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contributor authorRomero, Vicente J.
contributor authorSchroeder, Benjamin B.
contributor authorDempsey, James F.
contributor authorBreivik, Nicole L.
contributor authorOrient, George E.
contributor authorAntoun, Bonnie R.
contributor authorLewis, John R.
contributor authorWinokur, Justin G.
date accessioned2019-02-28T11:02:17Z
date available2019-02-28T11:02:17Z
date copyright4/30/2018 12:00:00 AM
date issued2018
identifier issn2332-9017
identifier otherrisk_004_04_041006.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4251976
description abstractThis 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.
publisherThe American Society of Mechanical Engineers (ASME)
titleSimple Effective Conservative Treatment of Uncertainty From Sparse Samples of Random Variables and Functions
typeJournal Paper
journal volume4
journal issue4
journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
identifier doi10.1115/1.4039558
journal fristpage41006
journal lastpage041006-17
treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:;2018:;volume( 004 ):;issue:004
contenttypeFulltext


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