A Bayesian framework for assessing the strength distribution of composite structures with random defects

Sandhu, A.; Reinarz, Anne; Dodwell, T.J.

Abstract

This paper presents a novel stochastic framework to quantify the knock down in strength from out-of-plane wrinkles at the coupon level. The key innovation is a Markov Chain Monte Carlo algorithm which rigorously derives the stochastic distribution of wrinkle defects directly informed from image data of defects. The approach significantly reduces uncertainty in the parameterization of stochastic numerical studies on the effects of defects. To demonstrate our methodology, we present an original stochastic study to determine the distribution of strength of corner bend samples with random out-of-plane wrinkle defects. The defects are parameterized by stochastic random fields defined using Karhunen-Loéve (KL) modes. The distribution of KL coefficients are inferred from misalignment data extracted from B-Scan data using a modified version of Multiple Field Image Analysis. The strength distribution is estimated by embedding wrinkles into high fidelity FE simulations using the high performance toolbox dune-composites from which we observe severe knockdowns to 74% of structural strength with a probability of 1/200. Supported by the literature our results highlight the strong correlation between maximum misalignment and knockdown in coupon strength. This observation allows us to define a surrogate model providing fast assessment of predicted strength informed from stochastic simulations utilizing both observed wrinkle data and high fidelity finite element models.

Citation

Sandhu, A., Reinarz, A., & Dodwell, T. (2018). A Bayesian framework for assessing the strength distribution of composite structures with random defects. Composite Structures, 205, 58-68. https://doi.org/10.1016/j.compstruct.2018.08.074

• Journal Article Type: Article
• Acceptance Date: Aug 27, 2018
• Online Publication Date: Oct 23, 2018
• Publication Date: 2018-12
• Deposit Date: Aug 20, 2020
• Journal: Composite Structures
• Print ISSN: 0263-8223
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 205
• Pages: 58-68
• DOI: https://doi.org/10.1016/j.compstruct.2018.08.074
• Public URL: https://durham-repository.worktribe.com/output/1258143