Simple effective conservative treatment of uncertainty from sparse samples of random functions

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.; Red-Horse, John R.

doi:10.1115/1.4039558

Title: Simple effective conservative treatment of uncertainty from sparse samples of random functions

Abstract

This paper examines the variability of predicted responses when multiple stress-strain 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 stress-strain curves used for the materials in the model. We desire to estimate response variability when only a few stress-strain 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 random-function source. A simple classical statistical method, Tolerance Intervals, is tested for effectively treating sparse stress-strain curve data. The method is found to perform well on the highly nonlinear input-to-output response mappings and non-standard response distributions in the can-crush 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 » economical.« less

Authors:

Romero, Vicente J. ^[1]; Schroeder, Benjamin B. ^[1]; Dempsey, J. Franklin ^[1]; Lewis, John R. ^[1]; Breivik, Nicole L. ^[1]; Orient, George Edgar ^[1]; Antoun, Bonnie R. ^[1]; Winokur, Justin ^[1]; Glickman, Matthew R. ^[1]; Red-Horse, John R. ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Publication Date:: Mon Apr 30 00:00:00 EDT 2018

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1367209

Report Number(s):: SAND-2017-5177J
Journal ID: ISSN 2332-9017; 653342

Grant/Contract Number:: AC04-94AL85000

Resource Type:: Journal Article: Accepted Manuscript

Journal Name:: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. Part B. Mechanical Engineering

Additional Journal Information:: Journal Volume: 4; Journal Issue: 4; Journal ID: ISSN 2332-9017

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 Red-Horse, 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.

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                    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., & Red-Horse, John R. Simple effective conservative treatment of uncertainty from sparse samples of random functions.  United States.  https://doi.org/10.1115/1.4039558

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                    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 Red-Horse, John R. 2018.  
        "Simple effective conservative treatment of uncertainty from sparse samples of random functions".  United States.  https://doi.org/10.1115/1.4039558.  https://www.osti.gov/servlets/purl/1367209.

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@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 Red-Horse, John R.},

  abstractNote = {This paper examines the variability of predicted responses when multiple stress-strain 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 stress-strain curves used for the materials in the model. We desire to estimate response variability when only a few stress-strain 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 random-function source. A simple classical statistical method, Tolerance Intervals, is tested for effectively treating sparse stress-strain curve data. The method is found to perform well on the highly nonlinear input-to-output response mappings and non-standard response distributions in the can-crush 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},

  url          = {https://www.osti.gov/biblio/1367209},
  journal      = {ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. Part B. Mechanical Engineering},

  issn         = {2332-9017},

  number       = 4,

  volume       = 4,

  place        = {United States},

  year         = {Mon Apr 30 00:00:00 EDT 2018},

  month        = {Mon Apr 30 00:00:00 EDT 2018}

}

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Title: Simple effective conservative treatment of uncertainty from sparse samples of random functions

Abstract

Citation Formats

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