Statistical evaluation of promoted ignition test data

Suvorovs, Terese, Ward, Nicholas R., Steinberg, Theodore A. and Wilson, Richard (2007) Statistical evaluation of promoted ignition test data. Journal of ASTM International, 4 7: 1-13. doi:10.1520/JAI101068


Author Suvorovs, Terese
Ward, Nicholas R.
Steinberg, Theodore A.
Wilson, Richard
Title Statistical evaluation of promoted ignition test data
Journal name Journal of ASTM International   Check publisher's open access policy
ISSN 1546-962X
Publication date 2007-07
Sub-type Article (original research)
DOI 10.1520/JAI101068
Volume 4
Issue 7
Start page 1
End page 13
Total pages 13
Editor Dean, S. W.
Place of publication United States
Publisher ASTM International
Collection year 2008
Language eng
Subject 230200 Statistics
C1
670800 Basic Metal Products (incl. Smelting)
Abstract This paper presents an analysis of promoted ignition test data based on formal statistical techniques. Logistic regression is identified as an appropriate method for modeling binary burn–no-burn test data. Logistic regression is applied to the data sets from promoted ignition testing of two Hastelloy® alloys, C-276 and G-3, previously published by Zawierucha , ASTM STP 1111, ASTM International, West Conshohocken, PA, 1991, pp. 270–287. The logistic regression model is used to predict the burn probability over a range of pressures in the nonflammable and flammable domains, and was shown to fit the raw data well. Confidence intervals for the model are also determined, allowing the uncertainty associated with the predicted burn probabilities to be quantified. The relationship between the amount of test data available and confidence levels is discussed, and recommendations are made to improve the current standard test methodology. In particular, more repeated tests are required at all pressure levels considered, and tests are also required at pressures in the nonflammable domain (in addition to the flammable domain). It is shown that there is a low level of statistical confidence associated with the definition of threshold pressure under the current methodology. It is concluded that the use of the logistic regression model is beneficial since it allows for the quantification of burn probabilities and confidence levels, which enables the definition of threshold pressure to be related to a critical burn probability threshold.
Keyword promoted-ignition
statistics
logistic regression
model
threshold pressure
Q-Index Code C1
Q-Index Status Confirmed Code

 
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Created: Mon, 21 Apr 2008, 11:35:00 EST by Marie Grove on behalf of School of Mathematics & Physics