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Stochastic Virtual Tests for High-Temperature Ceramic Matrix Composites

Annual Review of Materials Research 2014 76 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 35 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Brian N. Cox, Brian N. Cox, Robert O. Ritchie, Brian N. Cox, Hrishikesh Bale, Hrishikesh Bale, Brian N. Cox, Matthew R. Begley, Brian N. Cox, Matthew Blacklock, Bao-Chan Do, Bao-Chan Do, Tony Fast, Tony Fast, M. Naderi, M. Naderi, Márk Novák, Varun P. Rajan, Robert O. Ritchie, Renaud G. Rinaldi, Robert O. Ritchie, Robert O. Ritchie, Michael Rossol, John H. Shaw, Olivier Sudre, Olivier Sudre, Qingda Yang, Frank W. Zok, David B. Marshall

Summary

This review covers the development of computational 'virtual tests' to predict how high-temperature ceramic composites fail under stress, combining advanced imaging with material simulations. This is a specialized aerospace materials engineering study with no direct connection to microplastics or environmental health.

We review the development of virtual tests for high-temperature ceramic matrix composites with textile reinforcement. Success hinges on understanding the relationship between the microstructure of continuous-fiber composites, including its stochastic variability, and the evolution of damage events leading to failure. The virtual tests combine advanced experiments and theories to address physical, mathematical, and engineering aspects of material definition and failure prediction. Key new experiments include surface image correlation methods and synchrotron-based, micrometer-resolution 3D imaging, both executed at temperatures exceeding 1,500°C. Computational methods include new probabilistic algorithms for generating stochastic virtual specimens, as well as a new augmented finite element method that deals efficiently with arbitrary systems of crack initiation, bifurcation, and coalescence in heterogeneous materials. Conceptual advances include the use of topology to characterize stochastic microstructures. We discuss the challenge of predicting the probability of an extreme failure event in a computationally tractable manner while retaining the necessary physical detail.

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