Physics-based Life Prediction Model Incorporating Environmental Effects for SiC/SiC Ceramic Matrix Composites

MDS, LLC in collaboration with Rolls-Royce, Hyper-Therm HTC, AFIT, University of Akron and Columbia University will develop a validated physics-based long-term deformation and life prediction multiscale-multiphysics design system (M2DS) for advanced ceramic matrix composites under aerospace gas turbine engine environmental conditions. Specifically we will develop deterministic and probabilistic coupled thermo-mechano-oxidation models of degradation, mechanistic thermal fatigue model and CMC optimization toolkit that will enable to find the best approaches for protecting against oxidation depending on the application conditions, such as environment, temperature, stresses and required component lives. M2DS will be validated for two material systems, Chemical Vapor Infiltrated CMC and Melt Infiltrated CMC GEN II, by utilizing AFIT/AFRL burner rig to simulate various combustion and mechanical loading conditions. BENEFIT: Currently, there are at least two CMC components planned for eventual introduction into the F136 engine. This process would be accelerated if there were validated structural assurance tools available to the design community. It would also have a huge cost benefit, not only from the improved performance, but also from the reduced development costs. The current approach to structural assurance is to build an extensive data base covering all potential failure modes, very expensive and time consuming, followed by building components and destructively testing them, costing additional resources. Validated life assurance models would remove much of this cost. It would provide the confidence necessary to accelerate the use of CMC components and to gain the experience that would promote their usage. In addition to the application to military craft, there are a significant number of static CMC components planned for civil application, such as advanced friction systems, in the relatively near future. The development of efficient computational tools for sensitivity analysis, error and uncertainty quantification, and the solution of model calibration inverse problems are critically important to design and decision under uncertainty and is therefore a fundamentally important goal across DOD Agencies and civil applications.