| With the development of heavy-duty and high-speed of heavy-duty railway,the rail damage will become more and more serious,especially the rail wear at the small radius curve of heavy-duty railway.Therefore,many measures to reduce rail wear have been put forward by relevant scholars,and it is concluded that both track parameter optimization and rail profile grinding can change the wheel-rail contact state to reduce rail wear,but there is a lack of research on when to intervene in which measures to reduce rail wear.Therefore,it is necessary to study the intervention time nodes of anti-wear measures based on track parameters and rail profile optimization,and it is necessary to study the impact of anti-wear measures on the whole life cycle of rail wear.The goal of this paper is to extend the life cycle of rail wear by adopting rail wear reduction measures based on track parameters and rail profile optimization.The main research contents are as follows:Firstly,the influence of single track parameters on rail wear is analyzed.It is found that the gauge is the most sensitive to rail wear,followed by the rail bottom slope,and the superelevation is relatively insensitive.Then,the influence of rail parameters combination on rail wear is studied,and the optimal combination of rail parameters is determined by the method of pairwise combination.Then,the optimization of rail profile is analyzed.The asymmetric rail profile is optimized by using the wheel rail contact position.The Latin hypercube sampling is used to obtain the rail profile samples.The BP neural network and genetic algorithm are used to obtain the optimal rail profile to reduce the wear effect.Secondly,the life cycle of the rail under the initial condition is determined to be1287 Mt.Echelon optimization method is used in the study of when and what measures to reduce wear.Echelon optimization is to study the wear reduction scheme in stages.Firstly,according to the complexity of wear reduction measures,three kinds of wear reduction schemes are determined,and then the optimal intervention time of wear reduction measures is studied.The small and medium change wear reduction schemes are randomly sampled,and the BP neural network and genetic algorithm optimization models are established to study the optimal intervention time;Latin hypercube sampling is used to select the intervention time for the large-scale wear reduction project,and BP neural network and genetic algorithm are used to optimize the model to obtain the optimal intervention time.Through the sampling of the intervention time,the timeliness of the intervention time of the wear reduction measures is analyzed.The intervention of the wear reduction measures is not in the whole life cycle of the rail,which can prolong the service life of the rail.The first scheme achieves the rail life cycle of 1 764 Mt through 5 mm gauge widening at 211 Mt,the second scheme achieves the rail life cycle of 1 457 Mt through 171 Mt.The rail life cycle with the total weight of 1 794 Mt can be achieved by using the rail parameters combination(5 mm gauge widening,90 mm super height,1 / 25 rail bottom slope)at 93 Mt.The rail life cycle with the total weight of 1 809 Mt can be achieved by using the rail parameters combination(5 mm gauge widening,90 mm super height,1 / 25 rail bottom slope)at 16 Mt and the optimal steel rail profile at 220 Mt,According to the wear reduction measures and the optimal intervention time,the time period that the wear reduction scheme can maintain and the wear development of rail life cycle are studied.Finally,the applicability of various wear reduction schemes is analyzed,and the allowable error of wear reduction measures in the wear reduction schemes is analyzed,and the allowable parameter error and time error of on-site implementation of rail wear reduction measures are obtained.The safety and economy of the wear reduction schemes are analyzed,and the wear reduction schemes are all in a safe range with remarkable economic benefits. |
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