. Furthermore, it was found that the regionalization results correspond with the topographic features of Jeju Island and Busan, showing the validity.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• Wind and Structures
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• v.11 no.1
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• pp.35-50
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• 2008

Wind and temperature have been shown to be the critical sources causing changes in the modal properties of large-scale bridges. While the individual effects of wind and temperature on modal variability have been widely studied, the investigation about the effects of multiple environmental factors on structural modal properties was scarcely reported. This paper addresses the modeling of the simultaneous effects of wind and temperature on the modal frequencies of an instrumented cable-stayed bridge. Making use of the long-term monitoring data from anemometers, temperature sensors and accelerometers, a neural network model is formulated to correlate the modal frequency of each vibration mode with wind speed and temperature simultaneously. Research efforts have been made on enhancing the prediction capability of the neural network model through optimal selection of the number of hidden nodes and an analysis of relative strength of effect (RSE) for input reconstruction. The generalization performance of the formulated model is verified with a set of new testing data that have not been used in formulating the model. It is shown that using the significant components of wind speeds and temperatures rather than the whole measurement components as input to neural network can enhance the prediction capability. For the fundamental mode of the bridge investigated, wind and temperature together apply an overall negative action on the modal frequency, and the change in wind condition contributes less to the modal variability than the change in temperature.