Enhancing wind performance of tall buildings using corner aerodynamic optimization

Elshaer, Ahmed; Bitsuamlak, Girma; Damatty, Ashraf El

doi:10.1016/j.engstruct.2017.01.019

Cited by 141 publications

(62 citation statements)

References 37 publications

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“…Particularly, in Hernández, Cid, Nieto, and Jurado (2016), an optimization of a long span bridge is proposed considering variations of the shape of circular segments as the ones studied in this work. In addition, some recent references propose the shape optimization of tall buildings cross-section, such as the works by Bernardini, Spence, Wei, and Kareem (2015) and Elshaer, Bitsuamlak, and El Damatty (2017), and deck cross-sections, such as Cid , taking advantage of the use of surrogate models. Determining the sensitivity of force coefficients with respect to shape variations of the cross-section under study is one of the current challenges in the wind engineering field.…”

Section: Summary Of the Numerical Resultsmentioning

confidence: 99%

Numerical simulations of the aerodynamic response of circular segments with different corner angles by means of 2D URANS. Impact of turbulence modeling approaches

Montoya

Nieto

Álvarez

et al. 2018

Engineering Applications of Computational Fluid Mechanics

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This paper presents the results of numerical and experimental investigations on the force coefficients and Strouhal numbers of circular segments considering different corner angles or chord to sagitta ratios. The research is motivated because these geometries are becoming increasingly popular in several engineering disciplines. The so-called D-section (semi-circular cylinder with a corner angle of 90 •) has been experimentally studied in the past, since it is a galloping prone geometry. However, there is a lack of research for cases with different corner angles, and the numerical investigations related to this topic are particularly scarce. In this work, a 2D Unsteady Reynolds Averaged Navier-Stokes approach has been adopted aiming to study the circular segments at the sub-critical regime, considering corner angles from 40 • to 90 • , and the flow parallel to the rectilinear side. These sections were found to be particularly challenging since they present massive flow separation on the rectilinear side, alongside the inherent difficulties related to modeling the flow along curved surfaces at high Reynolds numbers. The impact of introducing low-Reynolds-number and curvature corrections in the k − ω SST turbulence model and the performance of the Transition SST model have been extensively studied.

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Section: Summary Of the Numerical Resultsmentioning

confidence: 99%

Numerical simulations of the aerodynamic response of circular segments with different corner angles by means of 2D URANS. Impact of turbulence modeling approaches

Montoya

Nieto

Álvarez

et al. 2018

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

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“…Therefore, it is advisable to employ an advanced evaluation technique such as wind tunnel tests or CFD simulations to assess the design if the multivariable regression model predicts a negative AP com value. The multivariable regression model can be further improved by using more data obtained from CFD simulations (see Wen et al, 2017;Elshaer et al, 2017) and validating with respect to results of an advanced prediction tool such as the Artificial Neural Network (ANN) model (Elshaer et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Pedestrian-level wind conditions in the space underneath lift-up buildings

Zhang

Tse

Weerasuriya

et al. 2018

Journal of Wind Engineering and Industrial Aerodynamics

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“…Bitsuamlak et al suggested combined computational fluid dynamics and ANN approach for computing the wind pressure coefficients and investigated the effects of topography on wind flow for irregularly shaped buildings. Elshaer et al utilized ANN in building corner optimization technique to reduce the wind load effect on tall buildings.…”

Section: Introductionmentioning

confidence: 99%

“…Elshaer et al [28] utilized ANN in building corner optimization technique to reduce the wind load effect on tall buildings. [29] demonstrated an application of ANN to determine the dynamic along-wind response as per Indian Wind Code (IWC) and proposed graphs that can predict the dynamic along-wind responses.…”

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confidence: 99%

Dynamic wind response of tall buildings using artificial neural network

Nikose

Sonparote

2019

Structural Design Tall Build

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Summary This paper presents an alternative approach for predicting the dynamic wind response of tall buildings using artificial neural network (ANN). The ANN model was developed, trained, and validated based on the data generated in the context of Indian Wind Code (IWC), IS 875 (Part 3):2015. According to the IWC, dynamic wind responses can be calculated for a specific configuration of buildings. The dynamic wind loads and their corresponding responses of structures other than the specified configurations in IWC have to be estimated by wind tunnel tests or computational techniques, which are expensive and time intensive. Alternatively, ANN is an efficient and economical computational analysis tool that can be implemented to estimate the dynamic wind response of a building. In this paper, ANN models were developed to predict base shear and base bending moment of a tall building in along‐ and across‐wind direction by giving the input as the configuration of the building, wind velocity, and terrain category. Multilayer perceptron ANN models with back‐propagation training algorithm was adopted. On comparison of results, it was found that the predicted values obtained from the ANN models and the calculated responses acquired using IWC standards are almost similar. Using the best fit model of ANN, an extensive parametric study was performed to predict the dynamic wind response of tall buildings for the configurations on which IWC is silent. Based on the results obtained from this study, design charts are developed for the prediction of dynamic wind response of tall buildings.

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Enhancing wind performance of tall buildings using corner aerodynamic optimization

Cited by 141 publications

References 37 publications

Numerical simulations of the aerodynamic response of circular segments with different corner angles by means of 2D URANS. Impact of turbulence modeling approaches

Numerical simulations of the aerodynamic response of circular segments with different corner angles by means of 2D URANS. Impact of turbulence modeling approaches

Pedestrian-level wind conditions in the space underneath lift-up buildings

Dynamic wind response of tall buildings using artificial neural network

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