Applying System Dynamics of Discrete Supported Track to Analyze the Rail Corrugation Causation on Curved Urban Railway Tracks

Urban rail corrugation on curved tracks with small radii causes strong howling during operation, which has been bothering subway operating companies for many years. Therefore, revealing its causes and growth is important for the comfort and safety of subway operation. Current studies believe that the occurrence of rail corrugation is largely due to the resonant vibration of the wheel-rail system. However, little attention has been paid to the key causes of the track resonance and the practical prediction of the occurrence probability of rail corrugation on the certain track. This paper intends to solve these above issues. Firstly, the practical model of predicting the rail corrugation growth is proposed based on the wheel-rail coupling interaction, the key causes of corrugation are investigated, and the sensitivity analysis is carried out, while the corrugation superposition model is introduced to the analyze the corrugation evolution as well as to validate the corrugation growth from the aspect of material friction and wear. Secondly, the impact of the key causes on the initiation and development of the rail corrugation is investigated based on the cosimulation. Finally, case studies validate the proposed theory model and method. The results show that the practical prediction model for the rail corrugation growth proposed in this paper is able to estimate the occurrence possibility of rail corrugation on a specific track, and the superharmonic resonance of the track directly excited by passing vehicles eventually leads to rail corrugation. It is also found that shortwave corrugation develops more rapidly, and adjusting the support stiffness or sleeper spacing leads to fluctuations in the corrugation wavelength and its wear rate.

Prediction of the remaining service lifetime of inflatable rubber dam with deep hole damage

This paper exhibits a method to predict the remaining service lifetime of inflatable rubber dam by considering the appearance of deep hole damage. The material used for the rubber dam is a composite comprising three layers of woven fabric as fiber and EPDM/SBR 64 474 rubber as a matrix. The service lifetime is predicted by calculating the degradation of rubber dam’s material properties. Simple Rate Law model and Time-Temperature Superposition model are employed to calculate the rubber properties degradation. A finite element analysis is then conducted to investigate stress and strain distributions which occur in the rubber dam membrane during operational loading. Furthermore, the effect of deep hole damage in the rubber dam, which is caused by improper maintenance, is modeled as well. The results show that a 7 mm depth of the hole can accelerate rubber degradation, which causes catastrophic failure. This can happen because two layers of the woven fabric in the rubber dam have been broken. Suggestion to hold up the degradation is also discussed.

Structural Analysis of the BaWO4 Crystal Doped with Ce and Codoped with Na Ions Based on g-shift Parameters

The relationship between the g-shift and the local structure of the Ce3+ paramagnetic center with axial symmetry were investigated for four BaWO4 single crystals doped with Ce and codoped with Na. Based on g-shift the displacements of Ce3+ ions are determined. The g-shift method yields displacements of impurity ions in good agreement with the superposition model (SPM) and the perturbation methods (PM) predictions. The structural analysis of the paramagnetic ions and its surrounding in the BaWO4 unit cell was also conducted.

Rain Detection and Removal via Shrinkage-based Sparse Coding and Learned Rain Dictionary

Rain removal is essential for achieving autonomous driving because it preserves the details of objects that are useful for feature extraction and removes the rain structures that hinder feature extraction. Based on a linear superposition model in which the observed rain image is decomposed into two layers, a rain layer and a non-rain layer, conventional rain removal methods estimate these two layers alternatively from an observed single image based on prior modeling. However, the prior knowledge used for the rain structures is not always correct because various types of rain structures can be observed in the rain images, which results in inaccurate rain removal. Therefore, in this article, a novel rain removal method based on the use of a scribbled rain image set and a new shrinkage-based sparse coding model is proposed. The scribbled rain images have information about which pixels have rain structures. Thus, various types of rain structures can be modeled, owing to the abundance of rain structures in the rain image set. To detect the rain regions, two types of approaches, one based on reconstruction error comparison (REC) via a learned rain dictionary and the other based on a deep convolutional neural network (DCNN), are presented. With the rain regions, the proposed shrinkage-based sparse coding model determines how much to reduce the sparse codes of the rain dictionary and maintain the sparse codes of the non-rain dictionary for accurate rain removal. Experimental results verified that the proposed shrinkage-based sparse coding model could remove rain structures and preserve objects’ details due to the REC- or DCNN-based rain detection using the scribbled rain image set. Moreover, it was confirmed that the proposed method is more effective at removing rain structures from similar objects’ structures than conventional methods.