scholarly journals Reliability analysis of a two-unit standby system by computer simulation

2003 ◽  
Vol 13 (1) ◽  
pp. 85-94
Author(s):  
Tatjana Davidovic ◽  
Slobodanka Jankovic
Keyword(s):  
Computer Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Reliability Analysis ◽  
Preventive Maintenance ◽  
Explicit Formulas ◽  
Unit System ◽  
Different Types ◽  
Standby System

We study the two-unit standby system with repair and with preventive maintenance. Preventive maintenance is introduced in order to make the lifetime of the system longer. Using Monte-Carlo method we simulate the work of the two-unit system and we analyze the influence of different types of preventive maintenance on reliability of the system. Monte-Carlo method enables us to find estimates of various parameters relevant to the system for which there exist no explicit formulas in the literature. .

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.

Evaluation of Measurement Uncertainties for Pneumatic Multi-Hole Probes Using a Monte Carlo Method

2016 ◽  
Author(s):  
Magnus Hölle ◽  
Christian Bartsch ◽  
Peter Jeschke
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Compressible Flows ◽  
Uncertainty Evaluation ◽  
Calibration Data ◽  
Input Quantity ◽  
Parameter Evaluation ◽  
Polynomial Curve ◽  
Different Types ◽  
Measurement Uncertainties

The subject of this paper is a statistical method for the accurate evaluation of the uncertainties for pneumatic multi-hole probe measurements. The method can be applied to different types of evaluation algorithms and is suitable for steady flowfield measurements in compressible flows. The evaluation of uncertainties is performed by a Monte Carlo method (MCM), which is based on the statistical law of large numbers. Each input quantity, including calibration and measurement quantities, is randomly varied on the basis of its corresponding probability density function (PDF) and propagated through the deterministic parameter evaluation algorithm. Other than linear Taylor series based uncertainty evaluation methods, MCM features several advantages. On the one hand, MCM does not suffer from lower-order expansion errors and can therefore reproduce nonlinearity effects. On the other hand, different types of PDFs can be assumed for the input quantities and the corresponding coverage intervals can be calculated for any coverage probability. To demonstrate the uncertainty evaluation, a calibration and subsequent measurements in the wake of an airfoil with a 5-hole probe are performed. MCM is applied to different parameter evaluation algorithms. It is found that the MCM approach presented cannot be applied to polynomial curve fits, if the differences between the calibration data and the polynomial curve fits are of the same order of magnitude compared to the calibration uncertainty. Since this method has not yet been used for the evaluation of measurement uncertainties for pneumatic multi-hole probes, the aim of the paper is to present a highly accurate and easy-to-implement uncertainty evaluation method.

Probabilistic Approach to Stability Analysis of a Reinforced Concrete Retaining Wall

2014 ◽  
Vol 1079-1080 ◽  
pp. 248-251
Author(s):  
David Pustka
Keyword(s):  
Computer Simulation ◽  
Monte Carlo ◽  
Reinforced Concrete ◽  
Stability Analysis ◽  
Structural Optimization ◽  
Reliability Analysis ◽  
Retaining Wall ◽  
Rapid Development ◽  
Probabilistic Approach ◽  
Simulation Monte Carlo

Rapid development of computer technologies brings possibility to exploitpowerful computers for reliability analysis of (civil) engineering structuresunder consideration of random properties of various quantities influencingtheir resulting reliabilities. Aim of this paper is to outline possibility ofutilization of computer simulation Monte Carlo to predict reliability of areinforced concrete retaining wall from the viewpoint of possible loss ofstability. One of advantages of this approach is possibility to quantifyprobability of failure allowing structural optimization leading to design ofmore effective structures.

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