A Monte Carlo simulation based inverse propagation method for stochastic model updating

Bao, Nuo; Wang, Chunjie

doi:10.1016/j.ymssp.2015.01.011

Cited by 29 publications

(14 citation statements)

References 21 publications

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“…It is shown that this model has a potential to simulate friction force in a bolted joint very well. Iranzad [10] also utilized a constitutive model with a thin layer of elasto-plastic material to model nonlinear behaviour of bolted joints. The thin layer elastic material properties represent the joint linear behaviour at low vibration levels.…”

Section: Fe Modelling Of Joints Structurementioning

confidence: 99%

“…Once the uncertainties are being considered, a deterministic problem will then change to non-deterministic problem (stochastic). It is very useful and highly recommended to explore numerical predictions on behavior of structure with uncertainties and many previous researches studied on stochastic model updating approach using two methods [5][6][7][8][9][10][11][12][13][14][15]; Monte Carlo simulation and perturbation method. For this paper, it focuses on spot welded structure and as simplified in the first section, the joints structure could be considered less accurately modelled due to the uncertainties in the structural parameters such as modulus elasticity and diameter of the weld, mass density, boundary condition etc.…”

Section: Identification Of Updating Parametersmentioning

confidence: 99%

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A review on model updating of joint structure for dynamic analysis purpose

et al. 2016

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Abstract. Structural joints provide connection between structural element (beam, plate etc.) in order to construct a whole assembled structure. There are many types of structural joints such as bolted joint, riveted joints and welded joints. The joints structures significantly contribute to structural stiffness and dynamic behaviour of structures hence the main objectives of this paper are to review on method of model updating on joints structure and to discuss the guidelines to perform model updating for dynamic analysis purpose. This review paper firstly will outline some of the existing finite element modelling works of joints structure. Experimental modal analysis is the next step to obtain modal parameters (natural frequency & mode shape) to validate and improve the discrepancy between results obtained from experimental and the simulation counterparts. Hence model updating will be carried out to minimize the differences between the two results. There are two methods of model updating; direct method and iterative method. Sensitivity analysis employed using SOL200 in NASTRAN by selecting the suitable updating parameters to avoid ill-conditioning problem. It is best to consider both geometrical and material properties in the updating procedure rather than choosing only a number of geometrical properties alone. Iterative method was chosen as the best model updating procedure because the physical meaning of updated parameters are guaranteed although this method required computational effort compare to direct method.

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Section: Fe Modelling Of Joints Structurementioning

confidence: 99%

Section: Identification Of Updating Parametersmentioning

confidence: 99%

A review on model updating of joint structure for dynamic analysis purpose

et al. 2016

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“…Takano [15] studied the stochastic model of structure strength with geometrical imperfection and uncertainty in material property. Fang [16] and Bao [17], respectively, proposed a stochastic model updating, and solved it based on the Monte Carlo method. Deng [18] established a time-dependent degradation model, but did not consider the randomness of variables at each moment.…”

Section: Introductionmentioning

confidence: 99%

A Time-Variant Reliability Model for Copper Bending Pipe under Seawater-Active Corrosion Based on the Stochastic Degradation Process

Sun

Liao

et al. 2018

Materials

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In the degradation process, the randomness and multiplicity of variables are difficult to describe by mathematical models. However, they are common in engineering and cannot be neglected, so it is necessary to study this issue in depth. In this paper, the copper bending pipe in seawater piping systems is taken as the analysis object, and the time-variant reliability is calculated by solving the interference of limit strength and maximum stress. We did degradation experiments and tensile experiments on copper material, and obtained the limit strength at each time. In addition, degradation experiments on copper bending pipe were done and the thickness at each time has been obtained, then the response of maximum stress was calculated by simulation. Further, with the help of one kind of Monte Carlo method we propose, the time-variant reliability of copper bending pipe was calculated based on the stochastic degradation process and interference theory. Compared with traditional methods and verified by maintenance records, the results show that the time-variant reliability model based on the stochastic degradation process proposed in this paper has better applicability in the reliability analysis, and it can be more convenient and accurate to predict the replacement cycle of copper bending pipe under seawater-active corrosion.

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“…The goal is to allow an estimate of dynamic responses generated by these considerations on physical parameters. There are a variety of methods in a view of uncertainties for this type of issues: such as the perturbation method [16,17], Neumann method [18,19], MCS method [20][21][22], polynomial chaos expansion (PCE) method [23][24][25], and PDD method [26][27][28]. The perturbation method is based on the expansion of random quantities into Taylor series [29], and the Neumann method is on the basis of Neumann series [30,31], they can both solve the small random fluctuations problems but do not fit for the case close to the resonant frequency.…”

Section: Introductionmentioning

confidence: 99%

Application of the polynomial dimensional decomposition method in a class of random dynamical systems

Lu¹,

Hou²,

Chen³

2017

J. vibroeng.

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The polynomial dimensional decomposition (PDD) method is applied to study the amplitude-frequency response behaviors of dynamical system model in this paper. The first two order moments of the steady-state response of a dynamical random system are determined via PDD and Monte Carlo simulation (MCS) method that provides the reference solution. The amplitude-frequency behaviors of the approximately exact solution obtained by MCS method can be retained by PDD method except the interval close to the resonant frequency, where the perturbations may occur. First, the results are shown on the two degrees of freedom (DOFs) spring system with uncertainties; the dynamic behaviors of the uncertainties for mass, damping, stiffness and hybrid cases are respectively studied. The effects of PDD order to amplitude-frequency behaviors are also discussed. Second, a simple rotor system model with four random variables is studied to further verify the accuracy of the PDD method. The results obtained in this paper show that the PDD method is accurate and efficient in the dynamical model, providing the theoretical guidance to complexly nonlinear rotor dynamics models.

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A Monte Carlo simulation based inverse propagation method for stochastic model updating

Cited by 29 publications

References 21 publications

A review on model updating of joint structure for dynamic analysis purpose

A review on model updating of joint structure for dynamic analysis purpose

A Time-Variant Reliability Model for Copper Bending Pipe under Seawater-Active Corrosion Based on the Stochastic Degradation Process

Application of the polynomial dimensional decomposition method in a class of random dynamical systems

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