English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The dynamic characteristics of the hydraulic damper are time-varying in the complex working environment. To reveal the internal influence mechanism of the boundary conditions on the dynamic performance of the hydraulic damper and take it into account in the multi-body dynamics calculation, the laboratory test of the hydraulic damper is carried out firstly, and it is confirmed that the hydraulic damper has significant frequency-dependent and amplitude-dependent and temperature-dependent characteristics. Then, combining the physical parameter model with the neural network model, an accurate hybrid neural network model of the hydraulic damper is proposed. The physical parameter model considers the damper structure, including orifice, damping valve, rubber joint and the relationship between temperature and viscosity of hydraulic oil. The neural network model describes the personality characteristics of the hydraulic damper, such as oil leakage, the internal friction force and the percentage of entrapped air in oil. Finally, the responses and the dynamic parameters of the hybrid neural network model are calculated and compared with the experimental results by considering various exciting amplitudes and frequencies. The results show that the proposed model can fully simulate the dynamic performance of the hydraulic damper under various operating conditions.

A physical model-neural network coupled modelling methodology of the hydraulic damper for railway vehicles