Key parameter identification method for high-speed train dynamics performance design

Abstract

The invention provides a key parameter identification method for high-speed train dynamics performance design, which relates to the field of dynamics simulation design and analysis for the high-speed trains. A single-output LM neural network agent model is effectively used, and overall design of the high-speed train is merged in multi-subject global simulation design. The method comprises the following steps: a high-speed train multi-rigid-body dynamics physical model and a simulation model are firstly used, high-speed train dynamics performance input and output design space is determined, expert domain knowledge is used, and high-speed train dynamics performance design parameters and response indexes are extracted to shorten the design space; then, LM algorithm is adopted to adjust weights and thresholds of the neural network so as to improve the convergence speed and the convergence accuracy of the single-output neural network, and according to a sensitivity formula of input parameters in relative to an output value in the neural network, sensitivity is calculated; and finally, sensitivity analysis and key parameter identification are carried out on high-speed train design parameters. The method of the invention is mainly used for high-speed train dynamics analysis and design.

Description

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AI Technical Summary

Benefits of technology

Introduction into High Speed Train Protocols (HSTP) researches how well different factors like temperature or pressure affect the performance of vehicles during training are identified by simulations conducted at speeds up to 2000 mph. These models help predict vehicle behavior better than previous designs without processing too much data. Overall, these techniques improve efficiency and accuracy when analyzed over large amounts of time.

Problems solved by technology

This patents describes different technical means related to improving the behavior of high speeds railway vehicles (HST) during their operation under harsh environmental factors like temperature changes or impact caused by traffic jams. These issues include ensuring stable ride quality while minimizing damage over time without compromising passenger experience. Additionally, these challenges involve identifying important relationships among multiple components within the vehicle, particularly relating to how they interact with external environments. Overall, there needs an effective way to identify critical elements associated with HST performance improvement measures.

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