Research on optimization Design of Suspension Parameters of railway vehicle bogies based on surrogate model

The suspension parameters of railway vehicle bogies have a significant impact on the dynamic performance. Its parameters are numerous, the model is complex, and there are different dynamic performance requirements under different working conditions. Establishing a fast optimization method suitable f...

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Veröffentlicht in:Multimedia tools and applications Jg. 84; H. 29; S. 35091 - 35109
Hauptverfasser: Zou, Hua, Wu, Qifeng, Zou, Xiaolong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2025
Springer Nature B.V
Schlagworte:
1231: IoT-driven Computer Vision Technology for Smart Transportation Applications
Analytic hierarchy process
Computer Communication Networks
Computer Science
Computer simulation
Computers
Data Structures and Information Theory
Design optimization
Dynamic models
Genetic algorithms
Mathematical analysis
Mathematical models
Motion stability
Multibody systems
Multimedia Information Systems
Neural networks
Optimization
Ordinary differential equations
Parameters
Performance indices
Railcars
Railroads
Response surface methodology
Sensitivity analysis
Shock absorbers
Software
Special Purpose and Application-Based Systems
Undercarriages
Vehicles
Working conditions
Surrogate model
Vehicle dynamics
Multi-objective optimization
ISSN:1573-7721, 1380-7501, 1573-7721
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Zusammenfassung:The suspension parameters of railway vehicle bogies have a significant impact on the dynamic performance. Its parameters are numerous, the model is complex, and there are different dynamic performance requirements under different working conditions. Establishing a fast optimization method suitable for suspension parameters under different operating conditions is the research goal of this article. For the optimal design of suspension parameters, a surrogate model is used to replace high-precision dynamic models to establish suspension parameter optimization functions based on weights for fast and accurate solution. First, based on the multi-body dynamics model of the railway vehicle, basic suspension parameters and performance index samples that can be simulated. The dynamics model is approximated using response surface model, Kriging model, and radial basis neural network model. According to the fitting accuracy of each model, the most effective fitting surrogate model is obtained; Then, the weight parameters of vehicle motion stability, running safety and ride stability under different working conditions are obtained by Analytic Hierarchy Process (AHP), and the optimization objective function of bogie suspension parameters is established. Finally, the NSCA-II algorithm is used to solve the optimization objective function, and the optimal suspension parameter combination under different working conditions is obtained. Taking a intercity EMU as an example, the calculation analysis based on the surrogate model shows that: using this method, the dynamic performance index of the original municipal vehicles under various working conditions have been optimized targetedly, which has remarkable effect; compared with the optimization based on the precise dynamic model, the optimization calculation time of this method is 1/30, and the efficiency of the optimization design is greatly improved.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-022-14022-4