Multiobjective Optimization of the Worn Turnout Rail Profile Driven by a Hybrid Intelligent Algorithm

Abstract

When high-speed trains pass through worn turnouts, the severe wear of the rails often leads to the carbody lateral acceleration (CLA) exceeding the standard. However, traditional rail grinding methods that focus on restoring the new rail profile are problematic for effectively addressing this issue. This study collects data on worn wheels and rails and uses nonuniform rational B-spline curves to accurately fit the worn rail profiles. By integrating the vehicle-turnout coupled dynamics model with the back propagation and nondominated sorting genetic algorithm III hybrid intelligent algorithm, multiobjective optimization of the rail profiles is carried out. While maintaining a good wheel–rail contact interaction, the grinding amount is reduced. The research reveals that the differential wear of the rails at different positions causes surface irregularities, leading to significant fluctuations in the nominal equivalent conicity, which is a key factor contributing to the excessive CLA. Simulation analysis shows that compared with the new profile and the representative worn profile, when a vehicle equipped with worn wheels runs on the optimized profile, the carbody acceleration, frame acceleration, wheel–rail forces, derailment coefficients, wear indices, and fatigue indices are all reduced. In the field application, after 10 months of monitoring the optimized profile, the CLA does not exceed the standard. The deviation between the measured profile and the optimized profile meets the acceptance criteria, and the grinding efficiency is improved. In conclusion, the optimized profile effectively solves the problem of excessive CLA, enhancing the stability and safety of high-speed train operations.

Keywords

high-speed train turnout rail wear rail profile optimization carbody lateral acceleration BP-NSGA III algorithm

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