科创中国

Published studies in regard to coupler systems have been mainly focused on the manufacturing process or coupler strength issues. With the ever increasing of tonnage and length of heavy haul trains, lateral in-train forces generated by longitudinal in-train forces and coupler rotations have become a more and more significant safety issue for heavy haul train operations. Derailments caused by excessive lateral in-train forces are frequently reported. This article studies two typical coupler systems used on heavy haul locomotives. Their structures and stabilizing mechanism are analyzed before the corresponding models are developed. Coupler systems models are featured by two distinct stabilizing mechanism models and draft gear models with hysteresis considered. A model set which consists of four locomotives and three coupler systems is developed to study the rotational behavior of different coupler systems and their implications for locomotive dynamics. Simulated results indicate that when the locomotives are equipped with the type B coupler system, locomotives can meet the dynamics standard on tangent tracks; while the dynamics performance on curved tracks is very poor. The maximum longitudinal in-train force for locomotives equipped with the type B coupler system is 2000 kN. Simulations revealed a distinct trend for the type A coupler system. Locomotive dynamics are poorer for the type A case when locomotives are running on tangent tracks, while the dynamics are better for the type A case when locomotives are running on curved tracks. Theoretical studies and simulations carried out in this article suggest that a combination of the two types of stabilizing mechanism can result in a good design which can significantly decrease the relevant derailments.

The classical natural coordinate modeling method which removes the Euler angles and Euler parameters from the governing equations is particularly suitable for the sensitivity analysis and optimization of multibody systems. However, the formulation has so many principles in choosing the generalized coordinates that it hinders the implementation of modeling automation. A first order direct sensitivity analysis approach to multibody systems formulated with novel natural coordinates is presented. Firstly, a new selection method for natural coordinate is developed. The method introduces 12 coordinates to describe the position and orientation of a spatial object. On the basis of the proposed natural coordinates, rigid constraint conditions, the basic constraint elements as well as the initial conditions for the governing equations are derived. Considering the characteristics of the governing equations, the newly proposed generalized-α integration method is used and the corresponding algorithm flowchart is discussed. The objective function, the detailed analysis process of first order direct sensitivity analysis and related solving strategy are provided based on the previous modeling system. Finally, in order to verify the validity and accuracy of the method presented, the sensitivity analysis of a planar spinner-slider mechanism and a spatial crank-slider mechanism are conducted. The test results agree well with that of the finite difference method, and the maximum absolute deviation of the results is less than 3%. The proposed approach is not only convenient for automatic modeling, but also helpful for the reduction of the complexity of sensitivity analysis, which provides a practical and effective way to obtain sensitivity for the optimization problems of multibody systems.

Cyber physical systems(CPS)recently emerge as a new technology which can provide promising approaches to demand side management(DSM),an important capability in industrial power systems.Meanwhile,the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics.DSM,integrated with CPS,is an effective methodology for solving energy optimization problems in manufacturing center.This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems.To gain prior knowledge of DSM operating results,a sparse Bayesian learning based componential forecasting method is introduced to predict24-hour electric load levels for specific industrial areas in China.From this data,a pricing strategy is designed based on short-term load forecasting results.To minimize total energy costs while guaranteeing manufacturing center service quality,an adaptive demand side energy optimization algorithm is presented.The proposed scheme is tested in a machining center energy optimization experiment.An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center.Based on the data collected from the sensing system,the load prediction-based energy optimization scheme is implemented.By employing both the PSO and the CPSO method,the problem of DSM in the manufacturing center is solved.The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5%compared with the traditional PSO optimization method.