Stochastic Analysis of Groundwater Traveltime for Long-Term Repository Performance Assessment

1983 ◽  
Vol 26 ◽  
Author(s):  
P.M. Clifton ◽  
R.G. Baca ◽  
R.C. Arnett
Keyword(s):  
Monte Carlo ◽  
Random Number Generator ◽  
Monte Carlo Analysis ◽  
Effective Porosity ◽  
Specific Storage ◽  
Hanford Site ◽  
Transient Problem ◽  
Degree Of Confidence ◽  
Using Data

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.

scholarly journals Maintenance Costs and Advanced Maintenance Techniques: Survey and Analysis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Douglas S Thomas ◽  
Brian Weiss
Keyword(s):  
Monte Carlo ◽  
Preventive Maintenance ◽  
Monte Carlo Analysis ◽  
Predictive Maintenance ◽  
The Other ◽  
Maintenance Costs ◽  
Maintenance Techniques ◽  
Using Data

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.

scholarly journals The mode of the climacogram estimator for a Gaussian Hurst-Kolmogorov process

2019 ◽  
Vol 22 (1) ◽  
pp. 160-169 ◽  
Author(s):  
Panayiotis Dimitriadis ◽  
Demetris Koutsoyiannis
Keyword(s):  
Monte Carlo ◽  
Gaussian Processes ◽  
Gaussian White Noise ◽  
Monte Carlo Analysis ◽  
Statistical Bias ◽  
Proposed Model ◽  
Geophysical Processes ◽  
Simple Scaling ◽  
Non Gaussian

Abstract Geophysical processes are often characterized by long-term persistence. An important characteristic of such behaviour is the induced large statistical bias, i.e. the deviation of a statistical characteristic from its theoretical value. Here, we examine the most probable value (i.e. mode) of the estimator of variance to adjust the model for statistical bias. Particularly, we conduct an extensive Monte Carlo analysis based on the climacogram (i.e. variance of the average process vs. scale) of the simple scaling (Gaussian Hurst-Kolmogorov) process, and we show that its classical estimator is highly skewed especially in large scales. We observe that the mode of the climacogram estimator can be well approximated by its lower quartile (25% quantile). To derive an easy-to-fit empirical expression for the mode, we assume that the climacogram estimator follows a gamma distribution, an assumption strictly valid for Gaussian white noise processes. The results suggest that when a single timeseries is available, it is advantageous to estimate the Hurst parameter using the mode estimator rather than the expected one. Finally, it is discussed that while the proposed model for mode bias works well for Gaussian processes, for higher accuracy and non-Gaussian processes, one should perform a Monte Carlo simulation following an explicit generation algorithm.

Generation of Correlated Logistic-Normal Random Variates for Medical Decision Trees

1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark
Keyword(s):  
Monte Carlo ◽  
Decision Trees ◽  
Computer Algebra ◽  
Taylor Series ◽  
Computer Algebra System ◽  
Random Variable ◽  
Monte Carlo Analysis ◽  
Normal Random Variable ◽  
Medical Decision ◽  
Theoretical Results

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.

Monte Carlo Analysis of Nonequilibrium Ionized Flows.

1996 ◽  
Author(s):  
Iain D. Boyd ◽  
Xiaoming Liu ◽  
Jitendra Balakrishnan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Analysis

Clinical impact of myositis-specific autoantibodies on long-term prognosis of juvenile idiopathic inflammatory myopathies: multicentre study

Rheumatology ◽  
2021 ◽  
Author(s):  
Yuichi Yamasaki ◽  
Norimoto Kobayashi ◽  
Shinji Akioka ◽  
Kazuko Yamazaki ◽  
Shunichiro Takezaki ◽  
...  
Keyword(s):  
Matrix Protein ◽  
Multiple Drug ◽  
Idiopathic Inflammatory Myopathies ◽  
Inflammatory Myopathies ◽  
Gamma Subunits ◽  
Specific Autoantibody ◽  
Long Term Prognosis ◽  
Using Data ◽  
Drug Free

Abstract Objectives This study aimed to investigate the clinical characteristics, treatment and prognosis of juvenile idiopathic inflammatory myopathies (JIIM) in Japan for each myositis-specific autoantibody (MSA) profile. Methods A multicentre, retrospective study was conducted using data of patients with JIIM at nine paediatric rheumatology centres in Japan. Patients with MSA profiles, determined by immunoprecipitation using stored serum from the active stage, were included. Results MSA were detected in 85 of 96 cases eligible for the analyses. Over 90% of the patients in this study had one of the following three MSA types: anti-melanoma differentiation-associated protein 5 (MDA5) (n = 31), anti-transcriptional intermediary factor 1 alpha and/or gamma subunits (TIF1γ) (n = 25) and anti-nuclear matrix protein 2 (NXP2) (n = 25) antibodies. Gottron papules and periungual capillary abnormalities were the most common signs of every MSA group in the initial phase. The presence of interstitial lung disease (ILD) was the highest risk factor for patients with anti-MDA5 antibodies. Most patients were administered multiple drug therapies: glucocorticoids and MTX were administered to patients with anti-TIF1γ or anti-NXP2 antibodies. Half of the patients with anti-MDA5 antibodies received more than three medications including i.v. CYC, especially patients with ILD. Patients with anti-MDA5 antibodies were more likely to achieve drug-free remission (29 vs 21%) and less likely to relapse (26 vs 44%) than others. Conclusion Anti-MDA5 antibodies are the most common MSA type in Japan, and patients with this antibody are characterized by ILD at onset, multiple medications including i.v. CYC, drug-free remission, and a lower frequency of relapse. New therapeutic strategies are required for other MSA types.

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