Simple Effective Conservative Treatment of Uncertainty From Sparse Samples of Random Variables and Functions
| Autor: | Justin Winokur, Nicole L. Breivik, J. F. Dempsey, John R. Lewis, Vicente J. Romero, George Edgar Orient, Benjamin B. Schroeder, Bonnie R. Antoun |
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| Rok vydání: | 2018 |
| Předmět: |
020301 aerospace & aeronautics
Computer science Mechanical Engineering 02 engineering and technology Materials testing 01 natural sciences Conservative treatment 010104 statistics & probability 0203 mechanical engineering Simple (abstract algebra) Applied mathematics Engineering simulation 0101 mathematics Uncertainty quantification Safety Risk Reliability and Quality Safety Research Random variable |
| Zdroj: | ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg. 4 |
| ISSN: | 2332-9025 2332-9017 |
| DOI: | 10.1115/1.4039558 |
| Popis: | 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 (QOIs) (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 or bound response variation 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 (TIs), 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-normal 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. The simple TI method is also demonstrated to be very economical. |
| Databáze: | OpenAIRE |
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