Purpose The purpose of this paper is to study the wear evolution of metro wheels under the conditions of different track sequences, track composition and vehicle load and then to predict wheel wear and to guide its maintenance. Methodology By using the SIMPACK and MATLAB software, numerical simulation analysis of metro wheel wear is carried out based on Hertz theory, the FASTSIM algorithm and the Archard model. First of all, the vehicle dynamics model is established to calculate the motion relationship and external forces of wheel-rail in the SIMPACK software. Then, the normal force of wheel-rail is solved based on Hertz theory, and the tangential force of wheel-rail is calculated based on the FASTSIM algorithm through the MATLAB software. Next, in the MATLAB software, the wheel wear is calculated based on the Archard model, and a new wheel profile is obtained. Finally, the new wheel profile is re-input into the vehicle system dynamics model in the SIMPACK software to carry out cyclic calculation of wear. Findings The results show that the setting order of different curves has an obvious influence on wear when the proportion of the straight track and the curve is fixed. With the increase in running mileage, the severe wear zone is shifted from tread to flange root under the condition of the sequence-type track, but the wheel wear distribution is basically stable for the unit-type track, and their wear growth rates become closer. In the tracks with different straight-curved ratio, the more proportion the curved tracks occupy, the closer the severe wear zone is shifted to flange root. At the same time, an increase in weight of the vehicle load will aggravate the wheel wear, but it will not change the distribution of wheel wear. Compared with the measured data of one city B type metro in China, the numerical simulation results of wheel wear are nearly the same with the measured data. Practical implications These results will be helpful for metro tracks planning and can predict the trend of wheel wear, which has significant importance for the vehicle to do the repair operation. At the same time, the security risks of the vehicle are decreased economically and effectively. Originality/value At present, many scholars have studied the influence of metro tracks on wheel wear, but mainly focused on a straight line or a certain radius curve and neglected the influence of track sequence and track composition. This study is the first to examine the influence of track sequence on metro wheel wear by comparing the sequence-type track and unit-type track. The results show that the track sequence has a great influence on the wear distribution. At the same time, the influence of track composition on wheel wear is studied by comparing different straight-curve ratio tracks; therefore, wheel wear can be predicted integrally under different track conditions.

Existing research on wheel wear prediction uses either data-driven or model-based methods. However, due to the high reliability and limited sample characteristics of metro wheel wear, data-driven methods are not accurate enough and require relatively high data costs, and model-based methods mainly lack verification with measured data and generalization ability. To address the shortcomings of the two types of methods, a new approach combining model-based and data-driven methods is used to predict wheel wear in this paper. First, the least-squares algorithm is used to analyze and calculate the difference between the wear measurement for a specific running mileage and the corresponding simulated wear, with the minimum difference taken as an objective function. By means of optimization algorithms including Genetic Algorithm, Particle Swarm Optimization, Tabu Search and Simulated Annealing, the wear coefficient k in Jendel wear model is optimized, thereby obtaining an optimized Jendel wear model. Later, metro wheel wear for additional running mileage is simulated and predicted through combined application of the vehicle system dynamics, wheel-rail contact, and optimized Jendel wear models. Finally, the paper analyzes the wear prediction results obtained by the integrated data-model-driven approach and compares them with the results of traditional methods and measured data. The results suggest that the integrated data-model-driven approach effectively reduces the uncertainty in selecting the wear coefficient by experience, lowers the experimental data costs, and improves the wear prediction accuracy. Therefore, it is a promising approach to wheel wear prediction. INDEX TERMS Metro wheel, wear prediction, data-model driven, optimization algorithms, Jendel wear model.

Out-of-roundness and tread wear are common types of damage to subway wheels, which greatly affect the dynamic performance of subway vehicles. In light of the coupling and asymmetry of out-of-roundness and tread wear found in real-world subway wheels, an analysis method that considers asymmetric out-of-roundness coupled with tread wear was proposed for vehicle dynamics. A vehicle dynamics model featuring asymmetric out-of-roundness coupled with tread wear was used to investigate the influences of asymmetric wheel damage and coupled damage on the dynamic performance of the vehicle system. The vehicle’s dynamic performance was simulated under different conditions, including asymmetric out-of-roundness and symmetric out-of-roundness, uncoupled damage and coupled damage, asymmetric coupled damage and symmetric coupled damage. Then the estimated data was compared against the measured data. The study finds that the vertical wheel/rail contact force, lateral wheel/rail contact force, derailment coefficient and wheel unloading rate increased in the case of asymmetric out-of-roundness. In the presence of coupled damage, the degree of tread wear had a relatively great influence on the lateral wheel/rail contact force, axle lateral force, and derailment coefficient, but had little influence on the vertical contact force. Compared to symmetric coupled damage, asymmetric coupled damage had a greater influence on peak vertical vibration acceleration and stability index, and their values are closer to the measured values in the case of asymmetric coupled damage. This suggests that the dynamics model that considers asymmetric out-of-roundness coupled with tread wear can provide more accurate results as guidance on the maintenance and overhaul of subway wheels.

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This paper systematically analyzes the models and processes related to wordof-mouth spreading in social networks. This paper simulates the characteristics and rules of word-of-mouth spreading on social network platforms, adopts network evolution models as well as virus spreading models which can precisely reflect the process of word-of-mouth spreading. By computer simulation, the effect of several kinds of parameters in networks and in wordof-mouth spreading model is analyzed. What has been proved, through parameter analysis, is that the secondary "push" of the key node (opinion leader) in social networks has played a significant role in promoting word-of-mouth spreading. In practical applications, shopkeepers can act appropriately to the situation, which means they put in a second period of advertise appropriately after placing one advertisement at random in order to save costs and increase efficiency. 158Social Networking means, those key nodes can influence the trend of public opinion in information diffusion.With the vigorous development of online marketing, the search algorithm or evaluation method for opinion leaders in the potential customer relationship network has become a research focus in multiple crossover fields including complex network analysis, data mining, and word-of-mouth marketing. For example, Liu Zhiming [1] combined micro-blog opinion leaders with user's influence and user's activity to identify opinion leaders, and analyzed their characteristics; He Li [2] used data mining techniques to obtain information related to microblog user characteristics. After analysis, it revealed the feasibility of personalized marketing among Weibo users; Zhang J et al. [3] analyzed the effect of network structure, different characteristics in the word-of-mouth spreading process, and opinion leaders' status; Li F [4] proposed an evaluation index system based on the analysis of social networks; Java et al. [5] also analyzed the topological structure of different types of social networks, such as web-based and micro-blog network, adopting data mining methods, and finally obtained their spreading characteristics.