The present paper is centered on the study to understand the behavior of various surfaces of a 'Z' planshaped tall building under varying wind directions. For that purpose, computational fluid dynamics (CFD) package of ANSYS is used. The length scale is considered as 1:300. Force coefficients both in the along and across wind direction as well as the external surface pressure coefficients for different faces of the object building are determined and listed for wind incidence angle 0°-150°with increment of 30°. The wind flow pattern around the building showing flow separation characteristics and vortices are presented. The variation of wind pressure on different surfaces of the building is clearly shown by contour plots. The nature of deviation of external pressure coefficients along the height of the building as well as along the perimeter of the building for different wind angles of attack is presented. The force coefficient (C f) along the X direction is extreme for 15°wind angle and along Y direction it is maximum for 60°angle of attack. Unsteady vortices are generated in the wake region due to a combination of positive and negative pressure in the windward and leeward faces, respectively.
The present study consists of shape optimization of a rectangular plan shaped tall building with horizontal limbs under wind attack, which would minimize the wind pressure on all the faces of the building model simultaneously. For the purpose, the external pressure coefficients on different faces of the building (Cpe) are selected as the objective functions. The position of the limbs and the wind incidence angle are taken as design variables. The design of experiment (DOE) is done using random sampling. The values of the objective functions are obtained by using Computational Fluid Dynamics method of simulated wind flow at each design point. The building model has a constant plan area 22500 mm2. The length and velocity scales are taken as 1:300 and 1:5, respectively. The results are used to construct the surrogate models of the objective functions using Response Surface Approximation method. The optimization study is done using the Multi-Objective Genetic Algorithm. The building shapes corresponding to the Pareto optimal decision variables are shown. The function values corresponding to the decision variables are verified by further introducing a CFD study.
Summary For tall buildings, values of wind force coefficients can be obtained from wind tunnel tests or Computational Fluid Dynamics (CFD). This paper is concentrated to analyze a set of CFD data and propose parametric equations for determining force coefficients in the alongwind and crosswind direction (Cfx and Cfy) of tall buildings with horizontal limbs. Initially, a parametric study is performed with CFD analysis considering RANS k − ε turbulence models keeping a constant plan area 22,500 mm2. The length and velocity scales are taken as 1:300 and 1:5, respectively. The required design parameters are obtained and used for fitting parametric equations. The CFD data are further utilized for training artificial neural networks of Cfx and Cfy. The results of CFD, ANN, and parametric equations are compared. The parametric equations are validated by employing a wind tunnel study. Finally, three optimization studies are carried out using a genetic algorithm (GA), of which the first two aim to present the maximum and minimum force coefficients considering single objectives. The third optimization is a multi‐objective optimization problem, carried out to simultaneously minimize and maximize the two orthogonal force coefficients. Pareto‐optimal design results are presented.
The present paper focuses on the study of wind-induced responses of cross-plan shaped tall buildings. Initially, three parametric building models are studied for the purpose with a constant plan area 22500 mm2. The length and velocity scales are taken as 1:300 and 1:5, respectively. Wind angle of attack (WAA) is considered from 0° to 330° with an increment of 30°. At first, the external surface pressure coefficients (Cp) at different faces of the models are carried out for different wind occurrence angles employing Computational Fluid Dynamics method of simulated wind flow. Again, Fast Fourier Transform (FFT) fitted expressions as the sine and cosine function of WAA are proposed for attaining mean wind pressure coefficient on the building faces. The accuracy of the Fourier series expansions is justified by presenting histograms of sum square error (SSE), R2 value and root mean square error (RMSE). The results are also compared by training Artificial Neural Networks (ANN). Training is continued till Regression (R) values are more than 0.99 and Mean Squared Error (MSE) tends to 0, ensuring a close relationship among the outputs and targets. The face-wise value of (Cp) obtained using all three methods, are plotted. The error histograms of the ANN models show that the fitting data errors are spread within a reasonably good range. It is observed that the deviation in the result is not more than 5% in any case. Finally, the ANN predictions are presented for nine parametric models to cover a wide range of possible cross-shaped buildings.
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