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Filtering by: Creator Nikolov, Denislav P Creator Srivastava, Siddhartha Language English, Matlab Resource type Dataset

Dataset for Ogden Material Calibration via Magnetic Resonance Cartography, Parameter Sensitivity, and Variational System Identification

Creator:

Nikolov, Denislav P, Srivastava, Siddhartha, Abeid, Bachir A, Scheven, Ulrich M, Arruda, Ellen M, Garikipati, Krishna, and Estrada, Jonathan B

Description:

Contemporary material characterisation techniques that leverage deformation fields and the weak form of the equilibrium equations face challenges in the numerical solution procedure of the inverse characterisation problem. As material models and descriptions differ, so too must the approaches for identifying parameters and their corresponding mechanisms. The widely-used Ogden material model can be comprised of a chosen number of terms of the same mathematical form, which presents challenges of parsimonious representation, interpretability, and stability. Robust techniques for system identification of any material model are important to assess and improve experimental design, in addition to their centrality to forward computations. Using fully 3D displacement fields acquired in silicone elastomers with our recently-developed magnetic resonance cartography (MR-u) technique on the order of ~20,000 points per sample, we leverage PDE-constrained optimisation as the basis of variational system identification of our material parameters. We incorporate the statistical F-test to maintain parsimony of representation. Using a new, local deformation decomposition locally into mixtures of biaxial and uniaxial tensile states, we evaluate experiments based on an analytical sensitivity metric, and discuss the implications for experimental design. This repository contains the acquired data and MRI processing code used in this work.

Keyword:

continuum mechanics, magnetic resonance, sensitivity, full-field deformations, physics inference, mechanics, mechanical engineering, and computational mechanics

Citation to related publication:

https://doi.org/10.48550/arXiv.2204.03122

Discipline:

Engineering

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