Failure Probability Estimation of Wind Turbines by Enhanced Monte Carlo Method

2012 ◽  
Vol 138 (4) ◽  
pp. 379-389 ◽  
Author(s):  
M. T. Sichani ◽  
S. R. Nielsen ◽  
A. Naess
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Wind Turbines ◽  
Failure Probability ◽  
Probability Estimation

Failure probability estimation of steam turbine blades by enhanced Monte Carlo Method

2015 ◽  
Vol 56 ◽  
pp. 80-88 ◽  
Author(s):  
J.A. Rodríguez ◽  
J.C. Garcia ◽  
E. Alonso ◽  
Y. El Hamzaoui ◽  
J.M. Rodríguez ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Steam Turbine ◽  
Failure Probability ◽  
Turbine Blades ◽  
Probability Estimation ◽  
Steam Turbine Blades

Reliability Analysis of Embankment Slope in Yaan-Luku Expressway

2011 ◽  
Vol 71-78 ◽  
pp. 1360-1365
Author(s):  
Jian Quan Ma ◽  
Guang Jie Li ◽  
Shi Bo Li ◽  
Pei Hua Xu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Reliability Analysis ◽  
Failure Probability ◽  
Latin Hypercube Sampling ◽  
Natural State ◽  
State Function ◽  
Embankment Slope ◽  
The Times ◽  
Distribution Type

Take a typical cross-section of rockfill embankment slope in Yaan-Luku highway as the research object, reliability analysis is studied under the condition of water table of 840.85m, 851.50m, and loading condition of natural state and horizontal seismic acceleration of 0.2g, respectively. Raw data use Kolmogorov-Smirnov test (K-S test) to determine the distribution type of parametric variation. And the parameters were sampling with Latin hypercube sampling (LHS) method and Monte Carlo (MC) method, respectively, to obtain state function and determine safety factors and reliability indexes. A conclusion is drawn that the times of simulation based on LHS method were less than Monte Carlo method. Also the convergence of failure probability is better than the Monte Carlo method. The safety factor is greater than one and the failure probability has reached to 35.45% in condition of earthquake, which indicating that the instability of rockfill embankment slope is still possible.

An Improved FOSM Method for Calculating the Failure Probability of Welded Pipes with Cracks

2004 ◽  
Vol 261-263 ◽  
pp. 561-566
Author(s):  
Li Xing Huo ◽  
Min Liu ◽  
You Feng Zhang ◽  
Fang Juan Qi
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Failure Probability ◽  
Safety Assessment ◽  
Linear Expansion ◽  
Failure Function ◽  
The Monte Carlo Method

To increase the accuracy of R-F method, it is necessary to solve the problems of the linear expansion of failure function and non-normal variables. In this paper, the improved FOSM method was applied to calculate the failure probability of welded pipes with cracks. The examples show that this method is simple, efficient and accurate for reliability safety assessment of welded pipes with cracks. It can save more time than the Monte Carlo method does, so that the improved FOSM method is recommended for general engineering reliability safety assessment of welded pipes with cracks.

scholarly journals Failure Probability Estimation Using Asymptotic Sampling and Its Dependence upon the Selected Sampling Scheme

2017 ◽  
Vol 17 (2) ◽  
pp. 65-72
Author(s):  
Magdalena Martinásková ◽  
Miroslav Vořechovský
Keyword(s):  
Monte Carlo ◽  
Failure Probability ◽  
Sampling Scheme ◽  
Probability Estimation ◽  
Test Functions ◽  
Random Vectors ◽  
Sampling Schemes ◽  
Alternative Means ◽  
Asymptotic Sampling

Abstract The article examines the use of Asymptotic Sampling (AS) for the estimation of failure probability. The AS algorithm requires samples of multidimensional Gaussian random vectors, which may be obtained by many alternative means that influence the performance of the AS method. Several reliability problems (test functions) have been selected in order to test AS with various sampling schemes: (i) Monte Carlo designs; (ii) LHS designs optimized using the Periodic Audze-Eglājs (PAE) criterion; (iii) designs prepared using Sobol’ sequences. All results are compared with the exact failure probability value.

PERMUTATIONAL METHODS FOR PERFORMANCE ANALYSIS OF STOCHASTIC FLOW NETWORKS

2013 ◽  
Vol 28 (1) ◽  
pp. 21-38 ◽  
Author(s):  
Ilya Gertsbakh ◽  
Reuven Rubinstein ◽  
Yoseph Shpungin ◽  
Radislav Vaisman
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Performance Analysis ◽  
Failure Probability ◽  
Stochastic Flow ◽  
Maximal Flow ◽  
Convergence Properties ◽  
Flow Networks ◽  
Flow Network ◽  
Random Failures

In this paper we show how the permutation Monte Carlo method, originally developed for reliability networks, can be successfully adapted for stochastic flow networks, and in particular for estimation of the probability that the maximal flow in such a network is above some fixed level, called the threshold. A stochastic flow network is defined as one, where the edges are subject to random failures. A failed edge is assumed to be erased (broken) and, thus, not able to deliver any flow. We consider two models; one where the edges fail with the same failure probability and another where they fail with different failure probabilities. For each model we construct a different algorithm for estimation of the desired probability; in the former case it is based on the well known notion of the D-spectrum and in the later one—on the permutational Monte Carlo. We discuss the convergence properties of our estimators and present supportive numerical results.

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