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Can-Crush Model and Simulations for Verifying Uncertainty Quantification Method for Sparse Stress-Strain Curve Data

[+] Author Affiliations
J. F. Dempsey, V. Romero, N. Breivik, G. Orient, B. Antoun, B. Schroeder, J. Lewis, J. Winokur

Sandia National Laboratories, Albuquerque, NM

Paper No. IMECE2016-65245, pp. V009T12A005; 10 pages
doi:10.1115/IMECE2016-65245
From:
  • ASME 2016 International Mechanical Engineering Congress and Exposition
  • Volume 9: Mechanics of Solids, Structures and Fluids; NDE, Diagnosis, and Prognosis
  • Phoenix, Arizona, USA, November 11–17, 2016
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5063-3
  • Copyright © 2016 by ASME

abstract

This work examines the variability of predicted responses when multiple stress-strain curves (reflecting variability from replicate material tests) are propagated through a transient dynamics finite element model of a ductile steel can being slowly crushed. An elastic-plastic constitutive model is employed in the large-deformation simulations. The present work assigns the same material to all the can parts: lids, walls, and weld. Time histories of 18 response quantities of interest (including displacements, stresses, strains, and calculated measures of material damage) at several locations on the can and various points in time are monitored in the simulations. Each response quantity’s behavior varies according to the particular stress-strain curves used for the materials in the model. We estimate response variability due to variability of the input material curves. When only a few stress-strain curves are available from material testing, response variance will usually be significantly underestimated. This is undesirable for many engineering purposes. This paper describes the can-crush model and simulations used to evaluate a simple classical statistical method, Tolerance Intervals (TIs), for effectively compensating for sparse stress-strain curve data in the can-crush problem. Using the simulation results presented here, the accuracy and reliability of the TI method are being evaluated on the highly nonlinear input-to-output response mappings and non-standard response distributions in the can-crush UQ problem.

Copyright © 2016 by ASME

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