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

Journal Article · Mon Apr 30 00:00:00 EDT 2018 · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. Part B. Mechanical Engineering

DOI:https://doi.org/10.1115/1.4039558· OSTI ID:1367209

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]

---

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

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.

• Research Organization: Sandia National Laboratories (SNL-CA), Livermore, CA (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000
• OSTI ID: 1367209
• Report Number(s): SAND--2017-5177J; 653342
• Journal Information: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. Part B. Mechanical Engineering, Journal Name: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. Part B. Mechanical Engineering Journal Issue: 4 Vol. 4; ISSN 2332-9017
• Publisher: American Society of Mechanical Engineers
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE