Variable MCS method for LTE V2V Mode4

This paper applies the stochastic finite element method (SFEM) to perform the natural frequency analysis of functionally graded material (FGM). It is assumed that the elastic modulus and width of the FGM beam vary along the thickness and width directions following exponential functions. The stochastic eigenvalue problem is solved independently by first-order perturbation and Monte Carlo simulation (MCS) method through changing elastic modulus as spatial randomness. The results show that the first-order perturbation method based SFEM produces a very close value to MCS method.

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Structural reliability assessment using Kriging metamodel and Monte Carlo simulation: AK-MCS method

Reliability and Optimization of Structural Systems ◽

10.1201/b10497-9 ◽

2010 ◽

pp. 73-80 ◽

Cited By ~ 1

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Structural Reliability ◽

Reliability Assessment ◽

Kriging Metamodel ◽

Mcs Method

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Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

Energies ◽

10.3390/en13184965 ◽

2020 ◽

Vol 13 (18) ◽

pp. 4965

Author(s):

Kun Mo Lee ◽

Min Hyeok Lee ◽

Jong Seok Lee ◽

Joo Young Lee

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Emission Factor ◽

Bootstrap Method ◽

Ghg Emissions ◽

Parametric Bootstrap ◽

Random Variable ◽

Ghg Emission ◽

Nonparametric Bootstrap ◽

Mcs Method

Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions.

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Probabilistic sensitivity analysis of two suspension bridges in Istanbul, Turkey to near- and far-fault ground motion

Natural Hazards and Earth System Science ◽

10.5194/nhess-12-459-2012 ◽

2012 ◽

Vol 12 (2) ◽

pp. 459-473 ◽

Cited By ~ 5

Author(s):

Ö. Çavdar

Keyword(s):

Ground Motion ◽

Dynamic Responses ◽

Suspension Bridges ◽

Near Fault ◽

Internal Forces ◽

Ground Motion Records ◽

Far Fault ◽

Sensitivity Method ◽

Mcs Method ◽

Strong Ground

Abstract. The aim of this paper is to compare the near-fault and far-fault ground motion effects on the probabilistic sensitivity dynamic responses of two suspension bridges in Istanbul. Two different types of suspension bridges are selected to investigate the near-fault (NF) and far-fault (FF) ground motion effects on the bridge sensitivity responses. NF and FF strong ground motion records, which have approximately identical peak ground accelerations, of the Kocaeli (1999) earthquake are selected for the analyses. Displacements and internal forces are determined using the probabilistic sensitivity method (PSM), which is one type of stochastic finite element method. The efficiency and accuracy of the proposed algorithm are validated by comparison with results of the Monte Carlo Simulation (MCS) method. The displacements and internal forces obtained from the analyses of suspension bridges subjected to each fault effect are compared with each other. It is clearly seen that there is more seismic demand on displacements and internal forces when suspension bridges are subjected to NF and FF ground motion.

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Assessment of Redundant Steel Structural Systems Considering Effects of Member Strength Variability on the System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.267.33 ◽

2012 ◽

Vol 267 ◽

pp. 33-41

Author(s):

Amanullah Rasooli ◽

Hideki Idota

Keyword(s):

Random Variable ◽

Structural Systems ◽

Deformation Capacity ◽

Plastic Properties ◽

Perfectly Plastic ◽

Combination System ◽

Statistical Behavior ◽

Essential Parameter ◽

Strength Variability ◽

Mcs Method

In the present study, the failure of basic redundant steel structural systems is investigated. By considering that each member of the system has brittle, semi-brittle, or perfectly plastic properties, the statistical behavior of perfectly brittle systems, semi-brittle systems, perfectly plastic and combination systems are evaluated, and the effects of the coefficient of variation (CoV) of members on the systems are investigated. Uncorrelated strengths with the same mean are considered for the system elements. By using the Monte Carlo simulation (MCS) method, maximum strength, yield strength and residual strength of the redundant steel structural systems are evaluated. The CoV of member strength is an essential parameter for statistical assessment of steel structural systems. In this study, the strength is defined random variable a selected normal distribution represents the random variable, for the member strength. The deformation capacity of the member is strongly depends to the characteristics of member strength, but the post failure factor has deterministic values, only for the combination system. The post failure factor is a random variable that represents the uncertainty, uniform distribution is selected to represents random variable, in combination system post failure factor.

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Motion Reliability Analysis of a 3-RRR Parallel Manipulator With Random and Interval Variables

Volume 4B: Dynamics, Vibration, and Control ◽

10.1115/imece2016-66655 ◽

2016 ◽

Cited By ~ 1

Author(s):

Zhenhui Zhan ◽

Xianmin Zhang

Keyword(s):

Reliability Analysis ◽

Parallel Manipulator ◽

Random Variable ◽

Parallel Manipulators ◽

Analysis Method ◽

Motion Error ◽

Interval Variables ◽

First Order Second Moment ◽

Joint Clearances ◽

Mcs Method

A general methodology for motion error and motion reliability analysis of planar parallel manipulators (PPMs) with random and interval variables is presented. The inherent uncertainties of the manipulator, including tolerances in manufactures, errors in inputs as well as joint clearances are taken into account. The error model of a 3-RRR parallel manipulator is built and the global sensitivity coefficients of motion errors to variations are defined and obtained. The joint clearances are treated as interval variables while the others are treated as random variables. As a result, the motion error of the manipulator could turn out to be the mixture of a random variable and an interval variable. A new motion reliability analysis method based on the First Order Second Moment (FOSM) method and the Monte Carlo simulation (MCS) method is developed for the manipulator with random and interval variables. This paper provides a new idea to better understand the motion reliability affected by the inherent uncertainties of PPMs.

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The Comparison of Two Methods on the Reliability of Ship Collision for Bridge Based on Applied Mechanics

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.485.608 ◽

2012 ◽

Vol 485 ◽

pp. 608-615

Author(s):

Long Sheng Bao ◽

Jun Zhang ◽

Ling Yu ◽

Guang Shan Zhu

Keyword(s):

Finite Element ◽

Probability Of Failure ◽

Computational Time ◽

Traditional Theory ◽

Applied Mechanics ◽

Basic Variable ◽

Response Variable ◽

Structure Response ◽

Ship Collision ◽

Mcs Method

As for the reliability of ship-bridge collision, function relation between the structure response variable and basic variable is very complex and the traditional theory has been difficult to work it out, so we need the help of finite element numerical method to obtain the solution. APDL language and two methods are used to solve the reliability of ship-bridge collision, compared to seven MCS method conditions and ODRSM-MCS probability of failure of bridge pier. The pier failure probability of 10000 MCS method sampling and ODRSM-MCS sample is very close. ODRSM-MCS method and the MCS method to the case of the same accuracy, while MCS reliability is used in the calculation, the time used is 280min and the ODRSM-MCS mix of computational time is 5min. So the ODRSM-MCS method is much more efficient and effective.

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