Efficient System Reliability Analysis by Finite Element Structural Models

2011 ◽  
Author(s):  
Bruno Gaspar ◽  
Arvid Naess ◽  
Bernt J. Leira ◽  
C. Guedes Soares
Keyword(s):  
Finite Element ◽  
Monte Carlo ◽  
System Reliability ◽  
Computational Cost ◽  
Nonlinear Finite Element ◽  
Reliability Estimation ◽  
Surface Model ◽  
Structural Systems ◽  
System Failure ◽  
Efficient System

In principle, the reliability of complex structural systems can be accurately predicted through Monte Carlo simulation. This method has several attractive features for structural system reliability, the most important being that the system failure criterion is usually relatively easy to check almost irrespective of the complexity of the system. However, the computational cost involved in the simulation may be prohibitive for highly reliable structural systems. In this study a new Monte Carlo based method recently proposed for system reliability estimation that aims at reducing the computational cost is applied. It has been shown that the method provides good estimates for the system failure probability with reduced computational cost. By a numerical example the usefulness and efficiency of the method to estimate the reliability of a system represented by a nonlinear finite element structural model is demonstrated. To reduce the computational cost involved in the nonlinear finite element analysis the method is combined with a response surface model.

System Reliability Analysis by Monte Carlo Based Method and Finite Element Structural Models

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Bruno Gaspar ◽  
Arvid Naess ◽  
Bernt J. Leira ◽  
C. Guedes Soares
Keyword(s):  
Finite Element ◽  
Monte Carlo ◽  
System Reliability ◽  
Structural Model ◽  
Computational Cost ◽  
Nonlinear Finite Element ◽  
Reliability Estimation ◽  
Surface Model ◽  
Structural Systems ◽  
System Failure

In principle, the reliability of complex structural systems can be accurately predicted by Monte Carlo simulation. This method has several attractive features for structural system reliability, the most important being that the system failure criterion is usually relatively easy to check almost irrespective of the complexity of the system. However, the computational cost involved in the simulation may be prohibitive for highly reliable structural systems. In this paper a new Monte Carlo based method recently proposed for system reliability estimation that aims at reducing the computational cost is applied. It has been shown that the method provides good estimates for the system failure probability with reduced computational cost. In a numerical example the usefulness and efficiency of the method to estimate the reliability of a system represented by a nonlinear finite element structural model is presented. To reduce the computational cost involved in the nonlinear finite element analysis the method is combined with a response surface model.

Estimation of System Reliability by Monte Carlo Simulation

2009 ◽  
Author(s):  
A. Naess ◽  
B. J. Leira ◽  
O. Batsevych
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Failure Probability ◽  
Failure Criterion ◽  
System Reliability ◽  
Substantial Reduction ◽  
Structural Systems ◽  
System Failure ◽  
Simulation Monte Carlo

A new method for estimating the reliability of structural systems is proposed. The method is based on the use of Monte Carlo simulation. Monte Carlo based methods for system reliability analysis has several attractive features, the most important being that the system failure criterion is usually relatively easy to check almost irrespective of the complexity of the system. The disadvantage of such methods is the amount of computational efforts that may be involved. However, by reformulating the reliability problem to depend on a parameter and exploiting the regularity of the failure probability as a function of this parameter, it is shown that a substantial reduction of the computational efforts involved can be obtained.

System Reliability Estimation for a Manufacturing Network with Joint Buffers

2018 ◽  
Vol 25 (04) ◽  
pp. 1850020 ◽  
Author(s):  
Ping-Chen Chang ◽  
Chia-Chun Wu ◽  
Chin-Tan Lee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
System Reliability ◽  
Manufacturing System ◽  
Reliability Estimation ◽  
Experimental Result ◽  
Production Lines ◽  
Work In Process ◽  
Process Output ◽  
Manufacturing Network

