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Adjoint-based Calibration of Plasticity Model Parameters from Digital Image Correlation Data.
Author(s) -
Daniel Seidl,
Brian Granzow
Publication year - 2018
Publication title -
osti oai (u.s. department of energy office of scientific and technical information)
Language(s) - English
Resource type - Reports
DOI - 10.2172/1474264
Subject(s) - digital image correlation , calibration , correlation , plasticity , image (mathematics) , computer science , mathematics , artificial intelligence , statistics , geometry , physics , optics , thermodynamics
Parameter estimation for mechanical models of plastic deformation utilized in nuclear weapons systems is a laborious process for both experimentalists and constitutive modelers and is critical to producing meaningful numerical predictions. In this work we derive an adjoint-based optimization approach for a stabilized, large-deformation J2 plasticity model that is considerably more computationally efficient but no less accurate than current state of the art methods. Unlike most approaches to model calibration, we drive the inversion procedure with full-field deformation data that can be experimentally measured through established digital image or volume correlation techniques. We present numerical results for two and three dimensional model problems and comment on various directions of future research.

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