A Case History of Ground Treatment for a Power Station in China

This paper presents a case history of ground treatment for a large power station - Zhang Ze Power Station, which locates in the south-east region in Shanxi, P. R. China. In the second phase of this project, a comprehensive treatment program including pile foundation, dynamic consolidation, and natural foundation has been used successfully according to significance of structure, conditions of loading, characters of technology, and so on. After 6 years working, the Station operates normally, and settlements of structures distribute well, proving the program is reasonable, workable, and economic. It is suggested that the choice of foundation treatment in a large project must consider comprehensively various factors, such as sorts of structure, types of loading, and geology in site, cost of construction, environment of site, etc

A Novel Space-Time-Speed Method for Increasing the Passing Capacity with Safety Guaranteed of Railway Station

A method for improving the passing capacity of a station without adding any track and equipment is proposed in this paper. In the process of handling train routes, by transforming the existing fixed train-approaching locking section into a variable mode, the route locking time is shortened and in-station resource consumption is reduced. This approach improves the capacity of the station. At the same time, delay of the train can be quickly returned to normal. A method of variable train-approaching locking section is discussed; a mathematical model for increasing station passing capacity is shown. Comparison between the impact of a variable train-approaching locking section and a fixed mode on the station passing capacity is shown

Covariance localization in the ensemble transform Kalman filter based on an augmented ensemble

With the increased density of available observation data, data assimilation has become an increasingly important tool in marine research. However, the success of the ensemble Kalman filter is highly dependent on the size of the ensemble. A small ensemble used in data assimilation could cause filter divergence, undersampling and spurious correlations. The primary method to alleviate these problems is localization. It can eliminate some spurious correlations and increase the rank of the forecast error covariance matrix. The ensemble transform Kalman filter has been widely used in various studies as a deterministic filter. Unfortunately, the covariance localization cannot be directly applied to ensemble transform Kalman filter. The new covariance localization needs to be presented to adapt the ensemble transform Kalman filter. Based on the method of expanded ensemble and eigenvalue decomposition, this study describes a variation of covariance localization that takes advantage of an unbiased covariance matrix from the expanded ensemble. Experiments described herein show that the new method outperforms the localization methods proposed by others when used in the ensemble transform Kalman filter. The new method yields an analysis estimate that is closer to the true state under different experimental conditions