• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.42-46
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• 2005

In spite of fast growing of prediction codes, there is still not negligible uncertainty in their results. This uncertainty affects on the turbine structural design and power production prediction. With the growing size of wind turbine, reducing this uncertainty is becoming one of critical issues for high performance and efficient wind turbine design. In this respect, there are international efforts to evaluate and tune prediction codes of wind turbine. As the reference data for this purpose, field test data is not appropriate because of its uncontrollable wind characteristics and its inherent uncertainty. Wind tunnel can provide controllable wind. For this reason, NREL has done the full scale test of the 10m turbine at NASA-Ames. With this reference data, a blind comparison has been done with participation of 18 organizations with 19 modeling tools. The results were not favorable. In Europe, a similar project is going on. Nine organizations from five countries are participating in the MEXICO project to do full scale wind tunnel tests and calculation with prediction codes. In this study. these two projects were reviewed in respect of wind tunnel test and its contribution. As a conclusion, it is suggested that scale model wind tunnel tests can be a complementary tool to calculation codes which were evaluated worse than expected.

• Journal of Wind Energy
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• v.15 no.1
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• pp.11-20
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• 2024

As the generation capacity of floating offshore wind turbines increases, the wind load applied to each turbine increases. Due to such a high wind load, the capacity of transport equipment (such as tugboats or cranes) required in the transportation and installation phases must be much larger than that of previous small-capacity wind power generation systems. However, for such an important wind load prediction method, the simple formula proposed by the classification society is generally used, and prediction through wind tunnel tests or Computational Fluid Dynamics (CFD) is rarely used, especially for a concept or initial design stages. In this study, the wind load of a 10 MW class floating offshore wind turbine was predicted by a simplified formula and compared with results of wind tunnel tests. In addition, the wind load coefficients at each stage of fabrication, transportation, and installation are presented so that it can be used during a concept or initial design stages for similar floating offshore wind turbines.

• 한국전산유체공학회:학술대회논문집
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• 2004.03a
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• pp.17-22
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• 2004

A space launch vehicle departs the ground in a low speed, soon reaches a transonic and a supersonic speed, and then flies in a hypersonic speed into the space. Therefore, the design of a launch vehicle should include the prediction of aerodynamic characteristics for all speed regimes, ranging from subsonic to hypersonic speed. Generally, Empirical and analytical methods and wind tunnel tests are used for the prediction of aerodynamic characteristics. This research presents considerable factors for aerodynamic analysis of a launch vehicle using CFD. This investigation was conducted to determine effects of wake over the base section on the aerodynamic characteristics of a launch vehicle and also performed to determine effects of the sting which exist to support wind tunnel test model.

• Wind and Structures
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• v.15 no.6
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• pp.495-508
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• 2012

The present paper is focused on the prediction of the acrosswind aeroelastic response of square tall buildings. In particular, a semi-analytical procedure is proposed based on the assumption that square tall buildings, for reduced velocities corresponding to operational conditions, do not experience vortex shedding resonance or galloping and fall in the range of positive aerodynamic damping. Under these conditions, aeroelastic wind tunnel tests can be unnecessary and the response can be correctly evaluated using wind tunnel tests on rigid models and analytical modeling of the aerodynamic damping. The proposed procedure consists of two phases. First, simultaneous measurements of the pressure time histories are carried out in the wind tunnel on rigid models, in order to obtain the aerodynamic forces. Then, aeroelastic forces are analytically evaluated and the structural response is computed through direct integration of the equations of motion considering the contribution of both the aerodynamic and aeroelastic forces. The procedure, which gives a conservative estimate of the aeroelastic response, has the advantage that aeroelastic tests are avoided, at least in the preliminary design phase.

• Wind and Structures
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• v.32 no.4
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• pp.293-308
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• 2021

For the past three decades a significant amount of research has been conducted on bridge flutter. Wind tunnel tests for a 2000 m class twin-box suspension bridge have revealed that a twin-box deck carrying 4 m tall 50% open area ratio wind screens at the deck edges achieved higher critical wind speeds for onset of flutter than a similar deck without wind screens. A result at odds with the well-known behavior for the mono-box deck. The wind tunnel tests also revealed that the critical flutter wind speed increased if the bridge deck assumed a nose-up twist relative to horizontal when exposed to high wind speeds - a phenomenon termed the "nose-up" effect. Static wind tunnel tests of this twin-box cross section revealed a positive moment coefficient at 0° angle of attack as well as a positive moment slope, ensuring that the elastically supported deck would always meet the mean wind flow at ever increasing mean angles of attack for increasing wind speeds. The aerodynamic action of the wind screens on the twin-box bridge girder is believed to create the observed nose-up aerodynamic moment at 0° angle of attack. The present paper reviews the findings of the wind tunnel tests with a view to gain physical insight into the "nose-up" effect and to establish a theoretical model based on numerical simulations allowing flutter predictions for the twin-box bridge girder.

• Wind and Structures
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• v.36 no.6
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• pp.367-377
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• 2023

This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to full-scale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.

• Journal of computational fluids engineering
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• v.17 no.1
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• pp.54-60
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• 2012

Transition prediction results are validated with experimental data obtained from a transonic wind tunnel for the KU109C airfoil. A Reynolds-Averaged Navier-Stokes code is simultaneously coupled with the transition transport model of Langtry and Menter and applied to the numerical prediction of aerodynamic performance of the KU109C airfoil. Drag coefficients from the experiment are better correlated to the numerical prediction results using a transition transport model rather than the fully turbulent simulation results. Maximum lift coefficient and drag divergence at the zero-lift condition with Mach number are investigated. Through the present validation procedure, the accuracy and usefulness of both the experiment and the numerical prediction are assessed.

• 한국전산유체공학회:학술대회논문집
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• 2007.10a
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• pp.167-170
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• 2007

In the present paper, wall correction method is reviewed and applied to the numerical experimental results obtained at the wind tunnel condition. The corrected lift coefficient agrees well with the reference data generated from the grid having very far boundary. However the corrected drag coefficient presents some deviation from the reference data.

• 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.

• 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.