Qiusheng Li

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

Large eddy simulation of wind loads on a long-span spatial lattice roof

Abstract: The 486m-long roof of Shenzhen Citizens Centre is one of the world`s longest spatial lattice roof structures. A comprehensive numerical study of wind effects on the long-span structure is presented in this paper. The discretizing and synthesizing of random flow generation technique (DSRFG) recently proposed by two of the authors (Huang and Li 2008) was adopted to produce a spatially correlated turbulent inflow field for the simulation study. The distributions and characteristics of wind loads on the roof were numerically evaluated by Computational Fluid Dynamics (CFD) methods, in which Large Eddy Simulation (LES) and Reynolds Averaged Navier-Stokes Equations (RANS) Model were employed. The main objective of this study is to explore a useful approach for estimations of wind effects on complex curved roof by CFD techniques. In parallel with the numerical investigation, simultaneous pressure measurements on the entire roof were made in a boundary layer wind tunnel to determine mean, fluctuating and peak pressure coefficient distributions, and spectra, spatial correlation coefficients and probability characteristics of pressure fluctuations. Numerical results were then compared with these experimentally determined data for validating the numerical methods. The comparative study demonstrated that the LES integrated with the DSRFG technique could provide satisfactory prediction of wind effects on the long-span roof with complex shape, especially on separation zones along leading eaves where the worst negative wind-induced pressures commonly occur. The recommended LES and inflow turbulence generation technique as well as associated numerical treatments are useful for structural engineers to assess wind effects on a long-span roof at its design stage.

Quantitative assessment of offshore wind speed variability using fractal analysis

Abstract: Proper understanding of offshore wind speed variability is of essential importance in practice, which provides useful information to a wide range of coastal and marine activities. In this paper, long-term wind speed data recorded at various offshore stations are analyzed in the framework of fractal dimension analysis. Fractal analysis is a well-established data analysis tool, which is particularly suitable to determine the complexity in time series from a quantitative point of view. The fractal dimension is estimated using the conventional box-counting method. The results suggest that the wind speed data are generally fractals, which are likely to exhibit a persistent nature. The mean fractal dimension varies from 1.31 at an offshore weather station to 1.43 at an urban station, which is mainly associated with surface roughness condition. Monthly variability of fractal dimension at offshore stations is well-defined, which often possess larger values during hotter months and lower values during winter. This is partly attributed to the effect of thermal instability. In addition, with an increase in measurement interval, the mean and minimum fractal dimension decrease, whereas the maximum and coefficient of variation increase in parallel.

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)