Bayesian calibration of continuum damage model parameters for an oxide-oxide ceramic matrix composite using inhomogeneous experimental data. (December 2022)

• Record Type: Journal Article
• Authors: Generale, Adam P.; Hall, Richard B.; Brockman, Robert A.; Joseph, V. Roshan; Jefferson, George; Zawada, Larry; Pierce, Jennifer; Kalidindi, Surya R.
• Is Part Of: Mechanics of materials. Volume 175(2022)
• Publication Date: 2022-12
• Subjects: Ceramic Matrix Composites -- Bayesian Inference -- Markov Chain Monte Carlo -- Probabilistic Calibration -- Uncertainty Quantification; Strength of materials -- Periodicals; Mechanics, Applied -- Periodicals; Résistance des matériaux -- Périodiques; Mécanique appliquée -- Périodiques; Mechanics, Applied; Strength of materials; Periodicals; Electronic journals; 620.11
• DOI: 10.1016/j.mechmat.2022.104487
• Languages: English
• ISSNs: 0167-6636

Abstract

Abstract: 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. Specifically, 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 estimates, 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. Highlights: High-dimensional viscous multimode continuum damage model parameters identified through Bayesian inference. Mixed effects statistical model enables simultaneous calibration against inhomogeneous experimental dataset. Posterior distribution can guide informative additional experimental runs.