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contributor authorRomero, Vicente J.
contributor authorWinokur, Justin G.
contributor authorOrient, George E.
contributor authorDempsey, James F.
date accessioned2022-02-06T05:49:10Z
date available2022-02-06T05:49:10Z
date copyright4/23/2021 12:00:00 AM
date issued2021
identifier issn2332-9017
identifier otherrisk_007_02_020912.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4278839
description abstractA discrete direct (DD) model calibration and uncertainty propagation approach is explained and demonstrated on a 4-parameter Johnson-Cook (J-C) strain-rate dependent material strength model for an aluminum alloy. The methodology's performance is characterized in many trials involving four random realizations of strain-rate dependent material-test data curves per trial, drawn from a large synthetic population. The J-C model is calibrated to particular combinations of the data curves to obtain calibration parameter sets which are then propagated to “Can Crush” structural model predictions to produce samples of predicted response variability. These are processed with appropriate sparse-sample uncertainty quantification (UQ) methods to estimate various statistics of response with an appropriate level of conservatism. This is tested on 16 output quantities (von Mises stresses and equivalent plastic strains) and it is shown that important statistics of the true variabilities of the 16 quantities are bounded with a high success rate that is reasonably predictable and controllable. The DD approach has several advantages over other calibration-UQ approaches like Bayesian inference for capturing and utilizing the information obtained from typically small numbers of replicate experiments in model calibration situations—especially when sparse replicate functional data are involved like force–displacement curves from material tests. The DD methodology is straightforward and efficient for calibration and propagation problems involving aleatory and epistemic uncertainties in calibration experiments, models, and procedures.
publisherThe American Society of Mechanical Engineers (ASME)
titleDiscrete-Direct Model Calibration and Uncertainty Propagation Method Confirmed on Multi-Parameter Plasticity Model Calibrated to Sparse Random Field Data
typeJournal Paper
journal volume7
journal issue2
journal titleASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
identifier doi10.1115/1.4050371
journal fristpage020912-1
journal lastpage020912-15
page15
treeASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2021:;volume( 007 ):;issue: 002
contenttypeFulltext


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