• Journal of Environmental Science International
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• v.18 no.8
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• pp.927-932
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• 2009

Wind turbine noise become main environmental problem as wind energy have been installed all around. Noise from large wind turbine give annoyance to listener, moreover it increase loading to whole system by restricting blade tip speed. However accurate noise mechanism of wind turbine is not yet examined. This paper reviewed noise source and analysis theory. Broadband noise if main component of wind turbine noise and airfoil self noise is main noise source. These make acoustic analogy hard to apply for analysis. For this reason, experimental equation is method for wind turbine noise prediction up to now. Spectrum analysis shows that vortex shedding noise exists around $1k{\sim}2k$ Hz. This region is most sensitive frequency range to human. Thus it is necessary to reduce this noise source.

• Wind and Structures
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• v.5 no.2_3_4
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• pp.151-164
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• 2002

Predictions of the pedestrian level wind speeds for the downtown area of Auckland that have been obtained by wind tunnel and computational fluid dynamic (CFD) modelling are presented. The wind tunnel method involves the observation of erosion patterns as the wind speed is progressively increased. The computational solutions are mean flow calculations, which were obtained by using the finite volume code PHOENICS and the $k-{\varepsilon}$ turbulence model. The results for a variety of wind directions are compared, and it is observed that while the patterns are similar there are noticeable differences. A possible explanation for these differences arises because the tunnel prediction technique is sensitivity to gust wind speeds while the CFD method predicts mean wind speeds. It is shown that in many cases the computational model indicates high mean wind speeds near the corner of a building while the erosion patterns are consistent with eddies being shed from the edge of the building and swept downstream.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.597-604
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• 2019

Artificial neural networks are algorithms that simulate learning through interaction and experience in neurons in the brain and that are a method that can be used to produce accurate results through learning that reflects the characteristics of data. In this study, a model using deep neural network was presented to improve the predicted wind speed values in the meteorological dynamic model. The wind speed prediction improvement model using the deep neural network presented in the study constructed a model to recalibrate the predicted values of the meteorological dynamics model and carried out the verification and testing process and Separate data confirm that the accuracy of the predictions can be increased. In order to improve the prediction of wind speed, an in-depth neural network was established using the predicted values of general weather data such as time, temperature, air pressure, humidity, atmospheric conditions, and wind speed. Some of the data in the entire data were divided into data for checking the adequacy of the model, and the separate accuracy was checked rather than being used for model building and learning to confirm the suitability of the methods presented in the study.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.33-36
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• 2007

We have persistently constructed gridded products of surface wind/wind stress over the world ocean using satellite scatterometer (ERS and Qscat). They are available for users as the Japanese Ocean Flux data sets with Use of Remote sensing Observation (J-OFURO) data together with heat flux components. Recently, a new version data of the Qscat/SeaWinds based on improved algorithm for rain flag and high wind-speed range have been delivered, and allowed us to reconstruct gridded product with higher spatial resolution. These products are validated by comparisons with in-situ measurement data by mooring buoys such as TAO/TRITON, NDBC and the Kuroshio Extension Observation (KEO) buoys, together with numerical weather prediction model products such as the NCEP-1 and 2. Results reveal that the new product has almost the same magnitude in mean difference as the previous version of Qscat product and much smaller than the NCEP-1 and 2. On the other hand, it is slightly larger root-mean-square (RMS) difference than the previous one and NCEPs for the comparison using the KEO buoy data. This may be due to the deficit of high wind speed data in the buoy measurement. The high resolution product, together with sea surface temperature (SST) one, is used to examine a new type of relationship between the lower atmosphere and upper ocean in the Kuroshio Extension region.

• International Journal of Fluid Machinery and Systems
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• v.7 no.1
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• pp.16-27
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• 2014

Double-blade vertical axis wind turbines (DB-VAWTs) can improve the self-starting performance of lift-driven VAWTs. We here propose the quadruple-multiple streamtube model (QMS), based on the blade element momentum (BEM) theory, for simulating DB-VAWT performance. Model validity is investigated by comparison to computational fluid dynamics (CFD) prediction for two kinds of two-dimensional DB-VAWT rotors for two rotor scales with three inner-outer radius ratios: 0.25, 0.5, and 0.75. The BEM-QMS model does not consider the effects of an inner rotor on the flow speed in the upwind half of the rotor, so we introduce a correction factor for this flow speed. The maximum power coefficient predicted by the modified BEM-QMS model for a DB-VAWT is thus closer to the CFD prediction.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.2
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• pp.149-158
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• 2023

To predict the aerodynamic characteristics of the grid fins from subsonic to supersonic speeds, low fidelity SW as well as CFD SW were applied. VLM(Vortex Lattice Method) and SE(Shock-Expansion) method were used at subsonic and supersonic speed domain respectively for the rapid prediction of low fidelity SW. For 2 configurations of the grid fins, the CFD computations and tests using the trisonic wind tunnel were also performed to compare the results of the grid fins. The results of low fidelity SW, CFD SW and the wind tunnel tests data were agreed well each other. Through further research on the grid fins, the effective parameters of the grid fin configurations according to the speed regime will be investigated.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.1
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• pp.105-110
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• 2020

This study provides a method and data for predicting the flight initiation wind speed of wind-borne debris. From the force equilibrium acting on debris including aerodynamic and inertia forces, the equation for predicting the flight initiation wind speeds are presented. Wind tunnel tests were carried out to provide necessary aerodynamic data in the equation for the debris with various aspect ratios. The proposed equation for flight initiation wind speeds was validated from free flying tests in the wind tunnel. The flights of debris were mostly initiated by slip when width to thickness was less than 10, otherwise overturning were dominant. The actual flight initiation speeds were lower than that of the computed ones. The surface boundary layer flow and the gap between the debris and surface might affect the prediction error.

• Proceedings of the KSRS Conference
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• v.1
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• pp.142-145
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• 2006

The synergism of active and passive microwave techniques for hurricane ocean wind remote sensing is explored. We performed the analysis of Windsat data for Atlantic hurricanes in 2003-2005. The polarimetric third Stokes parameter observations from the Windsat 10, 18 and 37 GHz channels were collocated with the ocean surface winds from the Holland wind model, the NOAA HWind wind vectors and the Global Data Assimilation System (GDAS) operated by the National Center for Environmental Prediction (NCEP). The collocated data were binned as a function of wind speed and wind direction, and were expanded by sinusoidal series of the relative azimuth angles between wind and observation directions. The coefficients of the sinusoidal series, corrected for atmospheric attenuation, have been used to develop an empirical geophysical model function (GMF). The Windsat GMF for extreme high wind compares very well with the aircraft radiometer and radar measurements.

• Wind and Structures
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• v.36 no.6
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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.

• Journal of Environmental Science International
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• v.8 no.3
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• pp.309-318
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• 1999

In order to fast predict the wind-driven current in a small bay, a convolution method in which the wind-driven current can be generated only wih the local wind is developed and applied in the Sachon Bay. The root mean square(rms) ratio defined as the ratio of the rms error to the rms speed is 0.37. The rms ratio is generally less than 0.2, except for all the mouths of Junju Bay and Namhae-do and in the region between Saryang Island and Sachon. The spatial average of the recover rate of kinetic energy(rrke) is 87%. Thus, the predicted wind-driven current by the convolution model is in a good agreement with the computed one by the numerical model. The raio of the difference between observed residual current (Vr) and predicted wind-driven current (Vc) to a residual current, that is, (Vr-Vc)/Vr shows 56%, 62% at 2 moorings in the Sachon Bay.