scholarly journals Use of the Wilshire Equations to Correlate and Extrapolate Creep Data of HR6W and Sanicro 25

Materials ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 1585 ◽  
Author(s):  
Vito Cedro ◽  
Christian Garcia ◽  
Mark Render
Keyword(s):  
Experimental Data ◽  
Activation Energy ◽  
Goodness Of Fit ◽  
Creep Data ◽  
Creep Life ◽  
Short Term ◽  
Data Set ◽  
Creep Activation Energy ◽  
Region Splitting ◽  
Term Data

Advanced power plant alloys must endure high temperatures and pressures for durations at which creep data are often not available, necessitating the extrapolation of creep life. Many methods have been proposed to extrapolate creep life, and one of recent significance is a set of equations known as the Wilshire equations. With this method, multiple approaches can be used to determine creep activation energy, increase the goodness of fit of available experimental data, and improve the confidence level of calculating long-term creep strength at times well beyond the available experimental data. In this article, the Wilshire equation is used to extrapolate the creep life of HR6W and Sanicro 25, and different methods to determine creep activation energy, region splitting, the use of short-duration test data, and the omission of very-short-term data are investigated to determine their effect on correlation and calculations. It was found that using a known value of the activation energy of lattice self-diffusion, rather than calculating Q C * from each data set, is both the simplest and most viable method to determine Q C * . Region-splitting improved rupture time calculations for both alloys. Extrapolating creep life from short-term data for these alloys was found to be reasonable.

scholarly journals Use of the Wilshire Equations to Correlate and Extrapolate Creep Data of Inconel 617 and Nimonic 105

Materials ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 2534 ◽  
Author(s):  
Vito Cedro III ◽  
Christian Garcia ◽  
Mark Render
Keyword(s):  
Experimental Data ◽  
Activation Energy ◽  
Creep Strength ◽  
Goodness Of Fit ◽  
Creep Data ◽  
Creep Life ◽  
Self Diffusion ◽  
Inconel 617 ◽  
Term Creep

Advanced power plant alloys must endure high temperatures and pressures for durations at which creep data are often not available, necessitating the extrapolation of creep life. A recently developed creep life extrapolation method is the Wilshire equations, with which multiple approaches can be used to increase the goodness of fit of available experimental data and improve the confidence level of calculating long-term creep strength at times well beyond the available experimental data. In this article, the Wilshire equation is used to extrapolate the creep life of Inconel 617 and Nimonic 105 to 100,000 h. The use of (a) different methods to determine creep activation energy, (b) region splitting, (c) heat- and processing-specific tensile strength data, and (d) short-duration test data were investigated to determine their effects on correlation and extrapolation. For Inconel 617, using the activation energy of lattice self-diffusion as Q C * resulted in a poor fit with the experimental data. Additionally, the error of calculated rupture times worsened when splitting regions. For Nimonic 105, the error was reduced when heat- and processing-specific tensile strengths were used. Extrapolating Inconel 617 creep strength to 100,000 h life gave conservative results when compared to values calculated by the European Creep Collaborative Committee.

Prediction of Long Term Stress Rupture Data for 2124

2006 ◽  
Vol 519-521 ◽  
pp. 1041-1046 ◽  
Author(s):  
Brian Wilshire ◽  
H. Burt ◽  
N.P. Lavery
Keyword(s):  
Q Methodology ◽  
Fracture Behavior ◽  
Stress Rupture ◽  
Creep Data ◽  
Short Term ◽  
Rupture Data ◽  
Term Creep ◽  
Term Data ◽  
Short Term Creep

The standard power law approaches widely used to describe creep and creep fracture behavior have not led to theories capable of predicting long-term data. Similarly, traditional parametric methods for property rationalization also have limited predictive capabilities. In contrast, quantifying the shapes of short-term creep curves using the q methodology introduces several physically-meaningful procedures for creep data rationalization and prediction, which allow straightforward estimation of the 100,000 hour stress rupture values for the aluminum alloy, 2124.

Short-Term Creep Data Based Long-Term Creep Life Predictability for Grade 92 Steels and Its Microstructural Basis

2018 ◽  
Vol 25 (3) ◽  
pp. 713-722 ◽  
Author(s):  
Seen Chan Kim ◽  
Jae-Hyeok Shim ◽  
Woo-Sang Jung ◽  
Yoon Suk Choi
Keyword(s):  
Creep Data ◽  
Creep Life ◽  
Short Term ◽  
Term Creep ◽  
Short Term Creep

Methodology of Creep Data Analysis for Advanced High Cr Ferritic Steel

2007 ◽  
Author(s):  
Kouichi Maruyama ◽  
Kyosuke Yoshimi
Keyword(s):  
Data Analysis ◽  
Rupture Life ◽  
Creep Rupture ◽  
Creep Data ◽  
Short Term ◽  
Data Set ◽  
Region Analysis ◽  
Rupture Data ◽  
Term Creep

