• International Journal of High-Rise Buildings
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• v.6 no.1
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• pp.49-72
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• 2017

The Guangzhou International Finance Centre (IFC) is a landmark building that symbolizes the emerging international strength of Guangzhou, China's third largest city. It is also one of the dual iconic towers along the main axis of Guangzhou Zhujiang New Town. Arup adopted a total engineering approach in embracing sustainability and aiming at high efficiency solutions based on performance-based design principles covering structures, building services, fire engineering, vertical transportation, and façade performance to constitute an efficient and cost-effective overall building design. Through dynamic integration of architectural and engineering principles, Guangzhou IFC represents a pioneering supertall building in China. It adopts a diagrid exoskeleton structural form that is clearly expressed through the building's façade and gives the building its distinctive character. The aerodynamic shape of the building not only presents the aesthetic quality of elegant simplicity, but also reduces the effects of wind, thereby reducing the size and weight of the structure. State-of-the-art advanced engineering methods, such as optimization techniques and nonlinear finite element modelling, were applied in parallel with large-scale experimental programs to achieve an efficient and high-performance design taking into account the constructability and cost-effectiveness for a project of this scale.

• The KSFM Journal of Fluid Machinery
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• v.18 no.4
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• pp.43-48
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• 2015

In this study, a method for optimal design of impeller shroud for centrifugal compressor using response surface method (RSM) and multi-objective genetic algorithm (MOGA) was studied. Numerical simulation was conducted using ANSYS CFX with various configurations of shroud. Each of the design parameters was divided into 3 levels. Total 15 design points were planned by central composite design (CCD) method, which is one of the design of experiment (DOE) techniques. Response surfaces based on the results of DOE were used to find the optimal shape of impeller shroud for high aerodynamic performance. The whole process of optimization was conducted using ANSYS Design Xplorer (DX). Results showed that the isentropic efficiency, which is the main performance parameter of the centrifugal compressor, was increased 0.4% through the optimization.

• Wind and Structures
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• v.28 no.3
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• pp.191-201
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• 2019

In this paper, the aerodynamic analysis of a vertical axis wind turbine was carried out by CFD approach to optimize the turbine performance. To perform numerical simulation, SST-Transition turbulence model was used, which demonstrated more precise results compared to non-transition models. A parametric study was conducted to optimize the VAWT performance based on the selected model. The investigation of pitch angle changes showed that the highest power produced by the turbine occurs at $2^{\circ}$ angle. Considering the effect of the rotor's arm junction to the airfoil showed that by increasing the distance of the junction from the edge of the airfoil from 25 cm to 40 cm, the power of the turbine increases by 60%. However, further increase in this distance results in power decrease. Based on the proposed numerical model, a case study was conducted to consider the installation of four VAWTs in the southwest corner of the medical science building at TMU campus with a height of 42m. The results of the simulation showed that 8.27 MWh energy is obtainable annually.

• Wind and Structures
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• v.36 no.6
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• pp.393-404
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• 2023

Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NIST-UWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively.