Managing the Computational Cost of Monte Carlo Simulation with Importance Sampling by Considering the Value of Information

2013 ◽  
Vol 6 (3) ◽  
pp. 436-440 ◽  
Author(s):  
Efstratios Nikolaidis ◽  
Mahdi Norouzi ◽  
Zissimos Mourelatos ◽  
Vijitashwa Pandey
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Importance Sampling ◽  
Value Of Information ◽  
Computational Cost

Reliability Assessment of Power System Using Importance Sampling Technique Based on Layer Optimization Simulation

2011 ◽  
Vol 88-89 ◽  
pp. 554-558 ◽  
Author(s):  
Bin Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power System ◽  
Importance Sampling ◽  
System Reliability ◽  
Sampling Method ◽  
Sampling Technique ◽  
Test System ◽  
Importance Sampling Method ◽  
Importance Sampling Technique

An improved importance sampling method with layer simulation optimization is presented in this paper. Through the solution sequence of the components’ optimum biased factors according to their importance degree to system reliability, the presented technique can further accelerate the convergence speed of the Monte-Carlo simulation. The idea is that the multivariate distribution’ optimization of components in power system is transferred to many steps’ optimization based on importance sampling method with different optimum biased factors. The practice is that the components are layered according to their importance degree to the system reliability before the Monte-Carlo simulation, the more forward, the more important, and the optimum biased factors of components in the latest layer is searched while the importance sampling is carried out until the demanded accuracy is reached. The validity of the presented is verified using the IEEE-RTS79 test system.

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.

Optimal importance sampling for the Laplace transform of exponential Brownian functionals

2016 ◽  
Vol 53 (2) ◽  
pp. 531-542 ◽  
Author(s):  
Je Guk Kim
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Performance Analysis ◽  
Laplace Transform ◽  
Importance Sampling ◽  
Pricing Model ◽  
Asymptotically Optimal ◽  
Closed Form Solutions ◽  
The Laplace Transform ◽  
Exponential Brownian Functionals

Abstract We present an asymptotically optimal importance sampling for Monte Carlo simulation of the Laplace transform of exponential Brownian functionals which plays a prominent role in many disciplines. To this end we utilize the theory of large deviations to reduce finding an asymptotically optimal importance sampling measure to solving a calculus of variations problem. Closed-form solutions are obtained. In addition we also present a path to the test of regularity of optimal drift which is an issue in implementing the proposed method. The performance analysis of the method is provided through the Dothan bond pricing model.

scholarly journals Non-deterministic Approach for Reliability Evaluation of Steel Portal Frame

2019 ◽  
Vol 5 (8) ◽  
pp. 1684-1697
Author(s):  
Hawraa Qasim Jebur ◽  
Salah Rohaima Al-Zaidee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Structural Reliability ◽  
Limit State ◽  
Computational Cost ◽  
Probability Of Failure ◽  
Ultimate Limit State ◽  
Axial Forces ◽  
Portal Frame ◽  
Ultimate Limit

In recent years, more researches on structural reliability theory and methods have been carried out. In this study, a portal steel frame is considered. The reliability analysis for the frame is represented by the probability of failure, P_f, and the reliability index, β, that can be predicted based on the failure of the girders and columns. The probability of failure can be estimated dependent on the probability density function of two random variables, namely Capacity R, and Demand Q. The Monte Carlo simulation approach has been employed to consider the uncertainty the parameters of R, and Q. Matlab functions have been adopted to generate pseudo-random number for considered parameters. Although the Monte Carlo method is active and is widely used in reliability research, it has a disadvantage which represented by the requirement of large sample sizes to estimate the small probabilities of failure. This is leading to computational cost and time. Therefore, an Approximated Monte Carlo simulation method has been adopted for this issue. In this study, four performances have been considered include the serviceability deflection limit state, ultimate limit state for girder, ultimate limit state for the columns, and elastic stability. As the portal frame is a statically indeterminate structure, therefore bending moments, and axial forces cannot be determined based on static alone. A finite element parametric model has been prepared using Abaqus to deal with this aspect. The statistical analysis for the results samples show that all response data have lognormal distribution except of elastic critical buckling load which has a normal distribution.

A Comprehensive Procedure to Estimate the Probability of Extreme Vibration Levels Due to Mistuning

2010 ◽  
Vol 132 (11) ◽  
Author(s):  
Y.-J. Chan ◽  
D. J. Ewins
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Distribution Function ◽  
Importance Sampling ◽  
Gradient Method ◽  
Sampling Method ◽  
Optimization Analysis ◽  
Amplification Factors ◽  
Bladed Disk ◽  
Importance Sampling Method

A new procedure is developed to find the probabilities of extremely high amplification factors in mistuned bladed disk vibration levels, typical of events which occur rarely. While a rough estimate can be made by curve-fitting the distribution function generated in a Monte Carlo simulation, the procedure presented here can determine a much more accurate upper bound and the probabilities of amplification factors near to that bound. The procedure comprises an optimization analysis based on the conjugate gradient method and a stochastic simulation using the importance sampling method. Two examples are provided to illustrate the efficiency of the procedure, which can be 2 or 3 orders of magnitude more efficient than Monte Carlo simulations.

The Soft Ion Model in Monte Carlo Simulation of Molten Salts

1988 ◽  
Vol 43 (2) ◽  
pp. 129-132
Author(s):  
C. Margheritis ◽  
C. Sinistri
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Molten Salt ◽  
Molten Salts ◽  
Computational Cost ◽  
Coulomb Energy ◽  
Numerical Application ◽  
Polarization Energy ◽  
Simple Evaluation ◽  
Salt Systems

Abstract This paper describes a method for a simple evaluation of the polarization energy in molten salt systems, by which it is possible to go, without heavy computational cost, from the rigid to the soft ion model. The method is based on the observation that, within the movements of single ions in the Monte Carlo chain, the deviation of the polarization energy is a linear function of the deviation of the Coulomb energy.An extended numerical application has been carried out for molten Lil at 800, 1200 and 1453 (b. p.) K. The parameters that are mostly affected by the used model are put into evidence.

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