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CONTENTS
Volume 34, Number 6, June 2022
 


Abstract
The generalized extreme value distribution (GEVD) is frequently used to fit the block maximum of environmental parameters such as the annual maximum wind speed. There are several methods for estimating the parameters of the GEV distribution, including the least-squares method (LSM). However, the application of the LSM with the expected order statistics has not been reported. This study fills this gap by proposing a fitting method based on the expected order statistics. The study also proposes a plotting position to approximate the expected order statistics; the proposed plotting position depends on the distribution shape parameter. The use of this approximation for distribution fitting is carried out. Simulation analysis results indicate that the developed fitting procedure based on the expected order statistics or its approximation for GEVD is effective for estimating the distribution parameters and quantiles. The values of the probability plotting correlation coefficient that may be used to test the distributional hypothesis are calculated and presented. The developed fitting method is applied to extreme thunderstorm and non-thunderstorm winds for several major cities in Canada. Also, the implication of using the GEVD and Gumbel distribution to model the extreme wind speed on the structural reliability is presented and elaborated.

Key Words
annual maximum wind speed; generalized extreme value distribution; least-squares method; plotting position

Address
Y.X. Liu and H.P. Hong: Department of Civil and Environmental Engineering, University of Western Ontario, N6A 5B9, Canada

Abstract
Three-dimensional (3D) computational fluid dynamics (CFD) analysis of flow around a hipped-roof building representative of UK inland conditions are conducted. Unsteady simulations are performed using three variations of the k-∈ RANS turbulence model namely, the Standard, Realizable, and RNG models, and their predictive capability is measured against current European building standards. External pressure coefficients and wind loading are found through the BS 6399-2:1997 standard (obsolete) and the current European standards (BS EN 1991-1-4:2005 and A1:20101). The current European standard provides a more conservative wind loading estimate compared to its predecessor and the k-∈ RNG model falls within 15% of the value predicted by the current standard. Surface shear stream-traces and Q-criterion were used to analyze the flow physics for each model. The RNG model predicts immediate flow separation leading to the creation of vortical structures on the hipped-roof along with a larger separation region. It is observed that the Realizable model predicts the side vortex to be a result of both the horseshoe vortex and the flow deflected off it. These model-specific aerodynamic features present the most disparity between building standards at leeward roof locations. Finally, pedestrian comfort and safety criteria are studied where the k-∈ Standard model predicts the most ideal pedestrian conditions and the Realizable model yields the most conservative levels.

Key Words
CFD (Computational Fluid Dynamics); design codes and standards; pedestrian wind comfort; steady/ unsteady aerodynamic force; turbulence; wind loads

Address
Khalid Khalil, Huzafa Khan, Divyansh Chahar, Jamie F. Townsend and Zeeshan A. Rana: Centre for Computational Engineering Sciences, Cranfield University, Cranfield, Bedfordshire, MK43 0AL, United Kingdom

Abstract
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 highfidelity 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.

Key Words
2D RANS; bluff body aerodynamics; drag coefficient; wind engineering

Address
Gregorio Bertani, Luca Patruno:DICAM, University of Bologna, Bologna, Italy

Fernando Gandia Aguer:IDR/UPM, Universidad Politecnica de Madrid, Plaza Cardenal Cisneros 3, Madrid, Spain

Abstract
Detecting the icing on wind turbine blades built-in cold regions with conventional methods is always a very laborious, expensive and very difficult task. Regarding this issue, the use of smart systems has recently come to the agenda. It is quite possible to eliminate this issue by using the deep learning method, which is one of these methods. In this study, an application has been implemented that can detect icing on wind turbine blades images with visualization techniques based on deep learning using images. Pre-trained models of Resnet-50, VGG-16, VGG-19 and Inception-V3, which are well-known deep learning approaches, are used to classify objects automatically. Grad-CAM, Grad-CAM++, and Score-CAM visualization techniques were considered depending on the deep learning methods used to predict the location of icing regions on the wind turbine blades accurately. It was clearly shown that the best visualization technique for localization is Score-CAM. Finally, visualization performance analyses in various cases which are close-up and remote photos of a wind turbine, density of icing and light were carried out using Score-CAM for Resnet-50. As a result, it is understood that these methods can detect icing occurring on the wind turbine with acceptable high accuracy.

Key Words
convolutional neural networks; deep learning method; grad-CAM; icing; wind turbine; inception-V3; resnet-50; score-CAM; VGG-16; VGG-19

Address
Kemal Haciefendioglu, Hasan Basri Basaga, Selen Ayas:Department of Civil Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

Mohammad Tordi Karimi:Department of Computer Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

Abstract
High-rise wooden pagodas generate large displacement responses under wind action. It is necessary and wise to reduce the wind loads and wind-induced responses on the architectural heritage using artificial plants, which do not damage ancient architecture and increase greenery. This study calculates and analyzes the wind loads and wind-induced responses on the Yingxian Wooden Pagoda, in China, using artificial plants via the finite element analysis (FEA). A three-dimensional windloading field was simulated using a wind tunnel test. Wind loads and wind-induced responses, including the displacement and acceleration of the pagoda with and without artificial plants, were analyzed. In addition, three types of tree arrangements were discussed and analyzed using the score method. The results revealed that artificial plants can effectively control wind loads and wind-induced displacements, but the wind-induced accelerations are enlarged to some extent during the process. The height of the tree significantly affected the shelter effects of the structure. The distance of trees from the pagoda and arrangement width of the tree had less influence on shelter effects. This study extends the understanding of the nondestructive method based on artificial plants, for controlling the wind base loads and structural responses of wooden pagodas and preserving architectural heritage via FEA.

Key Words
artificial plants; finite element analysis (FEA); nondestructive method; wind-induced responses; wind loads; wooden pagoda

Address
Yuhang LI:School of Civil Engineering, Southeast University, Nanjing 211189, China

Yang DENG:1)Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
2)School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Aiqun LI:1)School of Civil Engineering, Southeast University, Nanjing 211189, China
2)Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
3)School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China


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