About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration |
Author(s) |
Denielle Ricciardi, Tom Seidl, Brian Lester, Amanda Jones, Elizabeth Jones |
On-Site Speaker (Planned) |
Denielle Ricciardi |
Abstract Scope |
There has been a growing reliance on computational simulations to make important engineering decisions. The calibration of these complex material models is an essential step of material certification; however, existing calibration approaches can be data and time intensive, contributing to an elongated material discovery process.
This work reconsiders the calibration process with the proposed Interlaced Characterization and Calibration (ICC) framework for the calibration of phenomenological constitutive models for solid mechanics. Bayesian optimal experimental design is used to perform inference on model parameters and to determine the next best experiment to conduct to reduce parameter uncertainty. Dimension-reduced, full-field digital image correlation data is generated under heterogeneous loading conditions and is used to calibrate the model. The data collection process and inference are performed iteratively in a feedback loop to drive the calibration to an optimal state.
SNL is managed and operated by NTESS under DOE NNSA contract DE-NA000352. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, |