scholarly journals A model assessing cost of operating marine systems using data obtained from Monte Carlo analysis

Author(s):  
Daniel McNamara ◽  
Andrew Cunningham ◽  
Ian Jenkinson ◽  
Jin Wang
1983 ◽  
Vol 26 ◽  
Author(s):  
P.M. Clifton ◽  
R.G. Baca ◽  
R.C. Arnett

ABSTRACTThis paper describes a method of stochastically analyzing groundwater traveltime. The method uses a Monte Carlo technique to generate a suite of random spatial fields that are subsequently input to the groundwater flow and groundwater traveltime equations. Stochastic inputs to these equations can be (1) transmissivity (or hydraulic conductivity), (2) effective thickness (or effective porosity), or (3) boundary conditions. In a transient problem, storage coefficient (or specific storage) could also be stochastically treated. Spatial correlation in the random input fields is accounted for by means of a multivariate random-number generator, which requires the first two statistical moments of these fields to be specified. The output from the Monte Carlo analysis is a suite of groundwater traveltime realizations that can be used to derive exceedance probabilities. These probabilities provide a measure of the degree of confidence in meeting set criteria.A preliminary application of this method using data from the deep basalts beneath the Hanford Site is also presented. The results illustrate how this method can be used to evaluate compliance with a technical criterion relating to groundwater traveltime.


Author(s):  
Douglas S Thomas ◽  
Brian Weiss

The costs/benefits associated with investing in advanced maintenance techniques is not well understood. Using data collected from manufacturers, we estimate the national losses due to inadequate maintenance and make comparisons between those that rely on reactive maintenance, preventive maintenance, and predictive maintenance. The total annual costs/losses associated with maintenance is estimated to be on average $222.0 billion, as estimated using Monte Carlo analysis. Respondents were categorized into three groups and compared. The first group is the top 50 % of respondents that rely on reactive maintenance, measured in expenditures. The remaining respondents were split in half based on their reliance on predictive maintenance. The top 50 % of respondents in using reactive maintenance, measured in expenditures, compared to the other respondents suggests that there are substantial benefits of moving away from reactive maintenance toward preventive and/or predictive maintenance. The bottom 50 %, which relies more heavily on predictive and preventive maintenance, had 52.7 % less unplanned downtime and 78.5 % less defects. The comparison between the smaller two groups, which rely more heavily on preventive and predictive maintenance, shows that there is 18.5 % less unplanned downtime and 87.3 % less defects for those that rely more on predictive than preventive.


1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.


1996 ◽  
Author(s):  
Iain D. Boyd ◽  
Xiaoming Liu ◽  
Jitendra Balakrishnan

2021 ◽  
Vol 234 ◽  
pp. 113889
Author(s):  
Pietro Elia Campana ◽  
Luca Cioccolanti ◽  
Baptiste François ◽  
Jakub Jurasz ◽  
Yang Zhang ◽  
...  

2021 ◽  
Vol 171 ◽  
pp. 109638
Author(s):  
Tara Gray ◽  
Nema Bassiri ◽  
Shaquan David ◽  
Devanshi Yogeshkumar Patel ◽  
Sotirios Stathakis ◽  
...  

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