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- 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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- Creator:
- Ledva, Gregory S., Zhe, Du, Peterson, Sarah, Balzano, Laura, and Mathieu, Johanna L.
- Description:
- This is the code that resulted from NSF grant ECCS-1508943, "Inferring the behavior of distributed energy resources from incomplete measurements." The project focused on developing control, estimation, and modeling methods for residential demand response and electric distribution networks. The talks, papers, and poster in Deep Blue: http://hdl.handle.net/2027.42/149480
- Keyword:
- online learning, energy disaggregation, residential demand response, networked control, Kalman filter, and frequency regulation
- Citation to related publication:
- Ledva, Gregory S., Laura Balzano, and Johanna L. Mathieu. "Inferring the behavior of distributed energy resources with online learning." 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2015. https://doi.org/10.1109/ALLERTON.2015.7447003, Ledva, Gregory S., and Johanna L. Mathieu. "A linear approach to manage input delays while supplying frequency regulation using residential loads." 2017 American Control Conference (ACC). IEEE, 2017. https://doi.org/10.23919/ACC.2017.7963041, Ledva, Gregory S., Laura Balzano, and Johanna L. Mathieu. "Exploring Connections Between a Multiple Model Kalman Filter and Dynamic Fixed Share with Applications to Demand Response." 2018 IEEE Conference on Control Technology and Applications (CCTA). IEEE, 2018. https://doi.org/10.1109/CCTA.2018.8511493, Ledva, Gregory S., et al. "Disaggregating Load by Type from Distribution System Measurements in Real Time." Energy Markets and Responsive Grids. Springer, New York, NY, 2018. 413-437. https://doi.org/10.1007/978-1-4939-7822-9_17, Ledva, Gregory S., Sarah Peterson, and Johanna L. Mathieu. "Benchmarking of Aggregate Residential Load Models Used for Demand Response." 2018 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2018. https://doi.org/10.1109/PESGM.2018.8585847, Ledva, Gregory S., et al. "Managing communication delays and model error in demand response for frequency regulation." IEEE Transactions on Power Systems 33.2 (2018): 1299-1308. https://doi.org/10.1109/TPWRS.2017.2725834, Ledva, Gregory S., Laura Balzano, and Johanna L. Mathieu. "Real-time energy disaggregation of a distribution feeder's demand using online learning." IEEE Transactions on Power Systems 33.5 (2018): 4730-4740. https://doi.org/10.1109/TPWRS.2018.2800535, and Talks, papers, and poster in Deep Blue: http://hdl.handle.net/2027.42/149480
- Discipline:
- Engineering