Efficient Fitting Procedure for Corrosion Rate Distribution in View of Reliability Analysis

2017 ◽  
Author(s):  
Shinsuke Sakai ◽  
Takuyo Kaida
Keyword(s):  
Monte Carlo Simulation ◽  
Corrosion Rate ◽  
Reliability Analysis ◽  
Estimation Of Parameters ◽  
Tail Region ◽  
Rate Distribution ◽  
Fitting Procedure ◽  
Fitting Procedures ◽  
Gumbel Model ◽  
Fitting Accuracy

The Gumbel model is widely used for the theoretical distribution of the corrosion rate. In applying the reliability analysis, the parameters of the distribution must be estimated from the inspected data. The estimation of parameters is done by using some fitting procedures. However, it is not necessarily clear which fitting procedure is suitable in view of reliability analysis. Especially, the fitting accuracy around tail region is possibly influence the reliability analysis. In this study, the efficient fitting procedure for the corrosion rate distribution in view of reliability analysis was investigated using Monte Carlo Simulation together with reliability analysis.

Seeking the Good Fitting Procedure of PALS Polymer Spectra

2008 ◽  
Vol 607 ◽  
pp. 64-66
Author(s):  
Nicolas Laforest ◽  
Jérémie De Baerdemaeker ◽  
Corine Bas ◽  
Charles Dauwe
Keyword(s):  
Experimental Data ◽  
Low Temperature ◽  
Positron Annihilation ◽  
Physical Meaning ◽  
Lifetime Measurements ◽  
Positron Annihilation Lifetime ◽  
Fitting Procedure ◽  
Fitting Procedures ◽  
Blob Model

Positron annihilation lifetime measurements on polymethylmethacrylate (PMMA) at low temperature were performed. Different discrete fitting procedures have been used to analyze the experimental data. It shows that the extracted parameters depend strongly on the fitting procedure. The physical meaning of the results is discussed. The blob model seems to give the best annihilation parameters.

Research on Prediction of Carbon Steel in SO2 Atmospheric Corrosion Using Grey Model

2014 ◽  
Vol 507 ◽  
pp. 258-262
Author(s):  
Ping Wang ◽  
Zhao Hui Yin ◽  
Han Tao Ren ◽  
Song Xu
Keyword(s):  
Carbon Steel ◽  
Corrosion Rate ◽  
Prediction Model ◽  
Relative Error ◽  
Atmospheric Corrosion ◽  
Grey Model ◽  
Fitting Accuracy

The rate of carbon steel in SO2 Atmospheric Corrosion was modeled by grey model GM (1, 1). The accuracy and rationality of prediction model have been evaluated. The result indicated that the model had a better fitting accuracy. By comparing the calculated values with a predicted atmospheric corrosion rate of carbon steel after 264h, it showed that its relative error has been just 0.5619% which had higher forecast reliability.

scholarly journals Effect of Mathematical Modeling and Fitting Procedures on the Assessment of Critical Speed and Its Relationship With Aerobic Fitness Parameters

2021 ◽  
Vol 12 ◽  
Author(s):  
Aurélien Patoz ◽  
Nicola Pedrani ◽  
Romain Spicher ◽  
André Berchtold ◽  
Fabio Borrani ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Aerobic Fitness ◽  
Critical Speed ◽  
Ventilatory Threshold ◽  
Information Criterion ◽  
Accurate Estimation ◽  
Running Speed ◽  
Fitting Procedure ◽  
Fitting Procedures ◽  
Fitness Parameters

An accurate estimation of critical speed (CS) is important to accurately define the boundary between heavy and severe intensity domains when prescribing exercise. Hence, our aim was to compare CS estimates obtained by statistically appropriate fitting procedures, i.e., regression analyses that correctly consider the dependent variables of the underlying models. A second aim was to determine the correlations between estimated CS and aerobic fitness parameters, i.e., ventilatory threshold, respiratory compensation point, and maximal rate of oxygen uptake. Sixteen male runners performed a maximal incremental aerobic test and four exhaustive runs at 90, 100, 110, and 120% of the peak speed of the incremental test on a treadmill. Then, two mathematically equivalent formulations (time as function of running speed and distance as function of running speed) of three different mathematical models (two-parameter, three-parameter, and three-parameter exponential) were employed to estimate CS, the distance that can be run above CS (d′), and if applicable, the maximal instantaneous running speed (smax). A significant effect of the mathematical model was observed when estimating CS, d′, and smax (P < 0.001), but there was no effect of the fitting procedure (P > 0.77). The three-parameter model had the best fit quality (smallest Akaike information criterion) of the CS estimates but the highest 90% confidence intervals and combined standard error of estimates (%SEE). The 90% CI and %SEE were similar when comparing the two fitting procedures for a given model. High and very high correlations were obtained between CS and aerobic fitness parameters for the three different models (r ≥ 0.77) as well as reasonably small SEE (SEE ≤ 6.8%). However, our results showed no further support for selecting the best mathematical model to estimate critical speed. Nonetheless, we suggest coaches choosing a mathematical model beforehand to define intensity domains and maintaining it over the running seasons.

A New Reliability Analysis Method Combining Adaptive Kriging With Weight Index Monte Carlo Simulation

2018 ◽  
Vol 54 (3) ◽  
pp. 1-4 ◽  
Author(s):  
Jiangang Ma ◽  
Ziyan Ren ◽  
Guoxin Zhao ◽  
Yanli Zhang ◽  
Chang-Seop Koh
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Analysis Method ◽  
Weight Index ◽  
Adaptive Kriging
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