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Sensitivity Analysis for Influence Parameters of Rail Corrugation Characteristics in Metro Straight Section

Wang Z., Lei Z.

Advances in Science and Technology. Research Journal

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2021

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Vol. 15, no 4

110--117

EN

Based on the theory of friction self-excited vibration and the measured data of rail corrugation, the cause of rail corrugation in metro straight section was analyzed. Then, using the stochastic finite element method, the sensitivity of each parameter to rail corrugation was studied by selecting the elastic modulus E1 and density ρ1 of the wheel-rail material, the elastic modulus E2 and density ρ2 of the track slab material, the wheel-rail coefficient of friction f, the fastener vertical stiffness K and vertical damping C, the wheel-rail longitudinal relative slip s as the random parameters. The results show that under the support of Cologne egg fastener track, the characteristic frequency of friction self-excited vibration of wheel-rail system is close to the characteristic frequency of measured corrugation, indicating that the occurrence of rail corrugation is related to the friction self-excited vibration of wheel-rail system under the condition of saturated creep force. The parameter sensitivity analysis illustrates that the influence degree of each random parameter on the real part αi of complex eigenvalue is E1>ρ1>C>E2>ρ2>f>K>s in turn. E1, C and s are positively correlated with the real part αi of complex eigenvalue, while the remaining 5 parameters are negatively correlated with the real part αi of complex eigenvalue. Therefore, appropriate decrease of E1, C and s, and increase of ρ1, E2, ρ2, f and K can play a positive role in the control of rail corrugation.

2

Stochastic finite element analysis using polynomial chaos

Drakos S., Pande G. N.

Studia Geotechnica et Mechanica

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2016

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Vol. 38, nr 1

33-43

EN

This paper presents a procedure of conducting Stochastic Finite Element Analysis using Polynomial Chaos. It eliminates the need for a large number of Monte Carlo simulations thus reducing computational time and making stochastic analysis of practical problems feasible. This is achieved by polynomial chaos expansion of the displacement field. An example of a plane-strain strip load on a semi-infinite elastic foundation is presented and results of settlement are compared to those obtained from Random Finite Element Analysis. A close matching of the two is observed.

3

Stochastic finite element method for sensitivity analysis of structures

Bhattacharyya B., Chakraborty S.

Applied Mechanics and Engineering

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2000

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Vol. 5, no 4

867-882

EN

The conventional sensitivity analysis of structures is based on the assumption of complete certainty of design parameters. However, occurrence of uncertainty is unavoidable in structures. In the present work, an attempt has been made to study the response sensitivity, considering the effect of uncertainty in structural design parameters. The random parameters are modeled as Gaussian stochastic process and simulated through covariance matrix decomposition. The advantages of Neumann expansion technique has been utilized in deriving the finite element solution of the response sensitivity within the framework of Monte Carlo simulation. Numerical examples are presented to explain the accuracy and efficacy of Neumann expansion method over direct simulation the process.

4

An efficient stochastic finite element method for random field problems

Chakraborty S., Sekhar Dey S.

Applied Mechanics and Engineering

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1999

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Vol. 4, no 1

45-71

EN

The present paper primarily deals with the analysis of structures involving more than one parameter being stochastic in nature. The proposed method involves two stages. The first part hinges upon adequate representation of the stochastic process and a digital simulation of random parameters. This has been achieved by the local averaging technique followed by the Cholesky decomposition algorithm. Finally, the finite element solution has been obtained utilizing the Neumann expansion method within the framework of Monte Carlo simulation.The approach involves only single decomposition of the stiffnes matrix for the entire simulation and leads to a considerable saving of computing time. Another important flexibility of the approach is that more than one parameter can be handled simultaneously. Numerical examples are presented to elucidate the accuracy and efficiency of the method with respect to the direct simulation approach.