scholarly journals Dynamic Reliability Evaluation of Road Vehicle Subjected to Turbulent Crosswinds Based on Monte Carlo Simulation

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Bin Wang ◽  
Yongle Li ◽  
Helu Yu ◽  
Haili Liao
Keyword(s):  
Surface Roughness ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Process ◽  
Numerical Models ◽  
Random Processes ◽  
Distribution Model ◽  
Vibration System ◽  
Road Vehicle ◽  
Dynamic Reliability

As a vehicle moves on roads, a complex vibration system of the running vehicle is formed under the collective excitations of random crosswinds and road surface roughness, together with the artificial handing by the drivers. Several numerical models in deterministic way to assess the safety of running road vehicles under crosswinds were proposed. Actually, the natural wind is a random process in time domain due to turbulence, and the surface roughness of a road is also a random process but in spatial domain. The nature of a running vehicle therefore is an extension of dynamic reliability excited by random processes. This study tries to explore the dynamic reliability of a road vehicle subjected to turbulent crosswinds. Based on a nonlinear vibration system, the dynamic responses of a road vehicle are simulated to obtain the dynamic reliability. Monte Carlo Simulation with Latin Hypercube Sampling is then applied on the possible random variables including the vehicle weight, road friction coefficient, and driver parameter to look at their effects. Finally, a distribution model of the dynamic reliability and a corresponding index for the wind-induced vehicle accident considering these random processes and variables is proposed and employed to evaluate the safety of the running vehicle.

scholarly journals Empirical Bayesian regression model for estimation of small rates

2012 ◽  
Vol 53 ◽  
Author(s):  
Gintautas Jakimauskas ◽  
Leonidas Sakalauskas
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Population Data ◽  
Real Data ◽  
Simulation Method ◽  
Bayesian Regression ◽  
Distribution Model ◽  
Estimation Model ◽  
Hospital Discharges ◽  
Large Populations

The efficiency of adding an auxiliary regression variable to the logit model in estimation of small probabilities in large populations is considered. Let us consider two models of distribution of unknown probabilities: the probabilities have gamma distribution (model (A)), or logits of the probabilities have Gaussian distribution (model (B)). In modification of model (B) we will use additional regression variable for Gaussian mean (model (BR)). We have selected real data from Database of Indicators of Statistics Lithuania – Working-age persons recognized as disabled for the first time by administrative territory, year 2010 (number of populations K = 60). Additionally, we have used average annual population data by administrative territory. The auxiliary regression variable was based on data – Number of hospital discharges by administrative territory, year 2010. We obtained initial parameters using simple iterative procedures for models (A), (B) and (BR). At the second stage we performed various tests using Monte-Carlo simulation (using models (A), (B) and (BR)). The main goal was to select an appropriate model and to propose some recommendations for using gamma and logit (with or without auxiliary regression variable) models for Bayesian estimation. The results show that a Monte Carlo simulation method enables us to determine which estimation model is preferable.

Estimating Weights of Evaluation Factors on Basis of Monte Carlo Simulation

2012 ◽  
Vol 268-270 ◽  
pp. 1735-1740
Author(s):  
Yan Fei Tian ◽  
Li Wen Huang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Process ◽  
Stochastic Simulation ◽  
Research Work ◽  
Simulation Method ◽  
Distribution Functions ◽  
Probability Process ◽  
Evaluation Work

Although the value of factor weight in an evaluation work is deterministic, the solving process is random, so connection between weight solution with digital characteristics or distribution functions of specific random variables or random process could be build. Using stochastic simulation method to get a lot of random solutions to the problem, expectation of the random solutions can be used as a estimation solution. On basis of idea of Monte Carlo simulation, this paper analyzed the probability process of calculating factor weight, and provided the procedures of estimating factor weight by means of Monte Carlo simulation. Through discussion and example in this paper, feasibility and validity of this method were proved, which may make foreshadowing for follow-up research work.

An Importance Sampling Approach for Time-Dependent Reliability

2011 ◽  
Author(s):  
Amandeep Singh ◽  
Zissimos P. Mourelatos ◽  
Efstratios Nikolaidis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Process ◽  
Importance Sampling ◽  
Sampling Technique ◽  
Process Time ◽  
Cumulative Probability ◽  
Repairable Systems ◽  
Importance Sampling Method ◽  
True Value

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. The reliability usually degrades with time increasing the lifecycle cost due to potential warranty costs, repairs and loss of market share. Reliability is the probability that the system will perform its intended function successfully for a specified time. In this article, we consider the first-passage reliability which accounts for the first time failure of non-repairable systems. Methods are available which provide an upper bound to the true reliability which may overestimate the true value considerably. The traditional Monte-Carlo simulation is accurate but computationally expensive. A computationally efficient importance sampling technique is presented to calculate the cumulative probability of failure for random dynamic systems excited by a stationary input random process. Time series modeling is used to characterize the input random process. A detailed example demonstrates the accuracy and efficiency of the proposed importance sampling method over the traditional Monte Carlo simulation.

Sign in / Sign up

Export Citation Format

Share Document