This paper develops a Monte Carlo Simulation (MCS) approach to estimate the performance of a multistate manufacturing network (MMN) with joint buffers. In the MMN, products are allowed to be produced by two production lines with the same function to satisfy demand. A performance index, system reliability, is applied to estimate the probability that all workstations provide sufficient capacity to satisfy a specified demand and buffers possess adequate storage. The joint buffers with finite storage are considered in the MMN. That is, extra work-in-process output from different production lines can be stored in the same buffer. An MCS algorithm is proposed to generate the capacity state and to check the storage usage of buffers to evaluate whether the demand can be satisfied or not. System reliability of the MMN is estimated through this MCS algorithm. Besides, performability for demand pairs assigned to production lines can be obtained. A practical example of touch panel manufacturing system is used to demonstrate the applicability of the MCS approach. Experimental result shows that system reliability is overestimated when buffer storage is assumed to be infinite. Moreover, joint buffer for an MMN is more reliable than buffers are installed separately in different production lines.

scholarly journals Model Updating Method Based on Kriging Model for Structural Dynamics

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Hong Yin ◽  
Jingjing Ma ◽  
Kangli Dong ◽  
Zhenrui Peng ◽  
Pan Cui ◽  
...  
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Frequency Response ◽  
Structural Dynamics ◽  
Model Updating ◽  
Computational Cost ◽  
Element Model ◽  
Kriging Model ◽  
Surface Model ◽  
Sample Points

Model updating in structural dynamics has attracted much attention in recent decades. And high computational cost is frequently encountered during model updating. Surrogate model has attracted considerable attention for saving computational cost in finite element model updating (FEMU). In this study, a model updating method using frequency response function (FRF) based on Kriging model is proposed. The optimal excitation point is selected by using modal participation criterion. Initial sample points are chosen via design of experiment (DOE), and Kriging model is built using the corresponding acceleration frequency response functions. Then, Kriging model is improved via new sample points using mean square error (MSE) criterion and is used to replace the finite element model to participate in optimization. Cuckoo algorithm is used to obtain the updating parameters, where the objective function with the minimum frequency response deviation is constructed. And the proposed method is applied to a plane truss model FEMU, and the results are compared with those by the second-order response surface model (RSM) and the radial basis function model (RBF). The analysis results showed that the proposed method has good accuracy and high computational efficiency; errors of updating parameters are less than 0.2%; damage identification is with high precision. After updating, the curves of real and imaginary parts of acceleration FRF are in good agreement with the real ones.

A Numerical Model to Study the Effect of Temperature on Electrical Conductivity of Polymer-CNT Nanocomposites

2013 ◽  
Author(s):  
Amirhossein B. Oskouyi ◽  
Uttandraman Sundararaj ◽  
Pierre Mertiny
Keyword(s):  
Electrical Conductivity ◽  
Finite Element ◽  
Monte Carlo ◽  
Electrical Properties ◽  
Monte Carlo Model ◽  
Computational Cost ◽  
Three Dimensional ◽  
Effect Of Temperature ◽  
Tunneling Resistance ◽  
Percolation Network

In this study the effect of the temperature on the electrical conductivity of nanocomposites with carbon nanotube (CNT) fillers was investigated. A three-dimensional continuum Monte Carlo model was developed and employed first to form a CNT percolation network. CNT fillers were randomly generated and dispersed in a cubic representative volume element. Periodic boundary conditions were applied in this model to minimize size effects while decreasing computational cost. CNT fibers that connected electrically to each other through electron hopping were recognized and grouped as clusters. In addition to tunneling resistance, the effect of intrinsic CNT resistivity was considered. A three-dimensional resistor network was subsequently developed to evaluate nanocomposite electrical properties. Modeling employing the finite element method was conducted to evaluate the electrical conductivity of the percolation network. Considering the determining role of tunneling resistance on electrical conductivity of CNT based nanocomposites, as well as results obtained from experimental studies, temperature was expected to play an important role in nanocomposite electrical properties. The effect of temperature on electrical conductivity of CNT nanocomposites was thus investigated through employing the developed Monte Carlo and finite element models. Other aspects, including the electrical behavior of the polymer, tunneling resistivity and the intrinsic resistivity of CNT were considered in this study as well. The comprehensiveness of the developed modeling approach enables an evaluation of results in conjunction with experimental data in future works.

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