Journal
MECHANICS OF MATERIALS
Volume 175, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.mechmat.2022.104487
Keywords
Ceramic Matrix Composites; Bayesian Inference; Markov Chain Monte Carlo; Probabilistic Calibration; Uncertainty Quantification
Categories
Funding
- Department of Defense (DoD) High Performance Computing Modernization Program (HPCMP) 2020 HPC Internship Program (HIP)
- National Science Foundation [2027105]
- Air Force Research Laboratory (AFRL)
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [2027105] Funding Source: National Science Foundation
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This study demonstrates the successful application of Bayesian inference for simultaneous estimation of eleven material parameters of a viscous multimode CDM model, providing uncertainty estimates and principled decision making, applicable to mechanical models with high-dimensional parameter sets.
The calibration of continuum damage mechanics (CDM) models is often performed by least-squares regression through the design of specifically crafted experiments to identify a deterministic solution of model parameters minimizing the squared error between the model prediction and the corresponding experimental result. Spe-cifically, this work demonstrates a successful application of Bayesian inference for the simultaneous estimation of eleven material parameters of a viscous multimode CDM model conditioned upon a small inhomogeneous multiaxial experimental dataset. The stochastic treatment of CDM model parameters provides uncertainty esti-mates, enables the propagation of uncertainty into further analyses, and provides for principled decision making regarding informative subsequent experimental tests of value. The methodology presented in this work is also broadly applicable to various mechanical models with high-dimensional parameter sets.
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