Probabilistic Reanalysis Using Monte Carlo Simulation

An approach for Probabilistic Reanalysis (PRA) of a system is presented. PRA calculates very efficiently the system reliability or the average value of an attribute of a design for many probability distributions of the input variables, by performing a single Monte Carlo simulation. In addition, PRA...

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Bibliographic Details
Published in:SAE International Journal of Materials and Manufacturing 2009-01, Vol.1 (1), p.22-35, Article 2008-01-0215
Main Authors: Nikolaidis, Efstratios, Saleem, Sirine, Farizal, Zhang, Geng, Mourelatos, Zissimos P
Format: Article
Language:English
Subjects:
Cost estimates
Design
Design analysis
Design optimization
Determinism
Internal combustion engines
Mathematical independent variables
Monte Carlo methods
Monte Carlo simulation
Optimization
Parameter sensitivity
Probability distribution
Probability distributions
Probability theory
Random variables
Reliability analysis
Statistical analysis
System failures
System reliability
Vibration analysis
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Summary:An approach for Probabilistic Reanalysis (PRA) of a system is presented. PRA calculates very efficiently the system reliability or the average value of an attribute of a design for many probability distributions of the input variables, by performing a single Monte Carlo simulation. In addition, PRA calculates the sensitivity derivatives of the reliability to the parameters of the probability distributions. The approach is useful for analysis problems where reliability bounds need to be calculated because the probability distribution of the input variables is uncertain or for design problems where the design variables are random. The accuracy and efficiency of PRA is demonstrated on vibration analysis of a car and on system reliability-based optimization (RBDO) of an internal combustion engine.
ISSN:1946-3979
1946-3987
1946-3987
DOI:10.4271/2008-01-0215