Qiusheng Li

Bio: Qiusheng Li is an academic researcher from City University of Hong Kong. The author has contributed to research in topic(s): Wind speed & Wind tunnel. The author has an hindex of 47, co-authored 429 publication(s) receiving 8830 citation(s). Previous affiliations of Qiusheng Li include Chinese Ministry of Education & Guangzhou University.

Thermal Reduction Route to the Fabrication of Coaxial Zn/ZnO Nanocables and ZnO Nanotubes

Abstract: Coaxial Zn/ZnO nanocables and ZnO nanotubes have been fabricated via a thermal reduction route using ZnS powder as the source material. The samples were characterized using X-ray powder diffraction, scanning electron microscopy, transmission electron microscopy, and energy-dispersive X-ray spectrometry. The as-synthesized Zn/ZnO nanocables consisted of a metallic core (Zn) ≈50 nm in diameter and a semiconductor outer shell (ZnO) ≈5 nm in thickness and several micrometers in length. A good epitaxial relationship between the Zn core and ZnO shell was observed, and misfit dislocations were observed at the Zn/ZnO interface, which accommodated the relatively large lattice mismatch. The outer diameter and wall thickness of the ZnO nanotubes are ≈60 and ≈10 nm, respectively. The possible formation mechanisms for the Zn/ZnO nanocables and ZnO nanotubes are discussed.

Numerical evaluation of wind effects on a tall steel building by CFD

Abstract: A comprehensive numerical study of wind effects on the Commonwealth Advisory Aeronautical Council (CAARC) standard tall building is presented in this paper. The techniques of Computational Fluid Dynamics (CFD), such as Large Eddy Simulation (LES), Reynolds Averaged Navier–Stokes Equations (RANS) Model etc., were adopted in this study to predict wind loads on and wind flows around the building. The main objective of this study is to explore an effective and reliable approach for evaluation of wind effects on tall buildings by CFD techniques. The computed results were compared with extensive experimental data which were obtained at seven wind tunnels. The reasons to cause the discrepancies of the numerical predictions and experimental results were identified and discussed. It was found through the comparison that the LES with a dynamic subgrid-scale (SGS) model can give satisfactory predictions for mean and dynamic wind loads on the tall building, while the RANS model with modifications can yield encouraging results in most cases and has the advantage of providing rapid solutions. Furthermore, it was observed that typical features of the flow fields around such a surface-mounted bluff body standing in atmospheric boundary layers can be captured numerically. It was found that the velocity profile of the approaching wind flow mainly influences the mean pressure coefficients on the building and the incident turbulence intensity profile has a significant effect on the fluctuating wind forces. Therefore, it is necessary to correctly simulate both the incident wind velocity profile and turbulence intensity profile in CFD computations to accurately predict wind effects on tall buildings. The recommended CFD techniques and associated numerical treatments provide an effective way for designers to assess wind effects on a tall building and the need for a detailed wind tunnel test.

A new artificial neural network-based response surface method for structural reliability analysis

Abstract: This paper presents a new artificial neural network-(ANN)based response surface method in conjunction with the uniform design method for predicting failure probability of structures. The method involves the selection of training datasets for establishing an ANN model by the uniform design method, approximation of the limit state function by the trained ANN model and estimation of the failure probability using first-order reliability method (FORM). In the proposed method, the use of the uniform design method can improve the quality of the selected training datasets, leading to a better performance of the ANN model. As a result, the ANN dramatically reduces the number of required trained datasets, and shows a good ability to approximate the limit state function and then provides a less rigorous formulation in the context of FORM. Results of three numerical examples involving both structural and non-structural problems indicate that the proposed method provides accurate and computationally efficient estimates of the probability of failure. Compared with the conventional ANN-based response surface method, the proposed method is much more economical to achieve reasonable accuracy when dealing with problems where closed-form failure functions are not available or the estimated failure probability is extremely small. Finally, several important parameters in the proposed method are discussed.

A general inflow turbulence generator for large eddy simulation

Abstract: This paper presents a general inflow turbulence generator for numerical simulation of a spatially correlated turbulent flow field. The novel inflow turbulence generator is developed based on the discretizing and synthesizing random flow generation (DSRFG) technique that is proved to be able to generate a fluctuating turbulent flow field satisfying any given spectrum. Then, the techniques of aligning and remapping are incorporated in the inflow turbulence generator for generation of an inhomogeneous and anisotropic turbulent flow field following arbitrary target spectra in three orthogonal directions. The performance of the present inflow turbulence generator is compared with that of Smirnov’s random flow generation (RFG) method. Detailed numerical studies show that the proposed inflow turbulence generator can strictly guarantee divergence-free condition without any additional improving step and synthetically generate inflows satisfying prescribed spatial anisotropy and correlation conditions. It is demonstrated through numerical examples that the inflow turbulence generator developed in this study is an effective tool for generation of a spatially correlated turbulent flow field for large eddy simulation (LES).

Statistical analysis of wind characteristics and wind energy potential in Hong Kong

Abstract: The harvesting of renewable energy sources has become increasingly important to take account of the gradual decline of fossil fuel reserves and the environment degradation associated with the use of fossil fuels. Wind energy, as one of the most well-known renewable energy sources, has been extensively harnessed across the world. Nevertheless, the wind energy exploitation in Hong Kong is still rare. Based on 6-year wind data recorded at five meteorological stations with different terrain conditions, this study presents a statistical analysis of the wind characteristics and wind energy potential at typical sites in Hong Kong by the assistance of Weibull distribution model. The variations of mean wind speed, as well as Weibull parameters, were highlighted on various timescales. Among all the sites, the annual Weibull scale parameter varied from 2.85 m/s to 10.19 m/s, and the range of the annual shape parameter was 1.65–1.99. The highest Weibull scale parameter was observed at a hilltop, whilst the lowest was found at an urban site. The monthly variation of wind power density was presented and discussed for each site. Hilltops and offshore islands demonstrated prominently greater wind power density than urban areas. It was thus indicated that hilltops and offshore islands are the most promising locations for wind energy exploitation in Hong Kong.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Nonlinear Time Series Analysis.

Abstract: : This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)