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In high-speed trains, the energy is supplied from a high voltage catenary to the vehicle via a pantograph catenary system (PCS). Carbon pantograph strips must maintain continuous contact with the wire to ensure safety and reliability. The contact is often confined to a particular spot, resulting in excessive wear due to mechanical and thermal damage, exacerbated by the presence of an electric arc and associated electrochemical corrosion. The effectiveness and reliability of the PCS impacts on the performance and safety of HSTs, especially under high-speed conditions. To alleviate some of these adverse effects, this paper aims to propose a configuration where a circular PCS replaces the currently used pantograph strips.

Two dynamic multi-physics models of a traditional PCS with a carbon strip and a novel PCS with a circular pantograph strip catenary system are established, and the electrical and mechanical characteristics of these two systems are compared. Moreover, a PCS experimental platform is designed to verify the validity and accuracy of the multi-physics model.

A novel circular pantograph system is proposed in this paper to alleviate some of the shortcomings of the traditional PCS. Comparing with a traditional PCS, the circular PCS exhibits superior performance in both electromagnetic and thermal aspects.

The paper offers a new technical solution to the PCS and develops a dedicated multi-physics model for analysis and performance prediction with the aim to improve the performance of the PCS. The new system offers numerous benefits, such as less friction heat, better heat dispersion and improved catenary-tracking performance.

The increasing demands for customized services and frequent market variations have posed challenges to managing and controlling the manufacturing processes. Despite the developments in literature in this area, less consideration has been devoted to the growth of business social networks, cloud computing, industrial Internet of things and intelligent production systems. This study recognizes the primary factors and their implications for intelligent production systems' success. In summary, the role of cloud computing, business social network and the industrial Internet of things on intelligent production systems success has been tested.

Intelligent production systems are manufacturing systems capable of integrating the abilities of humans, machines and processes to lead the desired manufacturing goals. Therefore, identifying the factors affecting the success of the implementation of these systems is necessary and vital. On the other hand, cloud computing and the industrial Internet of things have been highly investigated and employed in several domains lately. Therefore, the impact of these two factors on the success of implementing intelligent production systems is examined. The study is descriptive, original and survey-based, depending on the nature of the application, its target and the data collection method. Also, the introduced model and the information collected were analyzed using SMART PLS. Validity has been investigated through AVE and divergent validity. The reliability of the study has been checked out through Cronbach alpha and composite reliability obtained at the standard level for the variables. In addition, the hypotheses were measured by the path coefficients and R², T-Value and GOF.

The study identified three variables and 19 sub-indicators from the literature associated that impact improved smart production systems. The results showed that the proposed model could describe 69.5% of the intelligence production systems' success variance. The results indicated that business social networks, cloud computing and the industrial Internet of things affect intelligent production systems. They can provide a novel procedure for intelligent comprehensions and connections, on-demand utilization and effective resource sharing.

Study limitations are as below. First, this study ignores the interrelationships among the success of cloud computing, business social networks, Internet of things and smart production systems. Future studies can consider it. Second, we only focused on three variables. Future investigations may focus on other variables subjected to the contexts. Ultimately, there are fewer experimental investigations on the impact of underlying business social networks, cloud computing and the Internet of things on intelligent production systems' success.

The research and analysis outcomes are considered from various perspectives on the capacity of the new elements of Industry 4.0 for the manufacturing sector. It proposes a model for the integration of these elements. Also, original and appropriate guidelines are given for intelligent production systems investigators and professionals' designers in industry domains.

The purpose of this study is to analyze and compare the tribological performances of journal bearings at different conditions through four numerical methods, which are based on the Boussinesq elastic half-space hypothesis or finite element theory.

An elasto-hydrodynamic lubrication (EHL) model of journal bearings is established, with the oil film pressure obtained by the finite difference method, and the deformation of bearing calculated by four different numerical methods, i.e. the direct finite element method (DFEM), influence coefficient method (ICM), fast-Fourier transform method (FFTM) and direct Boussinesq method (DBM). The tribological performances of the journal bearings obtained with the four methods along with the computation efficiency of the methods are discussed.

Under different operation conditions, the tribological performances with the finite element method-based methods (DFEM and ICM) agree with each other, and so do those with the Boussinesq-based methods (FFTM and DBM). Compared with the former two methods, the latter two overestimate the friction coefficient, film thickness and bearing deformation, but underestimate the film pressure, load-carrying capacity and friction force. The above discrepancies depend on the lubricant viscosity, the eccentricity ratio and rotational speed of the shaft and the length–diameter ratio of the bearing. Among the four methods, the FFTM has the best computation efficiency, followed by the DBM and the FEM-based methods.

This study conducts detailed discussions of the numerical methods used in the EHL calculation of journal bearings and gives a helpful reference to analyses and designs of journal bearings.