Spatio temporal Variation of the Density Distributions in Ar RF Plasmas Using the Monte Carlo Simulation/Legendre Polynomial Weighted Sampling Method

2003 ◽  
Vol 42 (Part 1, No. 3) ◽  
pp. 1445-1451 ◽  
Author(s):  
Ikuya Horie ◽  
Takuma Suzuki ◽  
Kazutaka Kitamori ◽  
Koichi Maruyama
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Temporal Variation ◽  
Legendre Polynomial ◽  
Sampling Method ◽  
Rf Plasmas ◽  
Density Distributions ◽  
Spatio Temporal

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.

scholarly journals Density Estimation of Spatio-temporal Point Patterns Using Moran's Statistic

2018 ◽  
Vol 7 (2) ◽  
pp. 80
Author(s):  
Jennifer L. Lorio ◽  
Norou Diawara ◽  
Lance A. Waller
Keyword(s):  
Infectious Disease ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Poisson Process ◽  
Common Factor ◽  
Disease Spread ◽  
Point Patterns ◽  
Rate Of Increase ◽  
Spatio Temporal ◽  
Over Time

Moran's Index is a statistic that measures spatial autocorrelation, quantifying the degree of dispersion (or spread) of objects in space. When investigating data in an area, a single Moran statistic may not give a sufficient summary of the autocorrelation spread. However, by partitioning the area and taking the Moran statistic of each subarea, we discover patterns of the local neighbors not otherwise apparent. In this paper, we consider the model of the spread of an infectious disease, incorporate time factor, and simulate a multilevel Poisson process where the dependence among the levels is captured by the rate of increase of the disease spread over time, steered by a common factor in the scale. The main consequence of our results is that our Moran statistic is calculated from an explicit algorithm in a Monte Carlo simulation setting. Results are compared to Geary's statistic and estimates of parameters under Poisson process are given.

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.

scholarly journals THE STUDY OF THE NUCLEUS–NUCLEUS INTERACTION POTENTIAL FOR 16O+27Al AND 16O+28Si FUSION REACTIONS

2012 ◽  
Vol 27 (06) ◽  
pp. 1250037 ◽  
Author(s):  
O. N. GHODSI ◽  
R. GHARAEI
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Method ◽  
Light Nuclei ◽  
Monte Carlo Simulation Method ◽  
Considerable Influence ◽  
Fusion Reactions ◽  
Nucleus Interaction ◽  
Nuclear Equation Of State ◽  
Density Distributions

Using the Monte Carlo simulation method accompanied by the modifying effects of the density distributions overlapping, we have examined the nuclear matter incompressibility effects for asymmetric systems with light nuclei, namely 16 O +27 Al and 16 O +28 Si fusion reactions. The obtained results show that the nuclear equation of state has considerable influence on the calculation of fusion probabilities for these asymmetric systems.

A novel reliability sensitivity analysis method based on directional sampling and Monte Carlo simulation

2020 ◽  
Vol 234 (4) ◽  
pp. 622-635
Author(s):  
Xiaobo Zhang ◽  
Zhenzhou Lu ◽  
Kai Cheng ◽  
Yanping Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Sampling Method ◽  
Simulation Method ◽  
Analysis Method ◽  
Gradient Information ◽  
Reliability Sensitivity ◽  
Global Reliability Sensitivity ◽  
By Products

Local reliability sensitivity and global reliability sensitivity are required in reliability-based design optimization, since they can provide rich information including variable importance ranking and gradient information. However, traditional Monte Carlo simulation is inefficient for engineering application. A novel numerical simulation method based on Monte Carlo simulation and directional sampling is proposed to simultaneously estimate local reliability sensitivity and global reliability sensitivity. By suitable transformation, local reliability sensitivity and global reliability sensitivity can be estimated simultaneously as by-products of reliability analysis for Monte Carlo simulation method. The key is how to efficiently classify Monte Carlo simulation samples into two categories: failure samples and safety samples. Directional sampling method, a classical reliability analysis method, is more efficient than Monte Carlo simulation for reliability analysis. A novel strategy based on nearest Euclidean distance is proposed to approximately screen out failure samples from Monte Carlo simulation samples using directional sampling samples. In the proposed method, local reliability sensitivity and global reliability sensitivity are by-products of reliability analysis using the directional sampling method. Different from existing methods, the proposed method does not introduce hypotheses and does not require additional gradient information. The advantages of the Monte Carlo simulation and directional sampling are well integrated in the proposed method. The accuracy and the efficiency of the proposed method for local reliability sensitivity and global reliability sensitivity are demonstrated by four numerical examples and two engineering examples including the headless rivet and the wing box structure.

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