Simple effective conservative treatment of uncertainty from sparse samples of random functions
Abstract
This paper examines the variability of predicted responses when multiple stressstrain curves (reflecting variability from replicate material tests) are propagated through a finite element model of a ductile steel can being slowly crushed. Over 140 response quantities of interest (including displacements, stresses, strains, and calculated measures of material damage) are tracked in the simulations. Each response quantity’s behavior varies according to the particular stressstrain curves used for the materials in the model. We desire to estimate response variability when only a few stressstrain curve samples are available from material testing. Propagation of just a few samples will usually result in significantly underestimated response uncertainty relative to propagation of a much larger population that adequately samples the presiding randomfunction source. A simple classical statistical method, Tolerance Intervals, is tested for effectively treating sparse stressstrain curve data. The method is found to perform well on the highly nonlinear inputtooutput response mappings and nonstandard response distributions in the cancrush problem. The results and discussion in this paper support a proposition that the method will apply similarly well for other sparsely sampled random variable or function data, whether from experiments or models. Finally, the simple Tolerance Interval method is also demonstrated to be verymore »
 Authors:

 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Publication Date:
 Research Org.:
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States); Sandia National Lab. (SNLCA), Livermore, CA (United States)
 Sponsoring Org.:
 USDOE National Nuclear Security Administration (NNSA)
 OSTI Identifier:
 1367209
 Report Number(s):
 SAND20175177J
Journal ID: ISSN 23329017; 653342
 Grant/Contract Number:
 AC0494AL85000
 Resource Type:
 Journal Article: Accepted Manuscript
 Journal Name:
 ASCEASME Journal of Risk and Uncertainty in Engineering Systems. Part B. Mechanical Engineering
 Additional Journal Information:
 Journal Volume: 4; Journal Issue: 4; Journal ID: ISSN 23329017
 Publisher:
 American Society of Mechanical Engineers
 Country of Publication:
 United States
 Language:
 English
 Subject:
 36 MATERIALS SCIENCE
Citation Formats
Romero, Vicente J., Schroeder, Benjamin B., Dempsey, J. Franklin, Lewis, John R., Breivik, Nicole L., Orient, George Edgar, Antoun, Bonnie R., Winokur, Justin, Glickman, Matthew R., and RedHorse, John R. Simple effective conservative treatment of uncertainty from sparse samples of random functions. United States: N. p., 2018.
Web. doi:10.1115/1.4039558.
Romero, Vicente J., Schroeder, Benjamin B., Dempsey, J. Franklin, Lewis, John R., Breivik, Nicole L., Orient, George Edgar, Antoun, Bonnie R., Winokur, Justin, Glickman, Matthew R., & RedHorse, John R. Simple effective conservative treatment of uncertainty from sparse samples of random functions. United States. doi:10.1115/1.4039558.
Romero, Vicente J., Schroeder, Benjamin B., Dempsey, J. Franklin, Lewis, John R., Breivik, Nicole L., Orient, George Edgar, Antoun, Bonnie R., Winokur, Justin, Glickman, Matthew R., and RedHorse, John R. Mon .
"Simple effective conservative treatment of uncertainty from sparse samples of random functions". United States. doi:10.1115/1.4039558. https://www.osti.gov/servlets/purl/1367209.
@article{osti_1367209,
title = {Simple effective conservative treatment of uncertainty from sparse samples of random functions},
author = {Romero, Vicente J. and Schroeder, Benjamin B. and Dempsey, J. Franklin and Lewis, John R. and Breivik, Nicole L. and Orient, George Edgar and Antoun, Bonnie R. and Winokur, Justin and Glickman, Matthew R. and RedHorse, John R.},
abstractNote = {This paper examines the variability of predicted responses when multiple stressstrain curves (reflecting variability from replicate material tests) are propagated through a finite element model of a ductile steel can being slowly crushed. Over 140 response quantities of interest (including displacements, stresses, strains, and calculated measures of material damage) are tracked in the simulations. Each response quantity’s behavior varies according to the particular stressstrain curves used for the materials in the model. We desire to estimate response variability when only a few stressstrain curve samples are available from material testing. Propagation of just a few samples will usually result in significantly underestimated response uncertainty relative to propagation of a much larger population that adequately samples the presiding randomfunction source. A simple classical statistical method, Tolerance Intervals, is tested for effectively treating sparse stressstrain curve data. The method is found to perform well on the highly nonlinear inputtooutput response mappings and nonstandard response distributions in the cancrush problem. The results and discussion in this paper support a proposition that the method will apply similarly well for other sparsely sampled random variable or function data, whether from experiments or models. Finally, the simple Tolerance Interval method is also demonstrated to be very economical.},
doi = {10.1115/1.4039558},
journal = {ASCEASME Journal of Risk and Uncertainty in Engineering Systems. Part B. Mechanical Engineering},
issn = {23329017},
number = 4,
volume = 4,
place = {United States},
year = {2018},
month = {4}
}
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