Long term creep rupture life is usually evaluated from short term data by a time-temperature parameter (TTP) method. The apparent activation energy Q for rupture life of steels sometimes changes from a high value of short term creep to a low value of long term creep. However, the conventional TTP analyses ignore the decrease in Q, resulting in the overestimation of rupture life recognized recently in advanced high Cr ferritic steels. A multi region analysis of creep rupture data is applied to a creep data set of Gr.122 steel; in the analysis a creep rupture data is divided into several data sets so that Q value is unique in each divided data set. The multi region analysis provides the best fit to the data and the lowest value of 105 h creep rupture strength among the three ways of data analysis examined. The conventional single region analysis cannot correctly represent the data points and predicts the highest strength. A half of 0.2% proof stress could not be an appropriate boundary for dividing data to be used in the multi region analysis. In the 2001 Edition of ASME Code an F average concept has been proposed as a substitution for the safety factor of 2/3 for average rupture stress. The allowable stress of Gr.122 steel may decrease significantly when the F average concept and the multi region analysis are adopted.

An alternative to the goodness of fit

2016 ◽  
Vol 72 (6) ◽  
pp. 696-703 ◽  
Author(s):  
Julian Henn
Keyword(s):  
Experimental Data ◽  
Goodness Of Fit ◽  
Systematic Errors ◽  
Data Sets ◽  
Alternative Measure ◽  
Data Set ◽  
Statistical Errors ◽  
Entire Data ◽  
Squared Residuals ◽  
The Ideal

An alternative measure to the goodness of fit (GoF) is developed and applied to experimental data. The alternative goodness of fit squared (aGoFs) demonstrates that the GoF regularly fails to provide evidence for the presence of systematic errors, because certain requirements are not met. These requirements are briefly discussed. It is shown that in many experimental data sets a correlation between the squared residuals and the variance of observed intensities exists. These correlations corrupt the GoF and lead to artificially reduced values in the GoF and in the numerical value of thewR(F2). Remaining systematic errors in the data sets are veiled by this mechanism. In data sets where these correlations do not appear for the entire data set, they often appear for the decile of largest variances of observed intensities. Additionally, statistical errors for the squared goodness of fit, GoFs, and the aGoFs are developed and applied to experimental data. This measure shows how significantly the GoFs and aGoFs deviate from the ideal value one.

Prediction of long-term cancer survival from a short-term data set?

2003 ◽  
Vol 48 (12) ◽  
pp. L35-L35
Author(s):  
G Bruggmoser, F Heinemann, N Hodapp R hner
Keyword(s):  
Cancer Survival ◽  
Short Term ◽  
Data Set ◽  
Term Data

scholarly journals The impact of burning on lion Panthera leo habitat choice in an African savanna

2013 ◽  
Vol 59 (3) ◽  
pp. 335-339 ◽  
Author(s):  
Stephanie Eby ◽  
Anna Mosser ◽  
Ali Swanson ◽  
Craig Packer ◽  
Mark Ritchie
Keyword(s):  
National Park ◽  
Habitat Choice ◽  
Territory Size ◽  
Short Term ◽  
Data Set ◽  
Burned Areas ◽  
The Impact ◽  
Term Data ◽  
Prey Attraction

Abstract Carnivores play a central role in ecosystem processes by exerting top-down control, while fire exerts bottom-up control in ecosystems throughout the world, yet, little is known about how fire affects short-term carnivore distributions across the landscape. Through the use of a long-term data set we investigated the distribution of lions, during the daytime, in relation to burned areas in Serengeti National Park, Tanzania. We found that lions avoid burned areas despite the fact that herbivores, their prey, are attracted to burned areas. Prey attraction, however, likely results from the reduction in cover caused by burning, that may thereby decrease lion hunting success. Lions also do not preferentially utilize the edges of burned areas over unburned areas despite the possibility that edges would combine the benefit of cover with proximity to abundant prey. Despite the fact that lions avoid burned areas, lion territory size and reproductive success were not affected by the proportion of the territory burned each year. Therefore, burning does not seem to reduce lion fitness perhaps because of the heterogeneity of burned areas across the landscape or because it is possible that when hunting at night lions visit burned areas despite their daytime avoidance of these areas.

scholarly journals Separating Predicted Randomness from Residual Behavior

2020 ◽  
Author(s):  
Jose Apesteguia ◽  
Miguel A Ballester
Keyword(s):  
Experimental Data ◽  
Goodness Of Fit ◽  
Choice Model ◽  
Choice Models ◽  
Data Set ◽  
Stochastic Choice

Abstract We propose a novel measure of goodness of fit for stochastic choice models, that is, the maximal fraction of data that can be reconciled with the model. The procedure is to separate the data into two parts: one generated by the best specification of the model and another representing residual behavior. We claim that the three elements involved in a separation are instrumental in understanding the data. We show how to apply our approach to any stochastic choice model and then study the case of four well-known models, each capturing a different notion of randomness. We illustrate our results with an experimental data set.

Lurasidone Gets Nod Based on Short-Term Data

2010 ◽  
Vol 38 (11) ◽  
pp. 6
Author(s):  
ELIZABETH MECHCATIE
Keyword(s):  
Short Term ◽  
Term Data
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