Publications

Roberts, Scott A.; Donohoe, Brendan D.; Martinez, Carianne M.; Krygier, Michael K.; Hernandez-Sanchez, Bernadette A.; Foster, Collin W.; Collins, Lincoln; Greene, Benjamin G.; Noble, David R.; Norris, Chance A.; Potter, Kevin M.; Roberts, Christine C.; Neal, Kyle D.; Bernard, Sylvain R.; Schroeder, Benjamin B.; Trembacki, Bradley L.; LaBonte, Tyler L.; Sharma, Krish S.; Ganter, Tyler G.; Jones, Jessica E.; Smith, Matthew D.

Abstract not provided.

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

Romero, Vicente J.; Schroeder, Benjamin B.; Dempsey, James F.; Lewis, John R.; Breivik, Nicole L.; Orient, George E.; Antoun, Bonnie R.; Winokur, Justin W.; Glickman, Matthew R.; Red-Horse, John R.

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.

Romero, Vicente J.; Bonney, Matthew S.; Schroeder, Benjamin B.; Weirs, Vincent G.

When very few samples of a random quantity are available from a source distribution of unknown shape, it is usually not possible to accurately infer the exact distribution from which the data samples come. Under-estimation of important quantities such as response variance and failure probabilities can result. For many engineering purposes, including design and risk analysis, we attempt to avoid under-estimation with a strategy to conservatively estimate (bound) these types of quantities -- without being overly conservative -- when only a few samples of a random quantity are available from model predictions or replicate experiments. This report examines a class of related sparse-data uncertainty representation and inference approaches that are relatively simple, inexpensive, and effective. Tradeoffs between the methods' conservatism, reliability, and risk versus number of data samples (cost) are quantified with multi-attribute metrics use d to assess method performance for conservative estimation of two representative quantities: central 95% of response; and 10^-4 probability of exceeding a response threshold in a tail of the distribution. Each method's performance is characterized with 10,000 random trials on a large number of diverse and challenging distributions. The best method and number of samples to use in a given circumstance depends on the uncertainty quantity to be estimated, the PDF character, and the desired reliability of bounding the true value. On the basis of this large data base and study, a strategy is proposed for selecting the method and number of samples for attaining reasonable credibility levels in bounding these types of quantities when sparse samples of random variables or functions are available from experiments or simulations.

Romero, Vicente J.; Schroeder, Benjamin B.; Glickman, Matthew R.; Winokur, Justin W.

Abstract not provided.

Romero, Vicente J.; Dempsey, James F.; Schroeder, Benjamin B.; Lewis, John R.; Orient, George E.; Breivik, Nicole L.; Antoun, Bonnie R.; Winokur, Justin W.; Glickman, Matthew R.; Red-Horse, John R.

Abstract not provided.

Romero, Vicente J.; Dempsey, James F.; Schroeder, Benjamin B.; Lewis, John R.; Breivik, Nicole L.; Orient, George E.; Antoun, Bonnie R.; Winokur, Justin W.; Glickman, Matthew R.; Red-Horse, John R.

Abstract not provided.