Sensitivity analysis of suspension characteristics for Korean high speed train

Abstract

The dynamic performance of railway vehicle is normally expressed as stability, safety and ride comfort, and is affected by mass properties, suspension characteristics, contact mechanism between a wheel and a rail, etc. This paper describes the procedure of sensitivity analysis between some of the suspension characteristics of the Korean high speed train (KHST) as the design variables and the dynamic performance as the response variables; and it analyzes the results of sensitivity characteristics for the design variables, comparing two different approximated approach processes known as the response surface model formulated in a polynomial equation and neural network model formulated in a processing code. Analyzing the suspension characteristics for KHST, the approximated method creating meta-models consisted of 29 design variables and 46 performance indexes, which are applied in this paper. The models were coded by using the correlation information between the design variables and the performance indexes made by the 66 times iterative simulations according to the design of experimental method. The table consists of the orthogonal array L32 and the D-Optimal design table. The results show that the proposed sensitivity analysis procedure is very efficient and simply applicable for a complex mechanical system such as railway vehicle system. Also they show that the two models applied in this paper have similar tendency in the view of the sensitivity order of the design variables.

A Systematic Optimization Design Method for Complex Mechatronic Products Design and Development

“…Cai et al [23] presented a general multiagent control methodology for an energy system optimization in a "plug-and-play" manner. Park et al [24] described the properties of sensitivity analysis between some of the suspension characteristics of the Korean high-speed train as the design variables and the dynamic performance as the response variables. Li et al [25] presented a new meta-model-based global optimization method using fuzzy clustering for design space reduction.…”

Optimization of Suspension Under the Condition of Curved Track in Railway Vehicle

“…Designing method for suspension characteristics using neural network model and response surface model is suggested by Park [4]. Optimization of diverse design variables with regard to suspension system employing response surface method and neural network is developed by Park [5]. Similarly, we suggest design strategy for optimizing requirement of reducing lateral acceleration based on car body, bogie and wheel set under the condition of curved track.…”

Sensitivity Analysis of Ride Behaviour of Indian Railway Rajdhani Coach using Lagrangian Dynamics

“…Suarez et al [1] carried out a sensitivity analysis of inertia properties of railway vehicle and found that mass and pitch moment inertia of the vehicle bodies are the most sensitive parameters. Park et al [5] applied surface response method and neural network method to investigate the sensitivity of suspension characteristics on the dynamic behaviour of Korean high speed train. Zakeri and Xia [7] performed a sensitivity analysis of sub-structure parameters and found that a reduction in sleeper spacing, rail pad stiffness, ballast stiffness and increase in ballast damping reduced the contact forces between wheel-rail, rail-sleeper and in sleeper-ballast.…”